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S. No

Volume-7 Issue-4S, November 2018, ISSN: 2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors:

S.R.Saratha, Vijeta Iyer, R.Manju

Paper Title:

Subspace Homotopy Methods for Solving Nonlinear Equations

Abstract: The concept of Newton subspace homotopy method has been introduced in this paper. As Newton – Homotopy method can’t be used for all the functions in topological spaces. So an algorithm has been developed by finding a subspace of the given space for which the subspace homotopy function has been found.

Keywords: Homotopy, Newton-Raphson Method, Subspace Homotopy, Matlab 2010 AMS Subject Classification: 55P10, 55P35, 55P99.

References:

  1. Abbasbandy, S (2003). ‘Improving Newton-Raphson method for nonlinear equations by modified Adomian decomposition method’ Applied Mathematics and Computation, 145 (2-3).pp.887-893.
  2. Saratha S R, Sai Sundara Krishnan G, Vijeta Iyer (2017) ‘Homotopy on Subspace Topology’. International Journal of Pure and Applied Mathematics, Volume 116 No. 12 2017, 189-197.
  3. Nor Hanim Abd. Rahman,Arsmah Ibrahim, Mohd Idris Jayes (2011) ‘ Newton Homotopy Solution for Nonlinear Equations Using Maple14, Journal of Science and Technology | ISSN 2229-8460 | Vol. 3 No. 2 December 2011
  4. Palancz B, Awange J.L, Zaletnyik P and Lewis R.H (2010) ‘Linear homotopy solution of nonlinear systems of equations in geodasy’ Journal of Geodasy 84(1).pp.79-95.
  5. Borsuk, K. (1967). Theory of Retracts. Polish Scientific Publishers, Warsaw.
  6. Dugundji, J. ( 1966). Allynand Bacon, Boston.

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2.

Authors:

T. Thulasimani

Paper Title:

Application of Scheduling using Critical Path Method to Hydraulic Performance of Impeller for a Multistage Submersible Pump

Abstract: The present study is an application of scheduling using critical path method without any restriction in resource.The study describes a full evaluation on hydraulic performance of impeller for a multistage submersible pump. Based on the planning project duration for completion wasfifty two weeks. However, the application of critical path to the study resulted tothirty nine weeks, a difference of 13 weeks reduction.

Keywords: Critical path analysis, scheduling, hydraulic performance of impeller for a multistage submersible pump.

References:

  1. Christopher M (2000),” The agile supply chain: competing in volatile markets”.Industrial Marketing Management 29: 37-44.6
  2. Kielmas M (2015),” History of the Critical Path Method”. Small BusinessChronCom Demand Media.1
  3. Gray CF, Larson EW., ”Project management: The managerial process”. Asia: McGraw-Hill; 2010.
  4. Bricknell L., “Project planning: Part I. In: Oossthuizen T, Venter R, eds. Project management in perspective”. South Africa: Oxford University Press; 2011.
  5. Hadju M., “Network scheduling techniques for construction project management”. Netherlands: Netherlands Kluwer Academic Publisher; 1997.

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3.

Authors:

N. Rajathi, N. Suganthi, Shilpa R.

Paper Title:

Automatic Electricity Bill Generating System

Abstract: The traditional way of reading energy meter is an expensive work where the reader of the meter has to go in person to each meter and take the meter reading manually. This manual reading goes into into the billing software to generate the bill to automate the payment process. This method of reading has short comings, such as reading error and involves more labors. To overcome this issue, an automatic power meter reading and billing system is proposed. Automation of energy meter reading and billing data entry process would reduce the laborious task and financial wastage. The proposed work measures the energy consumption in each house and generates the bill automatically with Arduino and Wi-Fi. The main goal of this work is to reduce the energy consumption in houses by notifing the owner continuously about the amount of units that are consumed. The goal of this work is to automate the billing process by checking the electricity unit’s consumption in a house and hence subsequently reduces the manual labor. The calculations are carried out automatically and the bill is updated on the Internet by the help of Wi-Fi. The bill amount can be checked by the owner anywhere and at any time by visiting the website or the online portal.

Keywords: Arduino, Electric meter, Wi-Fi module, Billing

References:

  1. k , Sudhish N George. “GSM Based Automatic Energy Meter Reading System with Instant Billing” 978-1-4673-5090-7©2013 IEEE
  2. Liting Cao, Jingwen Tian and Dahang Zhang “Networked Remote Meter-Reading System Based on Wireless Communication Technology” IEEE International Conference on Information Acquisition, August 20 – 3, 2006, Weihai, Shandong, China.
  3. Vinu V Das, “Wireless Communication System for Energy Meter Reading” International Conference on Advances in Recent Technologies in Communication and Computing 2009.
  4. T. Chandler, “The technology development of automatic metering and monitoring systems,” in IEEE International Power Eng. Conf., Dec. 2005.
  5. Smart meter Implementation Strategy Prospectus. July 2010. DECC, Of gem/Ofgem E-Serve.
  6. Faisal and A. Mohamed, “A new technique for power quality based condition monitoring,” in 17th Conf. Electrical Power Supply Industry, Oct. 2008.
  7. Wasi-ur-Rahman, Mohammad Tanvir Rahman, Tareq Hasan Khan and S.M. Lutful Kabira, “Design of an Intelligent SMS based Remote Metering System”, Proceedings of the 2009 IEEE International Conference on Information and Automation June 22 -25, 2009, Zhuhai/Macau, China.
  8. Aryo Handoko Primicanta, Mohd Yunus Nayan, Mohammad Awan,”Hybrid System Automatic Meter Reading”, Computer Technology and Development, 2009. ICCTD'09.
  9. Arun,Dr, Sidappa Naidu ,“Hybrid Automatic Meter Reading System”,in July 2012 International Journal of Advanced Research in Computer Science and Software Engineering.
  10. Sapna Ganurkar, Pravesh Gour.”Prepaid Energy Meter for Billing System Using Microcontroller and Recharge Card”, International Journal Of Core Engineering & Management (IJCEM) Volume 1, Issue 1, April 2014.
  11. Tarek Khalifa, Kshirasagar Naik and Amiya Nayak “A Survey of Communication Protocols for Automatic Meter Reading Applications” in IEEE Communications Surveys & Tutorials, vol. 13, no. 2, second quarter 2011.
  12. Suganthi N, Arun R, Saranya D and Vignesh N, “Smart Security Surveillance Rover”,International Journal of Pure and Applied Mathematics, Vol. 116, No.12, 2017, 67-75.
  13. Vanitha, V, Sumathi,VP,Cynthia,J and Illakia,B “Next Generation Vehicle Diagnostic Systems”, International Journal of Pure and Applied Mathematics Volume 116 No. 11 2017, 251-259.

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4.

Authors:

Sumathi VP, Vanitha V, Divyadarshini M.

Paper Title:

GDP based Medal Count Analysis in Summer Olympics Games for Two Decades - An Exploratory Analysis

Abstract: The Olympics games started way long back with many participants and winners from all over the world. The game involved in many disciplines and made a bigger impact on the participants and the audience as well. A big data boom is on the horizon, so it's more important than ever to take control of this data. Instinctively this analysis recognise that to perform better than the competitors, this need accurate evidence and data to base the decisions on. The game had its debut in the year 1896 and the progress till now is recorded with the athlete's respective years,disciplines,total medal counts. The goal of this thesis included improved understanding of the competing countries and to develop the players’ skills more efficiently for both the extremes (First 10 and Last 10 countries). The analysis is taken by the data of last 5 summer Olympics Games using statistical methods such as correlation factor. Performance analysis is based on the correlation factor with respect to country’s GDP (Gross Domestic Product), total medal counts and gold medal counts. This analysis results in an outcome for both extremes meant to amplify the information, which can make the users get higher knowledge about their competitors and country to proceed. There are attributes(year,GDP in million) taken from the dataset and derived attributes(country wise total medal count and country wise men and women athletes count and distinct medal counts for men and women) obtained and analysed to give the knowledge of both extreme countries’ (First 10 and Last 10 countries) performance in each year. Finally, the analysed data is plotted in graphs, which can help to find the successes as well asdisappointments.

Keywords: exploratory data analysis; olympics analytics ; performance; medal count analysis; gross domestic product (GDP); analysis;competitors skillsets; disciplines; regular expository; statistical methods; sample variance; graphs;

References:

  1. Lozano S et al., “ Measuring the performance of nations at the Summer Olympics using data envelopment analysis,” Journal of Operational Research Society. vol. 53, May
  2. Yang Yu, Xiao Wang. World Cup 2014 in the Twitter World: “A big data analysis of sentiments in U.S. sport fans’ tweet.Computers in Human Behavior”,vol. 48, July 2015.
  3. Yongjun Li et al., “ Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis”,.International Journal of Operational Research. December 2014.
  4. Jayantha K, Ubayachandra E G.Going for Gold Medals:” Factors affecting Olympic Performance.International Journal of Scientific and Research Publications”,vol.5.6,June 2015.
  5. Akalank Jayakumar ,Title “Is gdp of country and medal count are related”., In blog.September 2016.
  6. Hartley Brody, Title:”Global Dominance: Olympics Vs GDP”.,In blog.August 2106.
  7. Sumathi, VP, Kousalya, K, Vanitha, V, Cynthia, J, (2018), ‘ Crowd estimation at a social event using call data records’, Int. J. Business Information Systems, Vol 28, No. 2, pp 446-461.

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5.

Authors:

D. Arun Kumar, J. Merline Shyla, Francis P. Xavier

Paper Title:

Synthesis and Optical, Photoconductivity Study of Safranin O Dye Sensitized Titania/Silica oxide system Prepared by Modified Sol-gel Method

Abstract: Binary TiO2/SiO2 oxides were prepared by modified sol-gel method and calcined at 500oC for 5 hours. The TiO2/SiO2nanocomposites was sensitized with different concentrations of Safranin O dye solutions ranging from 1x10-2 mM to 5x10-2mM. The amount of dye adsorbed, amount of dye absorbed per unit mass and percentage of dye adsorbed increases with increase of concentration of dye. The Safranine O dye-sensitized TiO2/SiO2nanocomposites exhibit an intense peak at 520 nm due to n→π* transition enhances the spectral response of TiO2/SiO2nanocomposites to visible light. The field dependent dark and photoconductivity studies reveals that the dark and photocurrent are increase linearly with applied field. From the photoconductivity results, it was observed from the plots that for the highest concentration of dye sensitized (5x10-5 M), the photo current density increases by an order of 75 in comparison with that of TiO2/SiO2nanocomposites.

Keywords: TiO2-SiO2, Oxides, Composites, Mesoporous, Photoconductivity, Semiconductors, Dye Sensitization.

References:

  1. Hagfeldt A, Gratzel, M Molecularphotovoltaics, Acc. Chem. Res. 33 (2000) 269-277.
  2. Klein S, Thorimbert S, W.F. Maier, Amorphous MicroporousTitania–Silica Mixed Oxides: Preparation, Characterization, and Catalytic Redox Properties, Catal. 163 (1996) 476-488.
  3. Mariscal, M. López-Granados, J.L.G. Fierro, J.L. Sotelo, C. Martos, R. van Grieken Morphology and Surface Properties of Titania−Silica Hydrophobic Xerogels, Langmuir 16 (2000) 9460-9467.
  4. Fraile J. M, García J. I, Mayoral J. A, Vispe E, Catalytic sites in silica-supported titanium catalysts: silsesquioxane complexes as models, Catal. 233 (2005) 90-99.
  5. Martin A. Green, Keith Emery, Yoshihiro Hishikawa, Wilhelm Warta Solar cell efficiency tables, photovolt. res. Appl. 19 (2011) 84-92.
  6. Arun Kumar D, Francis P. Xavier, MerlineShyla J, Natural dye sensitization of TiO2 thin films using Lawsone dye extracted from LawsoniaInermis for solar cell applications, Archives of Applied Science Research 4 5 (2012) 2122-2132.
  7. PlinioInnocenzi, Alessandro Martucci, Massimo Guglielmi, Andrea Bearzotti, Enrico TraversaElectrical and structural characterization of mesoporous silica thin films as humidity sensors 76 1-3 (2001) 299-303.
  8. Aguado J, Grieken R V, Munoz M.J.L, Marugan J, Comprehensive study of the synthesis characterization and activity of TiO2 and mixed TiO2/SiO2 photo catalyst. Applied Catalysis A: General. 312 (2006) 202-212
  9. Anders Hagfeldt, BengtDidriksson, Tommy Palmqvist, Henrik Lindstrom, Sven Sodergren, HåkanRensmo, Sten-Eric Lindquist, Verification of high efficiencies for the Gratzel-cell. A 7% efficient solar cell based on dye-sensitized colloidal TiO2 films, Solar Energy Materials and Solar Cells 31 4 (1994) 481-488.
  10. Ponniah D, Xavier F, Electrical and electroreflectance studies on ortho-chloranil-doped polyanalinePhysica B 392 1-2 (2007) 20-28
  11. Arun Kumar K, Prakash SM, Low cost removal of Basic dye from aqueous solution using silk cotton hull, Journal of Environmental Research and Development 3 (2009) 728-734.
  12. Subba Reddy Y, Jeseentharani V, Jayakumar C, Nagaraja KS, Jeyaraj B Adsorptive removal of malachite green (oxalate) by low cost adsorbant, Journal of Environmental Research and Development 7 (2012) 275-284.
  13. Zhou Lijun, Shanshan Yan, BaozhuTian, Jinlong Zhang, Masakazu Anpo, Preparation of TiO2–SiO2 film with high photocatalytic activity on PET substrate, Materials Letters 60 (2006) 396–399.
  14. KyeongYoul Jung, Seung Bin Park, Photoactivity of SiO2/TiO2 and ZrO2/TiO2 mixed oxides prepared by sol-gel method, Materials letter 58 (2004) 2897-2900.
  15. Li-Lan Yang, Yi-Sheng Lai, Chen JS, Compositional tailored sol-gel SiO2-TiO2 thin films: crystallization, chemical bonding configuration and optical properties, Journal of Materials Research 20 (2005) 3141-3149.
  16. Arun Kumar D, MerlineShyla J, Francis P. Xavier, Synthesis and characterization of TiO2/SiO2nanocomposites for solar cell applications, Applied nanosciences 2 (2012) 429-436.
  17. Yin Zhao, Chunzhong Li, Xiuhong Liu, FengGu, Haibo Jiang, Wei Shao, Ling Zhang, Ying He Synthesis and optical properties of TiO2 nanoparticles, Materials Letter 61 (2007) 79-83.
  18. J Mohan, Organic Spectroscopy Principles and Applications, Narosha Publishing House Pvt. Ltd, 2nd New Delhi, 2009, pp 28-95.
  19. Aziz RadhiyanAbd, SopyanIis, Synthesis of TiO2-SiO2 powder and thin film photocatalysts of sol-gel method, International Journal of Chemistry 48 (2009) 951-957.
  20. XiaoyiShen, YuchunZhai, Yang Sun, HuiminGu, Preparation of monodisperse spherical SiO2 by microwave hydro-thermal method and kinetics of dehydrated hydroxyl, J Mater Sci Technol. 26 (2010) 711-714.
  21. MerlineShyla J, Electro-optical characterization of titanium dioxide-organic dye composites and fabrication of dye-sensitized solar cell using sol-gel coated TiO2 electrodes, Ph.D thesis, University of Madras, Chennai, 2005, 196.
  22. Marie-Isabella Baraton, Nano-TiO2 for solar cells and photocatalytic water splitting: scientific and technological challenges for commercialization, The open Nanoscience Journal 5 (2011) 64-77.
  23. Brian O'regan, Michael Grätzel, A low-cost, high-efficiency solar cell based on dye-sensitized colloidal TiO2films,Nature 353 (1991) 737 – 740.
  24. Dhar S, Chakrabarti S Electroless, Ni plating on n and p-type porous silicon Si for ohmic and rectifying contacts, Semicond. Sci. Technol. 11 (1996) 1231-1234.
  25. Arun Kumar D, Francis P. Xavier, MerlineShyla J, Investigations on the variation of conductivity and photoconductivity of CuO thin films as a function of layers of coating, Archives of Applied Science Research 4 5 (2012) 2174-2183.
  26. Simon J, Andre JJ Molecular Semiconductors: Photoelectrical properties and solar cells, Springer-Verlag, Germany, 1985, pp. 6.
  27. Ponniah JD, Electrical conductivity and Spectral investigations of pure and doped polyanaline complexes, Ph.D thesis, University of Madras, Chennai, 2005, pp. 27.
  28. Xavier FP, Optical and Transport properties of Phthalocyanine and related compounds, UMI, Ann Arbor, 1993, pp. 48.
  29. Arun Kumar D, Alex Xavier J, MerlineShyla J, Francis P. Xavier, Synthesis and structural, optical and electrical properties of TiO2/SiO2nanocomposites, Journal of Materials Science 48 10 (2013) 3700-3707.

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6.

Authors:

Deeparani M, Kalamani M, Krishnamoorthi M.

Paper Title:

A Survey on Ultrasound Image Segmentation Algorithm for Detection of Female Pelvic Masses

Abstract: Imaging is a very precious tool for diagnostic purpose and various modalities provides an excellent way for viewing the anatomy of the organs. These various imaging modalities are used for differentiating normal and diseased anatomy. For providing support to these imaging modalities computers are used for processing and analysis. This research paper provides a review of recent image segmentation algorithms for medical images. From the recent survey, the various methods and applications of medical image segmentation are discussed. The narrative of this paper is focused on different image segmentation algorithms used for computer aided diagnosis of female pelvic masses for ultrasound.

Keywords: Female Pelvic Masses, Ultrasound Images, Segmentation Algorithms, Clustering

References:

  1. Faria S.C, “Evaluation of the Patient with a Pelvic Mass”, Indian J RadiolImag , 25 (2):137–47, 2015.
  2. LidiyaThampi, Varghese Paul, “Abnormality recognition and feature extraction in female pelvic ultrasound imaging,” Informatics in Medicine, 23 February 2018.
  3. Alcazar JL, “ The role of ultrasound in the assessment of uterine cervical cancer”, J ObstetGynaecol India 64(5):311–6, 2014.
  4. Sonia H. Contreras Ortiza, TsuichengChiua, Martin D. Foxa, “Ultrasound image enhancement: A review” Biomedical Signal Processing and Control, 7, 419– 428, 2012.
  5. Zimmer and S. Akselrod, “Image segmentation in obstetrics and gynecology,” Ultrasound Med. Biol, vol. 26, no. 1, pp. S39–S40, 2000.
  6. Dilna K T and D.JudeHemanth, “Detection of Uterus Fibroids in Ultrasound Images: a survey” International Journal of Pure and Applied Mathematics. ISSN: 1311-8080, Volume 118 No. 16, 139-159, 2018.
  7. Muzzolini, Y. H. Yang, and R. Pierson, “Multiresolution texture segmentation with application to diagnostic ultrasound images,” IEEE Trans. Med. Imag., vol. 12, no. 1, pp. 108–123, 1993.
  8. Muzzolini, Y. H. Yang, and R. Pierson, “Texture characterization using robust statistics,” Pattern Recognition., vol. 27, no. 1, pp. 119–134, 1994.
  9. Shivakumar K. HarlapurRavindra S. Hegadi, “Segmentation and Analysis of Fibroid from Ultrasound Images” , International Journal of Computer Applications, 2015.

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7.

Authors:

P. Kannappan, R. Dhanasekaran

Paper Title:

Structural and Optical Characterization of ZnS Nanoparticles Synthesized by Low Temperature Solid-State Method

Abstract: The ZnS nanoparticles were synthesized by solid state reaction method. The synthesized sample was characterized by powder XRD, SEM, EDAX, UV-visible, fluorescence and FT Raman analysis. The powder XRD analysis shows the broad (111), (220) and (311) peaks which confirms the formation of cubic structure. The SEM image shows the agglomerated spherical shape of morphology. The EDAX analysis shows the composition of Zn and S are 46.47% and 53.53% respectively. The UV-Visible spectrum analysis shows the absorption wavelength as 325 nm and calculated band gap 3.81 eV. The fluorescence study reveals the near band edge emission at 363 nm and defect level peaks were observed in the higher wavelength of the photoluminescence spectrum. FT Raman analysis shows the vibration frequency at 234 cm-1 is due to the longitudinal optical (LO) mode of Zn-S lattice.

Keywords: ZnS, XRD, SEM, EDAX, Optical Properties

References:

  1. NavneetKaur, Sukhmeen Kaur, Jagpreet Singh and Mohit Rawat, J Bioelectron Nanotechnol 1(2016) 1-5
  2. Khamala, L. Franklin, Y. Malozovsky, A. Stewart, H. Saleem, D. Bagayoko, Computational Condensed Matter 6 (2016) 18-23
  3. Ashish Tiwari, S. J. Dhoble, RSC Advances, 6(2016) 64400-64420
  4. Houcine Labiadh, Karima Lahbib, Slah Hidouri, Soufiane Touil, Tahar BEN Chaabane Asian Pacific Journal of Tropical Medicine 9 (2016) 757- 762
  5. Xianfu Wang, Hongtao Huang, Bo Liang, Zhe Liu, Di Chen, Guozhen Shen, Critical Reviews in Solid State and Materials Sciences, 38 (2013) 57-90.
  6. Li Dao-hua, He Shao-fen, Chen Jie, Jiang Cheng-yan, Yang Cheng, IOP Series: Materials Science and Engineering 242 (2017) 012023
  7. Guinier, A. X-ray Diffraction, Freeman, San Francisco 1963.
  8. Edgar Mosquera, Nicolas Carvajal Materials Lett.129 (2014) 8-11
  9. Yingyot Infahsaeng Sarute Ummartyotin, Results in Phys., 7(2017) 1245-1251
  10. Dimitrievska, H. Xie, A. J. Jackson, X. Fontane, M. Espindola-Rodriguez, E. Saucedo, Perez-Rodriguez, A. Walsh, V.Izquierdo-Roca, Phys.Chem. Chem.Phys. 18(2016)7632
  11. A. Vinogradov, B. N. Mavrin, N. N. Novikova, V. A. Yakovlev, and D. M. Popova, Laser Phys. 19(2009)162-170.
  12. C. Cheng, C. Q. Jin, F. Gao, X.L. Wu, W. Zhong, S.H. Li, Paul K, Chu, J. Appl. Phys. 106(2009)123505-5

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8.

Authors:

A. Mahudeswaran, J. Vivekanandan, P. S. Vijayanand, J. Chandrasekaran

Paper Title:

Investigations on New Nanostructuredpoly (M-Toluidine-Co-3-Aminobenzoic Acid) Copolymer in Presence of DBSA Surfactantmoleclule by Insitu-Polymerisation

Abstract: A novel nanostructured poly (m-toluidine-co-3-aminobenzoic acid) copolymer has been prepared using dodecylbenzenesulphonic acid as surfactant and ammonium persulphate as oxidant at different monomer concentrations. The synthesized copolymer was soluble in solventslike DMSO, NMP, DMF and THF. The copolymers were subjected various analytical characterization methods such as electronic spectroscopy, FTIR spectroscopy, X-ray diffraction method, scanning electron microscopy and electrical conductivity. The UV-visible spectra reveals the π – π* and n-π* transitions at 313 nm and 517 nm respectively. FTIR absorption bands confirm benzenoid and quinoid rings in the copolymer chain. The X-ray diffraction study reveals that the copolymer is amorphous in nature. The DC electroactive nature of the copolymer is found to be 10-9 to 10-10 S/cm. The synthesized copolymer will show a change in resistance when exposed to the humidity and ammonia vapor sensors.

Keywords: Conjugated Polymer, Copolymer, Surfactant, Sensors

References:

  1. Lange, U, Roznyatovskaya, NV &Mirsky, VM 2008, ‘ Conducting polymers in chemical sensors and arrays’, AnalyticaChimicaActa, vol. 614, no.1, pp. 1-26.
  2. Qiu, Y, Duan, L & Wang, L 2002, ‘Flexible organic light-emitting diodes with poly-3,4-ethylenedioxythiophene as transparent anode’, Chinese Science Bulleting, vol. 47, no.23, pp. 1979-1982.
  3. Bejbouji, H, Vignau, L, Miane, JL, Dang, MT, Oualim, EM, Harmouchi, M &Mouhsen, A 2010, ‘Polyaniline as a hole injection layer on organic photovoltaic cells’, Solar Energy Materials and Solar Cells, vol. 94, no. 2, pp. 176-181.
  4. Zhang, J, Sun, B, Ahn, HJ, Wang, C & Wang, G 2013, ‘Conducting polymer-doped polypyrrole as an effective cathode catalyst for LiO2 batteries’, Materials Research Bulletin, vol. 48, no. 12, pp. 4979 – 4983.
  5. Diniz, FB, Andrade, GF, Martins, CR &Azevedo WM 2013, ‘A comparative study of epoxy and polyurethane based coatings containing polyaniline-DBSA pigments for corrosion protection on mild steel’, Progress in Organic Coatings, vol. 76, no.5, pp. 912-916.
  6. Panigrahi, R &Srivastava, SK 2015, ‘Tollen’s reagent assisted synthesis of hollow polyaniline microsphere/Ag nanocomposites and its applications in sugar sensing and electromagnetic shielding’, Materials Research Bulletin, vol. 64, no.1 pp. 33-41.
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  26. Chiang, JC & MacDiarmid, AG 1986, ‘‘Polyaniline’: Protonic acid doping of the emeraldine form to the metallic regime’, Synthetic Metals, vol.13, no.1-3, pp. 193-205
  27. Biswas, S, Dutta, B & Bhattacharya, S 2014, ‘Consequence of silver nanoparticles embedment on the carrier mobility and space charge limited conduction in doped polyaniline’, Applied Surface Science, vol. 292, pp. 420-431.
  28. Zhang, L & Wan, M 2003, ‘Self-Assembly of polyaniline-from nanotubes to hollow microspheres’, Advanced Functional Materials, vol. 13, no.13, pp. 815-820.
  29. Haba, Y, Sega, E, Narkis, M, Titelman, GI &Siegmann, A 2000, ‘Polyaniline-DBSA/polymer blends prepared via aqueous dispersions’, Synthetic Metals, vol.110, no.3, pp. 189-193.

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9.

Authors:

Sasikumar. C, Sundaresan. R, Nagaraja. M

Paper Title:

Hydrogen Production and Power Generation from Ocean Waves

Abstract: Oceans get heated repetitive by the solar radiation from sun. Ocean covers almost 85% of the globe surface; ocean power has high potential for both electricity production and other byproducts. Solar energy contributes to a reduction of greenhouse gases when compared to energy generated from fossil fuels. Ocean energy source will be an apt solution for meeting global demands of electricity. The nonrenewable energy resources of petroleum and other co2 sources are increasing the pollution level and causing the environmental affects. in future decades fossil sources like oil and coal might be shortage and huge hike in capital cost of coal. This research paper focuses a method of generating power by utilizing the temperature variation between the ocean surface and colder deep waters. This paper further discusses the electrolysis process for converting the power produced in an ocean thermal energy conversion system into hydrogen, environmental effect and special conditions of these processes.

Keywords: Hydrogen; Energy from Ocean; Renewable.

References:

  1. http://peswiki.com/index.php/Directory:Sea_Solar_Power
  2. http://www.esru.strath.ac.uk/EandE/Web_sites/02- 03/ocean_thermal_energy/group%20project/exports/otecex.html
  3. Sun F, Ikegami Y, BaojuJia and Arima H, “Optimization design and Exergy analysis of organic rankine cycle in ocean thermal energy conversion”, Applied Ocean Research, 35, 38– 46, 2012.
  4. Nihous G C, “An estimate of Atlantic Ocean thermal energy conversion (OTEC) resources”, Ocean Engineering 342210–2221 2007.
  5. Soerensen H C and Weinstein A, “Ocean energy: position paper for IPCC”, In: Proc of IPCC scoping meeting on Ren. En.Sources, Lubeck, Germany; 2008.
  6. Scruggs J and Jacob P, “Harvesting ocean wave energy”, Science 323:1176; 2009.
  7. Cornett A M A, “Global wave energy resource assessment”, In: Proc of the eighteenth international offshore and polar eng. Conference, Canada: Vancouver; 2008.
  8. http://www.nelha.org/about/history.html
  9. Vega L A, “Ocean Thermal Energy Conversion Primer”, Marine Technology Society Journal, Vol. 6, 4, Winter 2002/2003, pp. 25-35.
  10. Masutani S M and Takahashi P K, “Ocean thermal energy conversion”, University of Hawaii at Manoa, Honolulu, HI, USA, 2001 Academic Press.
  11. Kazim A and Veziroglu T N, “Role of PEM fuel cells in diversifying electricity production in the United Arab”, Emirates,International Journal of Hydrogen Energy Vol.28, no. 3, 349–355, 2003.
  12. Penner S S, “Steps Toward the Hydrogen Economy”, Center for Energy Research, The Environmental Literacy Council, 2002.
  13. Avery W H, and Berl W G, “Solar energy from the tropical oceans”, International Journal of Hydrogen Energy, Vol. 24, No.4, 1999, pp. 295–298.
  14. http://www.colano-corp.com/otec_tech.htm
  15. Ikegami Y, Fukumiya K, Jitsuhara K O S and Uehar H, “Hydrogen Production Using OTEC”, Proceedings of The Twelfth (2002) International Offshore and Polar Engineering Conf, pp.626-630.
  16. Yamada N, Hoshi A and Ikegami Y, “Performance simulation of solar-boosted ocean thermal energy conversion plant”, Renewable Energy, Vol. 34, 2009, pp. 1752–1758.
  17. Kobayashi H, “Water from the Ocean with OTEC”, Forum on Desalination using Renewable Energy, 2002.
  18. http://www.esru.strath.ac.uk/EandE/Web_sites/02-03/ocean_thermal_energy/group %20project/exports/otecex.html
  19. Bechtel M and Netz E, “Ocean Thermal Energy Conversion”.
  20. Etemadi A, Emdadi A, Afshar O A and Emami Y, “Electricity Generation by the Ocean Thermal Energy”, Energy Procedia, ICSGCE 2011, Chengdu, China, 2011.

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10.

Authors:

M. Dinesh Kumar

Paper Title:

Automation of Sand Core Drilling Process in Casting Industries

Abstract: Casting industries use different types of materials for making materials of complex shapes. Sand core is one of the cost effective methods for such a process. In order to get a shape a sand core is used. Core is a device used in casting and molding processes to produce materials of different shapes. Sand core is a delicate material which is highly prone to damages when handled manually. The productivity is also less due to the damages of manual handling. This paper discusses about the sand core drilling process used in industries manually and automating those processes using rack and pinion method with relay logic control mechanism.

Keywords: Sand Core, Industries, Drilling Methods.

References:

  1. Aadnoy, BerntSigve. "Effects of reservoir depletion on borehole stability." Journal of Petroleum Science and Engineering1 (1991): 57-61..
  2. Thirugnanam, Praphul das and Lenin Rakesh, Department of Mechanical Engineering," Design and Fabrication of Rack and Pinion Lift" Middle-East Journal of Scientific Research 20 (6): 744-748, 2014 ,ISSN 1990-9233,© IDOSI Publications, 2014,DOI: 0.5829/idosi.mejsr.2014.20.06.11372
  3. Howard H. Gerrish, William E. Dugger Jr. and Richard M. Roberts Copyright: 2009 Creating Relay Logic Diagrams
  4. Aadnoy, BerntSigve, and EirikKaarstad. "History model for sand production during depletion." SPE EUROPEC/EAGE Annual Conference and Exhibition. Society of Petroleum Engineers, 2010.

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11.

Authors:

S. Sudhahar, D. Sharmila

Paper Title:

Stabilization of Linear/Nonlinear Autonomous Systems using Lyapunov Functions

Abstract: This paper investigates the Lyapunov function construction of the linear/nonlinear autonomous systems for stability. The control Lyapunov functions are used to stabilize the system without sacrificing the transient state performance and trade-off between stability and performance of the system because safety of operation is very important then the performances of the system. The linear quadratic optimal control problems are solved based on the control Lyapunov functions for the tracking and disturbance rejection of both SISO and MIMO systems. The effectiveness of the proposed control Lyapunov functions for the system stability and performances shown through the numerically simulated examples.

Keywords: Lyapunov functions, Linear/Non linear Autonomous System, LQR, Safety Margins.

References:

  1. Katsuhiko Ogata, “Modern Control Engineering” PHI Learning Private Limited, New Delhi, 2012.
  2. Gopal M, “Digital Control and State Variable Methods: Conventional and Intelligent Control Systems” Tata McGraw Hill Education Private Limited, New Delhi, 2009.
  3. Wim Michiels and Silviu-lulian Niculescu, “Stability and Stabilization of Time-Delay Systems an Eigen value-Based Approach” Society for Industrial and Applied Mathematics, Philadelphia, 2007.
  4. Qiang Yu and Baowei Wu, “Generalized Lyapunov function theorems and its applications in switched systems” Systems & Control Letters, Vol.77, pp.40-45, 2015.
  5. Mario Sassano and Alessandro Astolfi, “Dynamic Lyapunov functions” Automatica, Vol. 49, pp. 1058–1067, 2013.
  6. G. Schultz and J. E. Gibson, “The variable gradient method for generating Lyapunov functions” Transactions of the American Institute of Electrical Engineers, Part II: Applications and Industry, Vol. 81, Issue: 4, 1962.
  7. Muhammad Zakiyullah Romdlony and Babu Jayawardhana, “Stabilization with guaranteed safety using Control Lyapunov–Barrier Function” Automatica, Vol. 66, pp. 39–47, 2016.
  8. Eugenio Alcala, Vicenç Puig, Joseba Quevedo, Teresa Escobet, Ramon Comasolivas, “Autonomous vehicle control using a kinematic Lyapunov-based technique with LQR-LMI tuning” Control Engineering Practice, Vol.73, pp.1-12, 2012.
  9. Rohit Gupta, Uroš V. Kalabic, Anthony M. Bloch and Ilya V. Kolmanovsky, “Solution to the HJB equation for LQR-type problems on compact connected Lie groups” Automatica, Vol. 95, pp. 525-528, 2018.
  10. Guillaume Mercère, Régis Ouvrard, Marion Gilson and Hugues Garnier, “Subspace based methods for continuous-time model identification of MIMO systems from filtered sampled data” Proceedings of the European Control Conference 2007, Kos, Greece, July 2-5, 2007.
  11. MATLAB toolbox: user’s guide, Math works, Inc, 2009.
  12. Raaja Ganapathy Subramanian Jovitha Jerome, and Vinodh Kumar Elumalai, “Adaptive PSO For optimal LQR tracking control of 2 DoF laboratory helicopter” Applied Soft Computing, Vol. 41, pp.77-90, 2016.

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12.

Authors:

G. Sathish Kumar, K. Premalatha, N. Aravindhraj, M. Nivaashini, M. Karthiga

Paper Title:

Secured Cryptosystem using Blowfish and RSA Algorithm for The Data in Public Cloud

Abstract: Data Security makes the finest importance in the area of cloud computing. Cryptosystem will provide the greater security for the data in the cloud. Many encryption techniques are available for secured data storage with its own advantages and disadvantages. There is a problem of Key escrow and certificate revocation in the identity based encryption. Personality based encryption is free from security mediator. The certificate less encryption technique will overcome the key escrow issue and the certificate revocation issue. The task of key production is shared between the cloud and client in the certificateless encryption. In the proposed framework, the data holder encodes the data utilizing his/her secret key. Following that the information holder encode the secret key twice to frame a intermediate key. At that point he/she will send this encoded information and middle of the road keys to the cloud. The cloud will unscramble the middle of the road key in part and send the mostly decoded key and scrambled information to the planned beneficiary. The client will decode again the somewhat decoded information which is sent by the cloud and the client will get the required key for decoding with the goal that the client can decode it totally. The information holder can send similar information to numerous customers with least expense.

Keywords: Blowfish, Cryptography, Cloud, Security, Encryption.

References:

  1. Abdalla ., “Searchable encryption revisited: Consistency properties, relation to anonymousibe, and extensions”, Journal of . Cryrocraphy., voloume. 21, no. 3, pp. 350391, Marrch 2008.
  2. Boneh “Fine-grained control of security capabilities”, ACM Trans. Internet Technol., volume.4, no.1, 6082, February. 2004.
  3. Fujisaki et. Al., “Secure integration of asymmetric and symmetric encryption schemes”. In M. J. Wiener, editor, Proceeding . Annual International Cryptology Conference Santa Barbara, 1999, volume 1666, pp.537554. Springer,1999.
  4. https://www.cse.wustl. edu /~jain/cse567-06/ftp/encryption_perf/
  5. Rui Guo, Qiaoyan Wen, Huixian Shi, Zhengping Jin, and Hua Zhang, Certificateless Public Key Encryption Scheme with Hybrid Problems and Its Application to Internet of Things, in Mathematical Problems in Engineering, Volume 2014, Article ID 980274.
  6. Al-Riyami and K. Paterson, Certificateless public key cryp- tography, in Proc. ASIACRYPT 2003, C.-S. Laih, Ed. Berlin, Germany Springer, LNCS 2894, pp. 452473.
  7. Green and G. Ateniese. Identity-based proxy re- encryption.Internzational Journal of , Applied Cryptography and Network Security Applied Cryptography and Network Security 2007, volume 42
  8. S. M. Chow, C. Boyd, and J. M. G. Nieto, Security mediated certificateless cryptography, in Proc. 9th Int. Conf. Theory Practice PKC, New York, NY, USA, 2006, pp.508524.
  9. Nubila Jaleel, Chinju, Mediated certificateless cryptosystem for the security of data in public cloud in IJRET: International Journal of Research in Engineering and Technology, eISSN: 2319-1163.

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13.

Authors:

C. Premavathi, P. Thangaraj

Paper Title:

Efficient Hand-dorsa Vein Pattern Recognition using KNN classification with Completed histogram CB in TP Feature Descriptor

Abstract: Hand-dorsa Vein Recognition System identifies an individual using the human hand vein features. Image capturing, extracting the features, keeping the features in a descriptor and making classification are important methods in hand-dorsa vein Recognition. In this paper, the feature descriptor and classification method is proposed for an efficient recognition system. A completed CB in TP has been proposed to represent selected features from Hand vein image system. K-nearest classification method with various proximity measure calculations is analysed to make an efficient classification system. A new minimum distance classification is proposed with dataset and the results are checked for accuracy and reliability. The proposed technique is calculated on a NCUT Dataset contains 2040 imagesfrom Prof. Yiding Wang, North China University of technology (NCUT) (Wang et al, 2010). Proximityprocess as Chi-square, City block, Euclidean, Chebychevalong with Murkowski are calculated and compared used for the better performance. The new resultsproveto facilitate the future feature descriptor achieved excellent performance for classification system.

Keywords: Feature Descriptor, K-Nearest Image Classification.

References:

  1. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996. View at PublisherView at Google ScholarView at Scopus
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  3. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107–1118, 2009. View at PublisherView at Google ScholarView at Scopus
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  6. Zhang, X. Huang, S. Z. Li, Y. Wang, and X. Wu, “Boosting local binary pattern (LBP) based face recognition,” in Proc. Advances in Biometric Person Authentication, ser. Lecture
  7. Shan, S. Gong, and P. W. McGowan, “Robust facial expression recognition using local binary patterns,” in Proc. IEEE International Conference on Image Processing, 2005, pp.

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14.

Authors:

K. Sathis Kumar, K. Paramasivam

Paper Title:

Survey on Particle Swarm Optimization Techniques in Network-on-Chip

Abstract: Network-on-Chip (NoC) an interconnection framework is proposed by Numerous number of Intellectual Property cores in the nature of System-on-Chip(SoC). Communication challenges in a global nature with respect to nanoscale technology is provided by NoC. A configuration of NoC with its least average traffic in communication, consumption of power and area covered in chip is the needed in real time applications. Effective routing, mapping the cache hierarchy, memory and application mapping are the main parameter to increase the efficiency in aNoC. This can be done with optimization techniques. With optimization technique, NoC can be configured such that the latency, consumption of power, and chip area engaged in aNoC are made to be minimal. This paper provides a survey of Particle Swarm Optimization (PSO) algorithm techniques to optimize NoC routing, Mapping of memory and application mapping to provide performance improvement.

Keywords: Network-on-chip (NoC); Cache Hierarchy; algorithms in routing; Particle Swarm Optimization (PSO); Quality of Service (QoS), NoC design, Chip Multiprocessor

References:

  1. XuChuan-pei, Yan xiao-feng and Chen Yu-qian, “A Technique for NoC Routing Based on Hybrid Particle Swarm Optimization algorithm”, Third International Conference on Genetic and Evolutionary Computing, 2009.
  2. Wang Lei, Ling Xiang, “Energy- and Latency-Aware NoC Mapping Based on Chaos Discrete ParticleSwarm Optimization”, International Conference on Communications and Mobile Computing, 2010.
  3. Leonid Yavits, Amir Morad, Ran Ginosar, “Cache Hierarchy Optimization”, IEEE Computer Architecture Letters, Vol. 13, No.2, 2014.
  4. Pradip Kumar Sahu, PuttaVenkatesh, SunilrajuGollapalli, “Application Mapping onto Mesh Structured Network-on-Chip using Particle Swarm Optimization”, IEEE Computer Society Annual Symposium on VLSI, 2011
  5. Hu J, Marculescu R. “Energy-aware mapping for tile-basedNoC architectures under performance constraints”, Proceedings of the 2003 Conference on Asia South PacificDesignAutomation , Kitakyushu , 2003, 233 – 239.
  6. Lei T, Kumar S, “A two-step genetic algorithm for mappingtask graphs to a network on chip architecture”, Proceedings of the Euro micro Symposium on DigitalSystem Design, Belek-Antalya, 2003, 180 – 187.
  7. Ascia G, Catania V, Palesi M, “An evolutionary approach to network-on-chip mapping problem”, Proceedings of the2005 IEEE Congress on Evolutionary Computation,Edinburgh, 2005, 112 – 119.
  8. Murali S, De Micheli G, “Bandwidth-constrained mapping ofcores onto NoC architectures”, Proceedings of the Design,Automation and Test in Europe Conference and Exhibition, Paris, 2004, 896 – 901.
  9. Benini, G, .De Micheli, “Networks on chips: a newSoCparadigm,”IEEE Computer, vol. 35, 2002. pp. 70-78.
  10. William J. Dally, Brian Towles, “Route packets, notwires: on-chip inteconnectionnetworks”,Proceedings of the38th annual Design Automation Conference, ACM, NewYork, NY, USA, 2001, pp. 684-689.
  11. Tang Lei, Shashi Kumar, “A two-step genetic algorithmfor mapping task graphs to a network on chip architecture”, Proceedings of the Euromicro Symposium on Digital SystemsDesign, IEEE Computer Society, Washington, DC, USA,2003, pp. 180-187.
  12. Alameldeen, “Using compression to improve chip multiprocessorperformance”, PhD thesis, University of Wisconsin, Madison, WI, 2006.
  13. Cassidy and A. Andreou, “Beyond Amdahl Law -An objectivefunction that links performance gains to delay and energy”, IEEETransactions on Computers, vol. 61, no. 8, pp. 1110-1126, Aug 2012.
  14. Krishna, A. Samih, and Y. Solihin. "Data sharing in multi-threadedapplications and its impact on chip design", ISPASS, 2012.
  15. Morad, T. Morad, L. Yavits, R. Ginosar, U. C. Weiser. "GeneralizedMultiAmdahl: Optimization of Heterogeneous Multi-Accelerator SoC,"IEEE Computer Architecture Letters, 2012.
  16. Benini, “Application Specific NoC Design,” Proceedings ofIEEE Design, Automation and Test in Europe Conference, 2006 vol. 1, pp. 1–5.
  17. Pande, C. Grecu, M. Jones, A. Ivanov and R. Saleh,“Performance Evaluation and Design Trade-offs for MP-SOC Interconnect Architectures,” IEEE Transactions on Computers, Vol.54, No. 8, pp.1025–1040, 2005
  18. Koziris et al.,”An Efficient Algorithm for the Physical Mappingof Clustered TaskGraphsontoMultiprocessorArchitectures,”Proceedingsof 8thEuroPDP, pp. 406-413, 2000.

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15.

Authors:

Arunkumar M. S, Suresh P, Gunavathi C, Preethi S.

Paper Title:

Periodicity Mining, “a Time Inference over High Utility Item set Mining” – A study

Abstract: In the past few years, mining of temporal frequent patterns from transactional database has gathered momentum. Numerous works and algorithms have been proposed for FIM [1,2,3,4], but the same models cannot be implemented to mine temporal patterns as none of the models are built to find patterns that consider periodicity of its occurrence in a database. The importance of an itemset really rests upon its utility rather than its participation count. Works over utility mining [5, 6 and 7] have gathered more momentum in this last decade and many research works have been carried out. In this paper, a survey is conducted on i) the works that led to periodic pattern mining, ii) the works over periodic pattern mining and iii) the extended and enhanced works of Periodic pattern mining.

Keywords: Temporal Mining, Utility Mining, Periodic Mining, High Utility Itemset mining, Sequential Pattern Mining.

References:

  1. Aggarwal, J. Han, “Frequent pattern mining”, Springer, 2014.
  2. Jian Pei, Jiawei Han, Hongjun Lu, ShojiroNishio, Shiwei Tang Dongqing Yang (2007), “H-Mine: Fast and space-preserving frequent pattern mining in large databases”, IIE Transactions, 39:6, 593-605.
  3. RakeshAgrawal , Tomasz Imieliński , Arun Swami, “Mining association rules between sets of items in large databases”, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., USA.
  4. Minato S., Uno T., Arimura H. (2008), “LCM over ZBDDs: Fast Generation of Very Large-Scale Frequent Itemsets Using a Compact Graph-Based Representation”. In: Washio T., Suzuki E., Ting K.M., Inokuchi A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science, vol 5012. Springer, Berlin, Heidelberg.
  5. Yao, H. J. Hamilton, L. Geng, “A unified framework for utility-based measures for mining itemsets”. In Proc. of ACM SIGKDD 2nd Workshop on Utility-Based Data Mining, pp. 28--37, USA, Aug., 2006.
  6. Hong Yao, Howard J. Hamilton, “Mining itemset utilities from transaction databases”, Data & Knowledge Engineering, v.59 n.3, p.603-626, December 2006.
  7. Yao, H., Hamilton, H. J., and Butz, C. J. 2004, “A foundational approach for mining itemset utilities from databases”. In Proceedings of the SIAM International Conference on Data Mining. Orlando, pp. 482-486.
  8. Erwin A., Gopalan R.P., Achuthan N.R. (2008), “Efficient Mining of High Utility Itemsets from Large Datasets”. In: Washio T., Suzuki E., Ting K.M., Inokuchi A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science, vol 5012. Springer, Berlin, Heidelberg.
  9. Ahmed, S. K. Tanbeer, B. Jeong and Y. Lee, “Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases”, in IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 12, pp. 1708-1721, Dec. 2009.
  10. Yu-Chiang Li , Jieh-Shan Yeh , Chin-Chen Chang, “Isolated items discarding strategy for discovering high utility itemsets”, Data & Knowledge Engineering, v.64 n.1, p.198-217, January, 2008.
  11. Raymond Chan , Qiang Yang , Yi-Dong Shen, “Mining High Utility Itemsets”, Proceedings of the Third IEEE International Conference on Data Mining, p.19, November 19-22, 2003.
  12. Ying Liu , Wei-keng Liao , AlokChoudhary, “A fast high utility itemsets mining algorithm”, Proceedings of the 1st international workshop on Utility-based data mining, p.90-99, August 21-21, 2005, Chicago, Illinois.
  13. Agrawal R., Faloutsos C., Swami A. (1993), “Efficient similarity search in sequence databases”. In: Lomet D.B. (eds) Foundations of Data Organization and Algorithms. FODO 1993. Lecture Notes in Computer Science, vol 730. Springer, Berlin, Heidelberg.
  14. Donald J. Berndt, James Clifford, “Using dynamic time warping to find patterns in time series”, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, July 31-August 01, 1994, Seattle, WA.
  15. Chan, K., Fu, W., “Efficient Time Series Matching by Wavelets”, Proceedings of the 15th International Conference on Data Engineering, p.126, March 23-26, 1999.
  16. Byoung-KeeYi , Christos Faloutsos, “Fast Time Sequence Indexing for Arbitrary Lp Norms”, Proceedings of the 26th International Conference on Very Large Data Bases, p.385-394, September 10-14, 2000.
  17. Yongwei Ding, Xiaohu Yang, A. J. Kavs and Juefeng Li, “A novel piecewise linear segmentation for time series”, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, 2010, pp. 52-55.
  18. Huang and P. Liou, “Retrieving Representative Structures from XML Documents Using Clustering Techniques”, 2011 European Intelligence and Security Informatics Conference, Athens, 2011, pp. 332-339.
  19. Liu and N. Cui, “A mining algorithm based on time series association rules”, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), Yichang, 2012, pp. 786-789.
  20. U. Kiran, H. Shang, M. Toyoda, M. Kitsuregawa, “Discovering recurring patterns in time series”, EDBT, pp. 97-108, 2015.
  21. Christos Faloutsos , M. Ranganathan , YannisManolopoulos, “Fast subsequence matching in time-series databases”, Proceedings of the 1994 ACM SIGMOD international conference on Management of data, p.419-429, May 24-27, 1994, Minneapolis, Minnesota, USA.
  22. PiotrIndyk , Nick Koudas , S. Muthukrishnan, “Identifying Representative Trends in Massive Time Series Data Sets Using Sketches”, Proceedings of the 26th International Conference on Very Large Data Bases, p.363-372, September 10-14, 2000.
  23. G. Elfeky, W. G. Aref and A. K. Elmagarmid, “Periodicity detection in time series databases”, in IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 7, pp. 875-887, July 2005.
  24. Ozden, S. Ramaswamy and A. Silberschatz, "Cyclic association rules," Proceedings 14th International Conference on Data Engineering, Orlando, FL, USA, 1998, pp. 412-421.
  25. Jiawei Han, Guozhu Dong and Yiwen Yin, “Efficient mining of partial periodic patterns in time series database”, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337), Sydney, NSW, Australia, 1999, pp. 106-115.
  26. A. S. Romani et al., “A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery”, in IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 1, pp. 140-150, Jan. 2013.
  27. Sheng Ma and J. L. Hellerstein, “Mining partially periodic event patterns with unknown periods”, Proceedings 17th International Conference on Data Engineering, Heidelberg, Germany, 2001, pp. 205-214.
  28. Berberidis, C., Vlahavas, I.P., Aref, W.G., Atallah, M.J., Elmagarmid, A.K., 2002. “On the discovery of weak periodicities in large time series”. In: Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 51–61.
  29. Cheng-KuiGu and Xiao-Li Dong, “Dot product based time series asynchronous periodic patterns mining algorithm”, 2009 International Conference on Machine Learning and Cybernetics, Hebei, 2009, pp. 178-182.
  30. Jiong Yang, Wei Wang and P. S. Yu, “Mining asynchronous periodic patterns in time series data”, in IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 613-628, May-June 2003.
  31. RakeshAgrawal , RamakrishnanSrikant, “Mining Sequential Patterns”, Proceedings of the Eleventh International Conference on Data Engineering, p.3-14, March 06-10, 1995.
  32. Mohammed J Zaki. (2001), “SPADE: An efficient algorithm for Mining requent Sequences”, Journal Machine Learning, Volume 42 Issue 1-2, January-February 2001 Pages 31-60.
  33. Yong-GuiZou and Hong Yu, “Moving sequential pattern mining based on Spatial Constraints in Mobile Environment”, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, 2010, pp. 103-107.
  34. Fricker, Hui Zhang and Chen Yu, “Sequential pattern mining of multimodal data streams in dyadic interactions”, 2011 IEEE International Conference on Development and Learning (ICDL), Frankfurt am Main, 2011, pp. 1-6.
  35. Haifeng Li, “A stream sequential pattern mining model”, Proceedings of 2011 International Conference on Computer Science and Network Technology, Harbin, 2011, pp. 704-707.
  36. Chang, H. Chueh and Y. Luo, “An integrated sequential patterns mining with fuzzy time-intervals”, 2012 International Conference on Systems and Informatics (ICSAI2012), Yantai, 2012, pp. 2294-2298.
  37. Tanbeer S.K., Ahmed C.F., JeongBS., Lee YK. (2009), “Discovering Periodic-Frequent Patterns in Transactional Databases”. In: Theeramunkong T., Kijsirikul B., Cercone N., Ho TB. (eds) Advances in Knowledge Discovery and Data Mining.PAKDD 2009. Lecture Notes in Computer Science, vol 5476. Springer, Berlin, Heidelberg.
  38. Surana A., Kiran R.U., Reddy P.K. (2012), “An Efficient Approach to Mine Periodic-Frequent Patterns in Transactional Databases”. In: Cao L., Huang J.Z., Bailey J., Koh Y.S., Luo J. (eds) New Frontiers in Applied Data Mining. PAKDD 2011. Lecture Notes in Computer Science, vol 7104. Springer, Berlin, Heidelberg.
  39. .UdayKiran R., Krishna Reddy P. (2010), “Towards Efficient Mining of Periodic-Frequent Patterns in Transactional Databases”. In: Bringas P.G., Hameurlain A., Quirchmayr G. (eds) Database and Expert Systems Applications. DEXA 2010. Lecture Notes in Computer Science, vol 6262. Springer, Berlin, Heidelberg.
  40. Rashid M.M., Karim M.R., JeongBS., Choi HJ. (2012), “Efficient Mining Regularly Frequent Patterns in Transactional Databases”. In: Lee S., Peng Z., Zhou X., Moon YS., Unland R., Yoo J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg.
  41. Lan, GC., Hong, TP. and Tseng V.S., “An efficient projection-based indexing approach for mining high utility itemsets”, KnowlInfSyst (2014) 38: 85.
  42. 42. G.-C. Lan, T.-P. Hong, V.S. Tseng, “An efficient gradual pruning technique for utility mining”, International Journal of Innovative Computing Information and Control, 8 (2012), pp. 5165-5178.
  43. Liu, K. Wang and B. C. M. Fung, “Direct Discovery of High Utility Itemsets without Candidate Generation”, 2012 IEEE 12th International Conference on Data Mining, Brussels, 2012, pp. 984-989.
  44. Liu Y., Liao W., Choudhary A. (2005), “A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets”. In: Ho T.B., Cheung D., Liu H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science, vol 3518. Springer, Berlin, Heidelberg.
  45. Alva Erwin , Raj P. Gopalan , N. R. Achuthan, “A bottom-up projection based algorithm for mining high utility itemsets”, Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, p.3-11, December 01-01, 2007, Gold Coast, Australia.
  46. F. Ahmed, S. K. Tanbeer, B. Jeong and Y. Lee, “Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases”, in IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 12, pp. 1708-1721, Dec. 2009.
  47. Kavitha V, Geetha B G, (2016), “High utility itemset mining with influential Cross selling items from transactional Database”, International Journal of Advanced Engineering Technology, vol 8, pp. 820 -826.
  48. Vincent S. Tseng , Cheng-Wei Wu , Bai-En Shie , Philip S. Yu, “UP-Growth: an efficient algorithm for high utility itemset mining”, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, July 25-28, 2010, Washington, DC, USA.
  49. F. Ahmed, S.K. Tanbeer, B.-S. Jeong, “A novel approach for mining high-utility sequential patterns in sequence databases”, ETRI J., 32 (5) (2010), pp. 676-686.
  50. JunfuYin ,ZhigangZheng , Longbing Cao, “USpan: an efficient algorithm for mining high utility sequential patterns”, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, August 12-16, 2012, Beijing, China.
  51. K. Alkan and P. Karagoz, “CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction”, in IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 10, pp. 2645-2657, 1 Oct. 2015.
  52. Guo-Cheng Lan, Tzung-Pei Hong, Vincent S. Tseng, Shyue-Liang Wang, “Applying the maximum utility measure in high utility sequential pattern mining”, Expert Systems with Applications, Volume 41, Issue 11, 2014, Pages 5071-5081.
  53. Zida S., Fournier-Viger P., Wu CW., Lin J.CW., Tseng V.S. (2015), “Efficient Mining of High-Utility Sequential Rules”. In: Perner P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science, vol 9166. Springer, Cham.
  54. Fournier-Viger P., Lin J.CW., Duong QH., Dam TL. (2016), “PHM: Mining Periodic High-Utility Itemsets”. In: Perner P. (eds) Advances in Data Mining. Applications and Theoretical Aspects.ICDM 2016. Lecture Notes in Computer Science, vol 9728. Springer, Cham.
  55. Ismail, M. M. Hassan and G. Fortino, “Productive-associated Periodic High-utility itemsets mining”, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, 2017, pp. 637-642.
  56. J. C. Lin, Jiexiong Zhang, P. Fournier-Viger, T. Hong, Chien-Ming Chen and J. Su, “Efficient mining of short periodic high-utility itemsets”, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, 2016, pp. 003083-003088

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16.

Authors:

Sakthivelmurugan.E, Senthilkumar G Ramakrishnan.A, Selvamuthukumaran.D

Paper Title:

Analysis of Searching Task using Chain Method in Swarm Robotics

Abstract: Swarm robotics is an emerging field within collective mobile robotics and largely inspired by studies of insect’s behavior. In defense system swarm robots are highly demanded for dangerous as well as difficult operation such as land mine detection. Land mine detection operation is similar to searching task that identifies the mines and pacifies them. From the literature survey it was found that very least of such type of problem is addressed. This project aims to search the given environment effectively, without missing out any mines; it would be better to form a chain of robots and search the area. Performance measures are affected by various factors like number of robot, area in which robots are working, presence of obstacles, etc., these factors are analyzed by Taguchi experimental design. From the analysis it was identified that speed of the chain, searching area, strategy and the interaction between searching area and strategy area were significantly affecting the performance of chain.

Keywords: Chain formation, Swarm Robotics, Strategy, Searching Area

References:

  1. M, Dorigo. M (2006), “Group transport along a robot chain in a self organised robot colony”. In: proc. Of the 9th Int. Conf. on Intelligent Autonomous Systems, IOS Press, Amsterdam, The Netherlands, 433-442.
  2. S., Gautrais, J., Theraulaz, G(2007), “The biological principles of swarm intelligence”. Swarm Intelligence, 13–31.
  3. S, Dorigo. M (2004), “Chain Formation in a Swarm Robots”. Technical Report TR/ IRIDIA.
  4. S (2006), “Chain Based Path Formation in Swarm of Robotics”. Technical Report TR/ IRIDIA.
  5. Paul M. Maxim, William M. Spears and Diana F.Spears (2009), “Robotic Chain Formations”, Robotics Enterprise Program, United States Department of Defense.
  6. D, Moller. R, Labhart. R, Pfeifer. R and Wehner. R (2000) “A mobile robot employing insect strategies for navigation”, The Journal of robotics and autonomous systems, pp 39-64.
  7. C. R and Zhang H (1997), “Task Modelling in Collective Robotics”. Autonomous Robots, Vol.4 No.1, Kluwer Academic, pp.53–72.
  8. J. A and Filliat. D (2003), “Map-Based Navigation in Mobile Robots - II. A Review of Map-Learning and Path-Planning Strategies”. The Journal of Cognitive Systems Research, Vol.4: pp. 283-317.
  9. K and Shahabudeen. P, “Applied Design of Experiments and Taguchi Methods”, The Book ISBN-978-81-203-4527-0, pp. 85-153.

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17.

Authors:

M. Ranjitham, N. V. Manjunath

Paper Title:

An Analytical and Experimental Investigation of Cold-Formed Stiffened Channel Section in Compression

Abstract: This research paper reports on the numerical, experimental study of the cold formed steel column. The primary objective is to study the buckling modes, load carrying capacity and failure load of the stiffened channel sections. The buckling mode and load carrying capacity was determined using both analytical and experimental investigation. Analytical investigation is carried using finite element model in ANSYS 16.1 and results are documented for comparison. Two types of columns are considered one without stiffeners which is open cold formed steel thin walled section, with web width of 80 mm, thickness 5mm and length of lip is 25mm. Another member with stiffeners having web width of 80 mm thickness 5mm and length of lip is 25mm. The thickness of intermediate stiffeners is varied from 10 mm, 20 mm and 30 mm. The length of the Cold formed steel column is 1000 mm. The comparison of analytical and experimental study shows that error percentage is 5% only. It’s observed that cold formed steel column may fail due to local and distortional buckling. This failure can be rectified by introducing stiffener throughout the length of the column. Also use of stiffener in the column increase the load carrying capacity. The load carrying capacity was increased by increasing width of the stiffener.

Keywords: cold formed steel, stiffener, finite element analysis and local buckling.

References:

  1. Kwon, Y.B., Kim, B.S. and Hancock, G.J. (2009), “Compression tests of high strength cold-formed steel channels with buckling interaction”, J. Constr. Steel Res., 65, 278-289.
  2. Dinis, P.B., Young, B. and Camotim, D. (2014), “Local distortional interaction in cold-formed steel rack-section columns”, Thin Wall. Struct., 81, 185-194.
  3. Schafer, M.ASCE ‘Local, Distortional, and Euler Buckling of ThinWalled columns’, Journal of Structural Engineering,Vol. 128, pp.289 -299.
  4. Ben Young and Kim J. R. Rasmussen. 1999. Behavior of cold - formed singly symmetric columns, Thin Walled Structures, vol. 33, pp. 83 - 102.
  5. Ben Young, (2004) ‘Tests and Design of Fixed-Ended Cold-Formed Steel Plain Angle Columns’, Journal of Structural Engineering, Vol.130, 1931-1940.
  6. Manikandan, P &Sukumar, S &Kannan, K. (2018). Distortional buckling behaviour of intermediate cold-formed steel lipped channel section with various web stiffeners under compression. International Journal of Advanced Structural Engineering. 10.1007/s40091-018-0191-3.
  7. Lue, D.M., Chung, PT., Liu, JL. et al. Int J Steel Struct (2009) 9: 231. Springer-Verlag 1598-2351.
  8. Bonada, M. Casafont, F. Roure, M.M. Pastor, Selection of the initial geometrical imperfection in nonlinear FE analysis of cold-formed steel rack columns, Thin-Walled Structures, Volume 51, 2012, Pages 99-111,ISSN 0263-8231.
  9. Aruna, G &Sukumar, S &Velayutham, Karthika. (2015). Study on cold-formed steel built-up square sections with intermediate flange and web stiffeners. Asian Journal of Civil Engineering. 16. 919-931.
  10. HareeshMuthuraj, S.K. Sekar, MahenMahendran and O.P. Deepak (2017), Post buckling mechanics and strength of cold-formed steel columns exhibiting Local-Distortional interaction mode failure. Structural Engineering and Mechanics Volume 64, Number 5, December10 2017, pages 621-640.
  11. Jia-Hui Zhang, Ben Young, Compression tests of cold-formed steel I-shaped open sections with edge and web stiffeners, Thin-Walled Structures, Volume 52, 2012, Pages 1-11, ISSN 0263-8231.

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18.

Authors:

Karthikamani R, Sathish Kumar.R, Divya.N

Paper Title:

Application of Sensors in Railway Tracks for Safety

Abstract: Safety is important in context to any physical mobility. The journey in train has now become more dangerous because of some natural and man-made phenomenon such as floods, earthquakes, cracks in railway tracks, climatic changes etc. To get rid of such dangerous situation sensors are used for safe journey. Many techniques such as crack detection using vibration sensor, obstacle detection using IR sensor, crack and obstacle detection using laser, image processing etc have been used. But the proposed system came out with a brilliant idea of using PIR sensor and electrochemical fatigue sensor for obstacle and crack detection. The system also uses GSM based message alerting so that engine driver can stop the train according the message received.

Keywords: Safety, Cracks, Obstacles, PIR Sensor, Electrochemical Fatigue Sensor, GSM.

References:

  1. V.Mahalakshmi , Dr.K.O.Joseph,” GPS based railway track survey system,” international journal of computer applications in engineering sciences, august 2013.
  2. Mujawar, Sangam.Borkar,” Design and implementation of wireless security system for railway tracks, IEEE international conference on power, control, signals and instrumentation engineering (icpcsi-2017).
  3. Siegfried Miickel, Frank Scherer, Peter F. Schuster,” Multi-Sensor Obstacle Detection on Railway Tracks”.
  4. Sarvesh Suhas Kapre , Saurabh Sahebrao Salunkhe , Rohan Manoj Thakkar , Akshay Prakash Pawar,Omkar Ashok Malusare,” Advanced Security Guard with PIR Sensor for Commercial and Residential use,” International journal for advance research in engineering and technology.
  5. Brent M. Phares, PE, Ph.D,” Demonstration of the electrochemical fatigue sensor system at the Transportation technology center facility.”
  6. Vinil Kumar.V, Divya.N, Mr. K.S.Vairavel,” Smart Door Lock Opening In Cars Using Face Recognition,” International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137 Volume – 02
  7. https://www.elprocus.com/gsm-architecture-features-working/
  8. Naresh Kumar, M.Uday, G. Brahmini, A.Mounika Reddy, M.Sagar Kumar, Railway Track Crack Detecting System,” International Journal of Scientific Development and Research (IJSDR) www.ijsdr.org
  9. Hari Prabhu, R.Hemalatha, S.Bhuvaneshwari,” Automatic Railway Security System Using Multisensors,” International Electrical Engineering Journal (IEEJ) Vol. 7 (2016) No.1
  10. Ankita Jadhav, Pallavi Bhangre , Snehal Gaikwad, Amol Deshpande,” Railway Track Security System,” International Journal of Engineering Research & Technology (IJERT)
  11. R Nikolaus Mahler Faruque Ahmed,” An Obstacle Detection System for Automated Trains.”
  12. https://www.electricaltechnology.org/2018/01/gsm-global-system-mobile-communications.html
  13. Krishnapriya K B , Sreelakshmi K U , Vivek John ,” Railway Level Crossing Gate Control & Measurement System for Railway Track Condition Monitoring,” International Journal of Innovative Research in Science, Engineering and Technology.
  14. Nisha S.Punekar 1 , Archana A.,” Improving Railway Safety with Obstacle Detection and Tracking System using GPS-GSM Model,” International Journal of Scientific & Engineering Research, Volume 4, August-2013.

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19.

Authors:

Karthik Prabhu T, Nagarajan, Jagadesh P, Eswaramoorthi P.

Paper Title:

Behaviour of the Steel Slag Blended Concrete by Determination of Its Elastic Properties

Abstract: The behavior of the steel slag blended concrete is experimented, by partially replacing Coarse aggregate by steel slagto study the mechanical properties by experimenting the cylinder compressive strength (CCSHH), the modulus of elasticity (MEY) and modulus of rupture (MRE). The steel slag is used as a supplementary material in the concrete. The partial replacement for the coarse aggregate replacement was done up to 8% and partial replacement of fine aggregate with steel slag was done up to 30% and the test procedure for determining the mechanical properties of concrete was confined to ASTM STP 169D, ACI :318 and IS 516 - 1959 codal regulations. The Comparison was done with the conventional concrete and the steel slag replaced concrete in terms of strength and economy for replacement mixes. Further modeling of the relationships between the mechanical properties of CCSHH, MEY and MRE of the replacement mixes was done and validated with NZS: 3101- New Zealand Standard code 3101, AS: 3600-Australian Standard code 3600) and ACI: 318-American Concrete Institute code 318)

Keywords: Enterkey about five words or phrases in alphabetical order, separated by commas. For a list of suggested keywords see: https://ieee-tmi.org/tmi-keywords.asp?s=author

References:

  1. Martys NS 1996 Survey of concrete transport properties and their measurement. NISTIR 5592, US Department of Commerce. 1–40
  2. Yu, P 1999 Structure of calcium silicate hydrate (C-S-H): near-, mid-, and far infrared spectroscopy. J. American Ceramic Society82:742–748
  3. P, Ramachandramurthy A, Murugesan Rand Sarayu K 2015 Micro analytical studies on sugar cane bagasse ash. Sadhana – Academy of science40:1629-1638
  4. Maslehuddin M, Alfarabi Sharif M, Shameem M, Ibrahim M and Barry M S (2003),"Comparison of properties of steel slag and crushed limestone aggregate concretes", J. Construction and building materials, vol :17(2), pp. 105-112
  5. KarthikPrabhu T and Subramanian K 2016 Experimental Studies on RCP by using Cost Effective and Recycled Materials for Improving the Strength and Reducing the Cost of Pavement.Asian Journal of Research in Social Sciences and Humanities, 6: 1 -14
  6. BIS (Bureau of Indian Standards). (2013a). “Ordinary Portland Cement 53 Grade- Specification” IS 12269 IS 1199, New Delhi, India.
  7. BIS (Bureau of Indian Standards). (2016). “Specification of coarse and fine aggregates from natural sources for concrete.” IS 383, NewDelhi, India.
  8. (2009). “Standard test method for bulk density (“unit weight”) and voids in aggregate.” ASTM C29/C29M-09, West Conshohocken, PA.
  9. Caijun Shi (2004), "Steel Slag—Its Production, Processing, Characteristics, and Cementitious Properties", Mater. Civ. Eng., 16(3): 230-236
  10. BIS (Bureau of Indian Standards). (1959a). “Methods of sampling and analysis of concrete.” IS 1199, New Delhi, India.
  11. BIS (Bureau of Indian Standards). (1959b) Reaffirmed 2004. “Methods of tests for strength of concrete.” IS 516, New Delhi, India
  12. BIS (Bureau of Indian Standards). (2012). “Specification for cement concrete flooring tiles.” IS 1237, New Delhi, India.
  13. DIN (DeutschesInstitut fur NormungeV). (1991-06). “Testing of hardened concrete (specimens prepared in mould).” DIN 1048 Part 5 1991, Germany.
  14. Ganjian, E., Khorami, M., and Maghsoudi, A. A. (2009) “Scrap-tyre-rubber replacement for aggregate and filler in concrete.” Constr. Build. Mater., 23(5), 1828–1836.
  15. RILEM, CPC-18. (1988). “Measurement of hardened concrete carbonation depth.” Mater. Struct., 21(6), 453–455.
  16. Manju R, Sathya S and Sylviya B (2017),“Shear Strength of High-Strength Steel Fibre Reinforced Concrete Rectangular Beams”, International Journal of Civil Engineering and Technology, Vol. 8, Issue 8, pp. 1716-1729.
  17. Ramadevi K, “A study on Properties of Concrete with Ceramic Waste Replaced for Fine Aggregate (2017),” International Journal of Civil Engineering and Technology, Vol. 8, Issue 8, pp. 1730-1737.

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20.

Authors:

PA. Prabakaran, G. L. Sathyamoorty, M. Adhimayan

Paper Title:

An Experimental and Comparative Study on Canal Lining Exploitation Geosynthetic Material, Cement Mortar and Material Lining

Abstract: This project is principally supported water insufficiency, a serious cause for individuals for all functions chiefly for irrigation. to beat this and to boost the potency of water flow and discharge in canals, the lining ways for canals ought to be modified as less permeable , increase in velocity and discharge. Canal lining is that the method of reducing flow loss of irrigation water by adding an imperviable layer. Technological development and producing of recent materials helps in varied functions. One such issue was the event of geosynthetic materials that was wide employed in construction fields in conjunction with concrete or as a separate material because the replacement for concrete. we have a tendency to selected PVC geosynthetic material for lining the canal rather than concrete, brick masonry and traditional material lining for canals. we have a tendency to create a comparative study for 3 canal linings like PVC, brick masonry and material lining close to Pollachi of alittle paradigm model in Mr. Sekar farm and notice the foremost economical material appropriate for canal lining altogether forms

Keywords: Canal Analysis’s, Effective Discharge, Most Economical-Comparative Study.

References:

  1. A report on “Studies on issues related on gap between Irrigation potential created and utilised”, IIM, Lucknow.
  2. A technical report on “Canal lining demonstration project” year 7durability report, September 1999
  3. Mishra et.al(2001), Hydraulic modeling of kangsabatimain canal for performance assessment, Journal of Irrigation and Drainage Engineering, Vol. 127, No. 1, January/February, 2001. Conference on geotextiles, Geomembranes and Related products”, Singapore,59.Pg 573-578.
  4. K. Rastogi(1992), FEM modelling to investigate seepage losses from the lined Nadiad branch canal, India, Journal of Hydrology,Elsevier,Vol.138,Issue1-2, sept.,1992, pages 153-168.
  5. B J Batliwala,J N Patel,P D Porey,2014, “Seepage Analysis of Kakrapar Right Bank Main Canal of Kakrapar Project, Gujarat, India” IJSRD, Vol11
  6. Charles M. Burt et.al(2010), Canal Seepage Reduction by Soil Compaction , Journal of Irrigation and Drainage Engineering, Vol. 136, No. 7, July 1, 2010. ©ASCE
  7. David McGraw et.al(2011), Development of tools to estimate conveyance losses in the Truckee River, USA ,Hydrogeology Journal Springer-Verlag 2011 Economic Analysis Guidebook, Department of Water Resource, California.
  8. ErhanAkkuzu et.al(2007), Determination of Water Conveyance Loss in the Menemen Open Canal Irrigation Network, Turk J Agric For 31 (2007) 11-22 c TUB‹TAK
  9. ErhanAkkuzu1 (2011), The Usefulness of Empirical Equations in Assessing Canal Losses Through Seepage in Concrete-Lined Canal, Journal of Irrigation and Drainage Engineering. /(ASCE)IR.1943-4774.0000414.
  10. Eric Leigh et.al, (2002), Seepage Loss Test ResultsIn Cameron County Irrigation District No. 2, Report Prepared for Cameron County Irrigation District No. 2 by Eric Leigh and Guy Fipps, P.E.2 in December 18, 2002
  11. Garg SK, Irrigation And hydrolic structure by Khanna Publishers 2006
  12. J. McGowen1(2001), Identifying channel seepage using pre-dawn thermal imagery, Geoscience and Remote Sensing Symposium, IEEE 2001,On page(s): 1631-1633 vol.4
  13. P. Giroud,J.G. Zornberg, and A. Zhao,7 October 2000,“Hydraulic Design of Geosynthetic and Granular Liquid Collection Layers”Geosynthetics International is published by the Industrial Fabrics Association International, Special Issue on Liquid Collection Systems, Vol. 7, Nos. 4-6, pp. 285-380.
  14. I.Comer, September 1994, “Water Conservation strategies using Geosynthetic. Fifth International

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21.

Authors:

M. Jamuna

Paper Title:

Statistical Analysis of Ground Water Quality Parameters in Erode District, Taminadu, India

Abstract: Water that can be used for drinking, cooking, land farming, etc should be free from physical and chemical contaminants. The major source of drinking water is ground water. The pore spaces and the fractures in rocks carries the major amount ground water which is found beneath the Earth’s surface. The objective of this study is to predict the ground water quality in the Erode district of Tamil Nadu, India. A total number of 26 samples were collected from different regions of the district. The present study deals with the analysis of physical-chemical parameters, Multivariate statistical analysis for predicting the ground water quality in the Erode district. Multivariate statistical method involves determination of correlation and factor analysis. Interpretation of analytical data showed the water quality variance. Finally it was found that there is a variance of about 63.273% in ground water quality during the pre-monsoon season and about 73.624% in post monsoon season.

Keywords: Groundwater, Physical-Chemical Parameters, Multivariate Statistical Methods, Correlation Analysis, Factor And Cluster Analysis

References:

  1. Aabha P Sargaonkar, Apurba Gupta and Sukumar Devotta (2008), “Multivariate Analysis of Groundwater Resources in Ganga-Yamuna Basin,” Journal of Environmental science and Engineering, Vol. 50, No. 3, pp. 215-222.
  2. Abdulmuhsin S. Shihab, Yousra T. Abdul Baqi (2010), "Multivariate Anlaysis of Ground Water Quality of Makhmor Plain/ North Iraq," Journal of Damascus University., vol(26), no(1).
  3. Ali M. Subyani and Masoud E. Al Ahmadi (2009), "Multivariate Statistical Analysis of Groundwater Quality in Wadi Ranyah, Saudi Arabia," JAKU: Earth Sci., vol. 21, no. 2, pp. 29-46.
  4. Andrea R. Pearce, Donna M. Rizzo, and J. Mouser (2011), “Subsurface characterization of groundwater contaminated by landfill leachate using microbial community profile data and a nonparametric decision-making process,” Water Resources Research, Vol. 47.
  5. Asif Mahmood, Waqas Muqbool, Muhammad Waseem Mumtaz and Farooq Ahmad (2011), "Application of Multivariate Statistical Techniques for the Characterization of Ground Water Quality of Lahore, Gujranwala and Silakot (Pakistan)," Pak. J. Anal. Environ. Chem., vol 12, no 1 & 2, pp. 102-112.
  6. S, Das. R, Ruj. B, Adhikari. K, Chatterjee. P.K. (2012), "Assessment by multivariate statistical analysis of ground water geochemical data of Bankura, India," International journal of Environmental sciences., vol. 3, no. 2.
  7. Charmaine Jerome and Anitha Pius (2010), “Evaluation of water quality index and its impact on the quality of life industrial area in Bangalore, South India,” American Journal of Scientific and Industrial Research.
  8. J, Vadivel. S, Ganeshkarthick. E (2012), “Physico-Chemical Analysis of Ground Water Samples of Selected Districts of Tamilnadu And Kerala,” International Journal of Scientific & Technology Research., Vol. 1.
  9. C and Thamarai. P (2008), “Study on Statistical Relationship between Ground Water quality parameters in Nambiyar River Basin, Tamil Nadu, India,” Poll Res, Vol. 27 (4), pp. 679-683.
  10. Golzar Hossain. MD, Selim Reza. A.H.M, Lutfun-Nessa. MST and Syed Samsuddin Ahmed (2013), “Factor and Cluster Analysis of water quality data of the Groundwater Wells of Kushtia, Bangladesh: Implication for Arsenic Enrichment and Mobilization,” Journal Geological Society of India., Vol. 81, pp. 377-384.
  11. S.M, Aris. A.Z (2010), “Groundwater resources assessment using numerical model: A case study in low-lying coastal area,” International Journal of Environmental Science, Vol. 7(1), pp. 135-146.
  12. M, Gurugnanam. B, Suganya. M and Vasudevan. S (2009), “Multivariate statistical Analysis of Geochemical Data of Groundwater in Veeranam Catchment Area, Tamil Nadu,” Journal Geological Society of Inida., Vol. 74, pp. 573-578.

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22.

Authors:

Lisa Mary Thomas, S.K.Shivaranjani

Paper Title:

Strength and Light Transmittance of Plastic Fiber Concrete

Abstract: The present consumption of electricity at a high rate for illumination purposes calls for new and innovative methods to use the natural source of lighting ie.sunlight. This study aims the use of plastic fiber in concrete in order to transmit sunlight into buildings. The properties of the cement, aggregate and plastic fiber were analyzed. The cube specimen prepared was tested for light transmittance property and compressive strength. Loss of signal strength was found to be 0.2V when attenuation experiment was carried out. The intensity of light coming from the concrete cube was found to be sufficient for viewing purpose. The cube specimens also gave sufficient strength results which makes it suitable for its use in purposes.

Keywords: Plastic Fiber, Attenuation, Light Intensity, Compressive Strength

References:

  1. Jianping He,Zhi Zhou ,2011,”Study On Smart Transparent ConcreteProduct And Its Performance", Journal of advanced smart materials and smart structures technology ,pg 25-38
  2. Bhavin K Khashiyani,Varsha Raina,Feb 2013”A Study On Transparent Concrete: A Novel Architecture Material To Explore Construction Sector", International journal of engineering and innovative technology Vol 2 issue 8
  3. A.A. Momin et al,2014,IOSR Journal of Mechanical and Civil Engineering(IOSR-JMCE)pg 67-72
  4. Jianping He et al.2011, Study on Smart Transparent Concrete Product and Its Performances,The 6th International Workshop on Advanced Smart Materials and Smart Structures Technology ANCRiSST2011,July 25-26, 2011, Dalian, China
  5. Kalymnios, D. Plastic Optical Fibers (POF) in sensing – current status and prospects. 17th International Conference on OpticalFiber Sensors SPIE, 5855, 2005
  6. Vishnu A,Mohana V,Manasi S,V Ponmalar, Use of polyethylene terephthalate in concrete-A Brief reviewInternational Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 7, July 2017, pp.1171-1176

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23.

Authors:

Nishaant Ha, Anand T, Sachin Prabhu P, Dayaanandan M

Paper Title:

Risk Mitigation of Construction Projects in Hilly Areas

Abstract: In construction industry, there is no risk free project. There are various types of risk associated in a hilly construction project. Risk is identified and then risk assessment and analysis is done. Then risk management and risk mitigation process is carried out in order to reduce risk. Various types of risk associated with hilly construction projects are identified from case study, papers and field study. The identified risk factors are grouped under 9 categories. Risk factor is characterized by its occurrence and impact. The occurrence and impact score for different types of risk are collected from site and online survey from experts and weighted average mean interval score is calculated. From the score value obtained, the risk priority is given using risk heat map. Then find feasible strategy to mitigate the risk in hilly areas.

Keywords: Risk Management, Risk Factors, Risk Assessment, Risk Factors, Risk Priority, Feasible Strategy

References:

  1. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-5, October 2013 A Study of Risk Management Techniques for Construction Projects in Developing Countries, Patel Ankit Mahendra, Jayeshkumar R. Pitroda, J. J. Bhavsar
  2. Darius migilinskas; Leonasustinovicity, et.al(2008) “Methodology of Risk and Uncertainty Management in Constructions Technologies and Economical Problems”ISARC-2008.
  3. P.Ganapathy; C.L.Hada (2012), “Landslide Hazard Mitigation in the Nilgiris District, India – Environmental and Societal Issues” International Journal of Environmental Science and Development, Vol. 3, No. 5, October 2012.
  4. Ganapathy; K.Mahendran (2010) “Need and Urgency of Landslide Risk Planning for NilgiriDistrict,Tamil Nadu State”,International Journal Of Geomatics And Geosciences Volume 1, No 1, 2010.
  5. Hyun –hochoi; HYO-NAM Cho et.al(April 2004), “Risk assessment methodology for underground construction project” J Constr. Eng. Management.
  6. Dr Patrick. X.W. Zou1; Dr Guomin Zhang et,al “Identifying Key Risks in Construction Projects: Life Cycle and Stakeholder Perspectives”
  7. Sathishkumar; P.N.Raghunath et.al(31 January 2015). “Critical Factors Influencing to Management Risk in Construction Projects”, The International Journal Of Engineering And Science (IJES).
  8. M. Renuka, C.Umarani, et.al.(2014). “A Review on Critical Risk Factors in the Life Cycle of Construction Projects” Journal of Civil Engineering Research 2014, 4(2A): 31-36 DOI: 10.5923/c.jce.201401.07.
  9. Nishaant Ha, Anand T “Durability Gaining in an Old Structure Using Retrofitting Techniques” International journal of Civil Engineering and Technology (IJCIET)-Vol.8, Issue 8, August 2017.
  10. Anand T, Nishaant Ha “Implementing Challenges of Extended Producer Responsibility” International journal of Civil Engineering and Technology (IJCIET)- Vol.8, Issue 7, July 2017.

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24.

Authors:

R.Rajkumar, A. James Albert, S.P. Siddique Ibrahim

Paper Title:

Feature Selection with Enhanced Bat Algorithm and Modified Recursive Bayesian Deep Neural Network (MRBDNN) for Temperature Prediction

Abstract: Weather forecasting is major problemin ecological science. Existing statistical and Climate models are ineffective prediction tools of the long run temperature. Exact weather forecasting is tedious tasks that deal with huge amount of data . In this paper, an Enhanced Bat Algorithm (EBA) is proposed for selection of features from the temperature dataset. High dimensionality of data based on DNN with RMBLR are attempted in this work. Based on analysis of monthly high, average and low temperatures data sets, a novel Recursive Modified Bayesian Linear Regression (RMBLR) algorithm based on Deep Neural Network (DNN) is presented in this study.

Keywords: Feature Selection, BAT Algorithm, Recursive Bayesian, Temperature Prediction, Deep Neural Network.

References:

  1. Biaobing Huang, Guihe Qin, Rui Zhao, Qiong Wu, AlirezaShahriari, “Recursive Bayesian echo state network with an adaptive inflation factor for temperature prediction” , Neural Computer and Application, November 12, 2016.
  2. Radhika, M.Shashi, “Atmospheric temperature prediction using Support Vector Machine”, International Journal of Computer Theory and Engineering, Vol.1, No.1, April 2009.
  3. Dipi A. Patel, R.A.Christian, “Ambient atmospheric temperature prediction using fuzzy knowledge-rule based for inland cities in India”, World Applied Sciences Journal, 2012.
  4. TarunRao, N.Rajasekhar, Dr.T.V.Rajinikanth, “An efficient approach for weather forecasting using Support Vector Machine”, International Conference on Computer Technology and Science, Vol.47, 2012.
  5. Amartya Raj Gurung, “Forecasting weather system using Artificial Neural Network (ANN): A survey paper”, International journal of Latest Engineering Research and applications (IJLERA), Vol.02, October 2017.
  6. Deepak RanjanNayak, AmitavMahapatra, Pranati Mishra, “A survey on rainfall prediction using Artificial Neural Network”, International Journal of Computer Applications, Vol.72, No.16, June 2013.
  7. Herbert Jaeger, Harald Hass, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication”, Science, Vol.304, April 2, 2004.
  8. S.De, A.Debnath, “Artificial neural network based prediction of maximum and minimum temperature in summer monsoon months over India”, Applied Physics Research, Vol.1, No.2, November 2009.
  9. Shaminder Singh, PankajBhambri, Jasmeen Gill, “Time series based temperature prediction using back propagation with genetic algorithm technique”, International Journal of Computer Science Issues, Vol.8, Issue 5, No.3 September 2011.
  10. Pankaj Kumar, “Minimum weekly temperature forecasting using ANFIS”, Computer Engineering and Intelligent Systems, Vol.3, No.5, 2012
  11. Zhen, Z. Yongquan, and L. U. Mindi, “A simplified Adaptive Bat Algorithm Based on Frequency”, J.Comput. Inf. Syst., vol. 9: 16, pp. 6451–6458, 2013.

96-99

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25.

Authors:

M. Mohankumar, M Anand Kumar, S. Aruna Devi, R. Suresh Kumar

Paper Title:

Requirement Analysis Document in Google Drive for Green and Sustainable Software Engineering Approach

Abstract: This study shows how a requirement analysis can help to organizations become more environmentally sustainable in a structured and efficient manner, for this we have analyzed the Google Drive document as a requirement analysis document with the help of that document we try to cover the software requirement specification from the customer, then we try to observe the if that document located in desktop pc what is the cumulative processor energy, IA energy and GT energy, if that document shared with cloud environment minimum and maximum communication of resource sharing details are analyzed for user base and data center of various regions, finally the load event details are observed for the requirement document shared in the Google drive , This result show that the technologies delivers specific suggestions for improvement both on reducing the environmental foot print of ICT and on using ICT as a green solution for software requirement analysis process.

Keywords: Green ICT, IA Energy, GT Energy, Google Drive, Software Requirement Specification

References:

  1. Chitchyanet al., "Sustainability Design in Requirements Engineering: State of Practice," 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), Austin, TX, 2016, pp. 533-542.
  2. Mendez Fernandez, S. Wagner Naming the Pain in Requirements Engineering: Design of a Global Family of Surveys and First Results from GermanyIn: Proc. of the 17th International Conference on Evaluation and Assessment in Software Engineering (EASE ’13), ACM, 2013.
  3. Dhingra, Savithri G, M. Madan and Manjula R, "Selection of prioritization technique for software requirement using Fuzzy Logic and Decision Tree,"2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, 2016, pp. 1-11.
    doi: 10.1109/GET.2016.7916822
  4. UmmaKhatunaJannat ,” Green Software Engineering Adaption In Requirement Elicitation Process”2016 international journal of scientific & technology research volume 5, issue 08, august 2016 issn 2277-8616
  5. Hankel and P. Lago, "How organisations can assess and improve their green ICT activities in a standard and efficient way," 2016 ITU Kaleidoscope: ICTs for a Sustainable World (ITU WT), Bangkok, 2016, pp. 1-6
  6. Erik Jagroep” Extending software architecture views with an energy consumption perspective Computing, 2017, Volume 99, Number 6, Page 553
  7. Paul p.k,”Is green computing a social software engineering domain?”,2016 international journal of applied science and engineering 4(2).PP.67-73
  8. Becker, Colin (2016) Requirements: The Key to Sustainability. IEEE Software, 33 (1). pp. 56-­65. ISSN 0740­7459
  9. C. Venters et al., "Characterising Sustainability Requirements: A New Species Red Herring or Just an Odd Fish?," 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS), Buenos Aires, 2017, pp. 3-12.
  10. VivekShukla, DhirendraPandey and Raj Shree. Article: Requirements Engineering: A Survey. Communications on Applied Electronics3(5):28-31, November 2015. Published by Foundation of Computer Science (FCS), NY, USA
  11. Becker, D. Walker and C. McCord, "Intertemporal Choice: Decision Making and Time in Software Engineering," 2017 IEEE/ACM 10th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), Buenos Aires, 2017, pp. 23-29
  12. Marimuthu K. Chandrasekaran," Software Engineering Aspects of Green and Sustainable Software: A Systematic Mapping Study". Proceedings of the 10th Innovations in Software Engineering Conference Jaipur, India 2017 PP[ 34-44]
  13. Komeilraisian,” Current Challenges And Conceptual Model of Green And Sustainable Software Engineering,”Journal of Theoretical and Applied Information Technology
    31st December 2016 -- Vol. 94. No. 2 – 2016
  14. Torre, G. Procaccianti, D. Fucci, S. Lutovac and G. Scanniello, "On the Presence of Green and Sustainable Software Engineering in Higher Education Curricula," 2017 IEEE/ACM 1st International Workshop on Software Engineering Curricula for Millennials (SECM), Buenos Aires, 2017, pp. 54-60.
  15. Rashid,” Developing Green and Sustainable Software Using Agile Methods in Global Software Development: Risk Factors for Vendors”,2017 Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering ENASE 2016 PP 247—253
  16. Maqbool Ahmed Muhammad Azeem,” Requirement Engineering the backbone of a project”,2017,researchgate.net/publication/318787262,
  17. Reza, R. Sehgal, J. Straub and N. Alexander, "Toward model-based requirement engineering tool support," 2017 IEEE Aerospace Conference, Big Sky, MT, 2017, pp. 1-10.
  18. H. Khan, M. N. bin Mahrin and S. btChuprat, "Situational requirement engineering framework for Global Software Development," 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, 2014, pp. 224-229.
  19. Sitthithanasakul and N. Choosri, "Application of software requirement engineering for ontology construction," 2017 International Conference on Digital Arts, Media and Technology (ICDAMT), Chiang Mai, 2017, pp. 447-453.
  20. Arunadevi, Dr.VijetaIyer, “A Study on M/M/C Queueing Model under Monte Carlo Simulation in Traffic Model”, 2017, International Journal of pure and Applied Mathematics(IJPAM), No:12, vol 116, pg:199-207.
  21. http://sustainabilitydesign.org

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26.

Authors:

M. Parimala, D. Arivuoli, R. Jeevitha

Paper Title:

Composition of Functions under Niαg Continuous and Niαg-Irresolute Functions in Nano Ideal Topological Spaces

Abstract: This research paper targets nIαg-continuous, nIαg-irresolute functions in nano ideal topological spaces. Comparison and Characterisation of these functions are explored. Preservation of composition of functions under nIαg-continuous, nIαg- irresolute functions are also proved. 2010 Mathematics Subject Classification.. 54A05, 54A10, 54B05.

Keywords: NIαg-continuous function, nIαg-irresolute function,Composition of functions.

References:

  1. Kuratowski, Topology, Vol. I. Academic Press, New York, 1996.
  2. Jankovic and T. R. Hamlett, New topologies from old via ideals, Amer.Math. Monthly, 97(4) (1990), 295 - 310.
  3. .D. Jankovi and T. R. Hamlett, Compatible extensions of ideals, Boll. Un.Mat. Ital. (7), 6-B (1992), 453-465.
  4. Lellis Thivagar and Carmel Richard, On nano continuity, International Journal of Math ematics and statistics invention,Volume 1,Issue 1, August 2013, PP-31-37.
  5. Bhuvaneswari and K.Mythili Gnanapriya, Nano generalized closed sets in nano topo logical spaces, International Journal of Scientic and Research Publications, Volume 4, Issue 5, May 2014, 1-3.
  6. Lellis Thivagar and Carmel Richard, On nano forms of weakly open sets, Mathematical Theory and Modeling ISSN 2224-5804 (Paper) ISSN 2225- 0522 (Online), Vol.3, No.7, 2013, PP-32-37.
  7. .M. Parimala and S. Jafari, Decomposition of continuity in nano ideal topo- logical spaces. - communicated.
  8. Parimala, T. Noiri and S. Jafari, New types of nano topological spaces via nano ideals - communicated.
  9. .M. Parimala and R. Perumal, Weaker form of open sets in nano ideal topo- logical spaces, Global Journal of Pure and Applied Mathematics (GJPAM), Volume 12, Number 1 (2016), 302-305.
  10. Parimala, S. Jafari and S. Murali, Nano Ideal Generalised Closed Sets in Nano Ideal Topological Spaces, Annales Univ. Sci. Budapest., 60 (2017), 3-11.
  11. .M.Parimala, D.Arivuoli, nIg-closed sets and Normality via nIg-closed sets in Nano Ideal Topological
  12. R.Thanga Nachiyar, K.Bhuvaneswari, On nano generalised α-closed sets and nano α-generalised closed sets in nano topological spaces, International Journal of Engineering Trends and Technology.

107-110

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27.

Authors:

Vijeta Iyer, S R Saratha, V Sudha

Paper Title:

Function-E-Chainble Sets in Bitopological Space

Abstract: Inthis paper,the concept of function-ϵ – chainability between two sets in bitopological spaces using continuous function has been introduced which is the extension of function-ε-chain between two points of the bitopological space. A characterization of function- ϵ-chainability betweentwo sets has been discussed in respect of function – ϵ-chains between their points. Also, some results of [1] have been generalized for bitopological spaces.Subject Classification: AMS (2000):54A99

Keywords: Simple chain, function -ϵ-chain between points in bitopological space, function - ϵ-chain between sets in bitopological space.

References:

  1. Shrivastava kiran, agrawal geeta, Characterization of ε-chainable sets in metric spaces, Indian Journal of Pure and Applied Mathematics, 33(6):933-940, 2002
  2. Munkers, james R, Topology, A first Course PHI, 1987.
  3. Lipschutz, S., Schaum’s outline of theory and problems of General Topology, 1965.
  4. Kelly, j.l., general Topology, Van Nostrand Reinhold Company, New York, 1969.
  5. Joshi, k.d., Introduction to General Topology, Wiley Eastern Limited, 1992.
  6. Steen, lynn arthur; seebach, j. Arthur jr.(1995)[1978], Counterexamples in topology (Dover reprint of 1978 ed.), Berlin, New York: Springer- Verlag.
  7. Iyer vijeta, shrivastava kiran, choudhary priya, Chainability in topological spaces through continuous functions, International Journal of Pure and Applied Mathematics, Vol. 84, No. 3, 2013, 269-277.
  8. Iyer vijeta, saratha s r, Chainability in bitopological spaces through continuous functions, (communicated).
  9. Priya choudhary, kiran shrivastava, vijeta iyer, Characterization of function-ε-chainable sets in topological space, Mathematical Theory And Modeling, Vol.3, No.6,2013, 189-192.

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28.

Authors:

Ramalatha Marimuthu, Navaneethakrishnan R, Alagu Meenakshi, Uma Maheswari S

Paper Title:

Catch Them Young: Importance of Career Planning in Indian School Education Systems

Abstract: Higher Education Systems receive a lot of attention from the researchers on identifying good teaching practices in higher education to make it student attractive since it is the nearest point of education towards career and early life education is often ignored. But the primary factor in the choice of higher education in India is the current popularity of the stream (on the basis of the mass and media opinion) and not the aspiration and interest of the students. The lack of systematic student profiling to understand their strengths and encourage them in the correct career path is the major drawback of the Indian School Education System. This paper provides a framework for the steps to be followed to introduce life career planning education in schools and the various factors to be considered while profiling the students.

Keywords: student profiling, SWOT analysis, school education, career and life planning

References:

  1. https://timesofindia.indiatimes.com/home/education/ news/ 60-of-engineering- graduates- unemployed/ articleshow/ Cms
  2. https://www.gnu.org/education/edu-system-india.en.html
  3. Arjit Ghosh, Rittika Chanda Parruk, Sasha Sheppard, "Indian School Education System An Overview", The British Council, India, 2014.
  4. Kamlesh Gakhar, Harjeet Kour, "Scenario Of Present Education System: A Comparative Study Of Haryana And Its Neighbouring States", International Journal of Social Science & Interdisciplinary Research Vol.1 Issue 8, August 2012
  5. Gretchen Rhines Cheney, Betsy Brown Ruzzi and KarthikMuralidharan, "A Profile of the Indian Education System", National Center on Education and the Economy, 2006
  6. Gautam, Mohan & Singh, Sunny &Fartyal, Gopal&Tiwari, Ankit& Singh Arya, Kuldeep. (2016). Education System in Modern India. International Journal of Scientific Research And Education. 10.18535/ijsre/v4i01.16.
  7. Urvashi Sahni, "Primary Education in India:Progress and Challenges" Brookings Report, January 2015
  8. Akash A.R., Ramalatha Marimuthu, Navaneethakrishnan R, Kanagaraj S, "Cultural factors impacting the Global Energy transition- a review, International Conference on Renewable Energies, Power Systems and Green Inclusive Economy, 23-24, April 2018, Casablanca, Morocco.
  9. V.Mohana Sundaram, SWOT analysis of Indian Higher Education, ECONSPEAK, A Journal Of Advances In Management, IT and Social Sciences, Volume 1, Issue 3 (September, 2011)
  10. Guide on Life Planning Education and Career Guidance for Secondary Schools, Career Guidance Section, School Development Division, Education Bureau, (May 2014)
  11. Ramalatha Marimuthu, S.Sathyavathi, “Impact of service learning and social immersion on education and career building of young Indian Engineering graduates – A case study”, IEEE International Women in Engineering Conference on Electrical, Electronics and Com[puter Engineering, Pune December 2016.

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29.

Authors:

K. Paramasivam, Suresh Kumar N

Paper Title:

Minimum Power Consumption High Efficiency Bypassing-Based 2D Multiplier Design using 65nm CMOS Technology

Abstract: Presently, in VLSI design, Power management has turn out to be a major issue. In this research, a Minimum Power Consumption Bypassing-Based 2D Multiplier Design using 65nm CMOS Technology was presented. When matched up with digital row bypassing based multiplier design, digital column bypassing based multiplier design and digital low power two-dimension bypassing based multiplier design, the experimentation outcomes shown our presented Multiplier Design decreases 31.2% of the power dissipation for 4*4 Multiplier.

Keywords: Low Power, Multiplier, CMOS, Bypassing

References:

  1. Sureshkumar N, K.Paramasivam, “Bypassing-Based Multiplier Design: A Tutorial and Research Survey”, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.29 (2015) pp:22606-22613.
  2. Senthil Kumar and Dr.K.Paramasivam“Test Power Minimization of VLSI Circuits: A Survey” in the IEEE sponsored 4th International Conference on Computing Communication and Networking Technologies – ICCCNT 2013 at Vivekanandha College of Engineering for Women, Tiruchengode, 4-6th July 2013. The paper is available in IEEE Xplore PortalDOI:10.1109/ICCCNT.2013.6726569. pp.1-6.
  3. Chitra, Dr.K.Paramasivam, “Design of Adiabatic Logic Cells for Efficient Power Reduction and Area Characteristics”, International Journal of Systems, Algorithms & Applications, Volume 2, Issue 11, November 2012, ISSN Online: 2277-2677.
  4. G. Moshnyaga and K. Tamaru, “A comparative study of switching activity reduction techniques for design of low power multipliers,” IEEE International Symposium on Circuits and Systems, pp.1560-1563,1995.
  5. Wu, “High performance adder cell for low power pipelined multiplier,” IEEE International Symposium on Circuits and Systems, pp.57–60, 1996.
  6. Ahn and K. Choi, “dynamic operand interchange for low power,” Electronics Letters, Vol. 33, no. 25, pp.2118-2120, 1997.
  7. Choi, J. Jeon and K. Choi, “Power minimization of functional units by partially guarded computation,” International Symposium on Low-power Electronics and Design, pp.131-136, 2000
  8. Ohban, V. G. Moshnyaga, and K. Inoue, “Multiplier energy reduction through bypassing of partial products,” IEEE Asia-Pacific Conference on Circuits and Systems, pp.13–17, 2002.
  9. C. Wen, S. J. Wang and Y. M. Lin, “Low power parallel multiplier with column bypassing, “IEEE International Symposium on Circuits and Systems, pp.1638-1641, 2005.
  10. N. Sung, Y. J. Ciou and C. C. Wang, “A power-aware 2- dimensional bypassing multiplier using cell- based design flow,” IEEE International Symposium on Circuits and Systems, pp.3338-3341, 2008.
  11. T. Yan and Z. W. Chen, “Low-power multiplier design with row and column bypassing,” IEEE International SOC Conference, pp.227-230, 2009.
  12. Jin-Tai Yan, Zhi-Wei Chen “Low-Cost Low-Power Bypassing-Based Multiplier Design” IEEE International Symposium on Circuits and Systems,pp 2338-2341, 2010.
  13. Kalamani, K. Paramasivam “Survey of Low Power Testing Using Compression Techniques”, International Journal of Electronics and Communication Technology /Volume-4, Issue-4, Oct-Dec 2013.

119-123

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30.

Authors:

K.Paramasivam, Suresh Kumar N

Paper Title:

Design and Anlaysis of Low Power Full Adder using 65nm CMOS Technology

Abstract:This Paper deals with Low-Power full adder using 65nm CMOS Technology by taking merits of existing full-adders. The proposed one-bit full adder has least power consumption. The proposed adder compared and then analyzed average power, Area and Max power with existing full adder. The designs have been simulated shown results using Tanner EDA tool.

Keywords: CMOS, Area, Average Power

References:

  1. Sureshkumar N, K.Paramasivam, “Bypassing-Based Multiplier Design: A Tutorial and Research Survey”, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 10 No.29 (2015) pp:22606-22613.
  2. Senthil Kumar and Dr.K.Paramasivam“Test Power Minimization of VLSI Circuits: A Survey” in the IEEE sponsored 4th International Conference on Computing Communication and Networking Technologies – ICCCNT 2013 at Vivekanandha College of Engineering for Women, Tiruchengode, 4-6th July 2013. The paper is available in IEEE Xplore PortalDOI:10.1109/ICCCNT.2013.6726569. pp.1-6.
  3. Chitra, Dr.K.Paramasivam, “Design of Adiabatic Logic Cells for Efficient Power Reduction and Area Characteristics”, International Journal of Systems, Algorithms & Applications, Volume 2, Issue 11, November 2012, ISSN Online: 2277-2677.
  4. Goel, A. Kumar, and M. A. Bayoumi, “Design of robust, energy efficient full adders for deep-sub micrometer design using hybrid-CMOS logic style,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 14,no. 12, pp. 1309–1321, Dec. 2006.
  5. Jan M. Rabaey, AnanthaChadrakasan, BorivojeNikolic ;”Digital Integrated Circuits: Design approach”. of IEEE International Conference on Power, Control and Embedded System (ICPCES), 28 Nov.-1Dec. 2010.
  6. Zhuang and H. Wu, “A new design of the CMOS full adder,” JSSC, vol. 27, no. 5, May 1992, pp. 840-844.
  7. Jan M. Rabaey, AnanthaChadrakasan, BorivojeNikolic;”Digital integrated Circuits : Design approach” .of IEEE International Conference on Power, Control and Embedded System (ICPCES), 28 Nov.-1Dec. 2010.
  8. Shubhajit Roy Chowdhury, Aritra Banerjee, Aniruddha Roy, HiranmaySaha, “A high Speed 8 Transistor Full Adder Design using Novel 3 Transistor XOR Gates”, International Journal of Electronics, Circuits and Systems 2, p-218,2008
  9. Shiv Shankar Mishra, Adarsh Kumar Agrawal and R.K.Nagaria ”A comparative performance analysis of various CMOS design techniques for XOR and XNOR circuits”, International Journal on Emerging Technologies, ISSN : 0975-8364.
  10. Saradindu Panda, A. Banerjee, B.Maji, Dr.A.K.Mukhopadhyay, ”Power and Delay Comparison in between Different types of Full Adder Circuits”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, ISSN: 2278 - 8875.
  11. FengboRen, and DejanMarkovi,” True Energy-Performance Analysis of the MTJ-Based Logic-in-Memory Architecture ”,IEEE transactions on electron devices,vol:57,no:5, may 2010.
  12. Shivshankar Mishra, V. Narendar, Dr. R. A. Mishra, ” On the Design of High-Performance CMOS 1-Bit Full Adder Circuits”, International Conference on VLSI, Communication and Instrumentation,2011.
  13. Kalamani, K. Paramasivam “Survey of Low Power Testing Using Compression Techniques”, International Journal of Electronics and Communication Technology /Volume-4, Issue-4, Oct-Dec 2013.

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31.

Authors:

S. Tamil Elakkiya, K. Karthika, K. Jasmine

Paper Title:

RTOS based Online Condition Monitoring and Diagnosis of Electrical Equipment

Abstract: In an electrical system if the maintenance process carried over in scheduled manner there is a chance that would leads to unexpected failure before our next scheduled maintenance break. RTOS based Online condition monitoring techniques for electrical equipment mainly includes system status, system performance, system recovery and automatic diagnosis. If the electrical systems are more complex, its better to split into number of tasks. These tasks would have different priorities and timing deadline. By using priority scheduling algorithm the various tasks were managed. With the help of C language, the program has been written to assign the task priorities, timing deadline to monitor the tasks, and also recovery actions should be carried over if any fault occurs. This program has been flashed at ARM7 processor and based upon that the entire system works. This emulation supports online condition monitoring to monitor and diagnosis of electrical equipment in real time.

Keywords: RTOS, ARM7 processor, Matlab-Simulink, LPC2148 kit,Priority scheduling algorithm.

References:

  1. Rao and M. Chandorkar, “Rapid prototyping tool for electrical load emulation using power electronic converters,” in Industrial Electronics Applications, 2009. ISIEA 2009. IEEE Symposium on, vol. 1, 4-6 2009, pp. 106–111.
  2. http://www.linuxjournal.com/article/
  3. http://www.reliableembeddedsystems.com/pdfs/2010
  4. Zhang, L. Chen, and A. Yao, “Study and comparison of the rthal- based and adeos-based rtai real-time solutions for linux,” Computer and Computational Sciences, International Multi-Symposiums on, vol. 2, pp.771–775, 2006.
  5. Vun, H. Hor, and J. Chao, “Real-time enhancements for embedded linux,” Parallel and Distributed Systems, International Conference on, vol. 0, pp. 737–740, 2008.
  6. E. McKone, R. G. Schroeder, and K. O. Cua, “Impact of total productive maintenance practices on manufacturing performance,” Journal of Operations Management, vol. 19, no. 1, pp. 39–58, 2001.
  7. K. Pil and T. Fujimoto, “Lean and reflective production: the dynamic nature of production models,” International Journal of Production Research, vol. 45, no. 16, pp. 3741–3761, 2007.
  8. Viruthambal K., Arunkumar B, “RTOS Based Dynamic Scheduler in Power Quality Applications,” International Journal of Scientific Engineering and Technology (ISSN : 2277-1581),Volume No.2, Issue 6, pp : 554-559

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32.

Authors:

N. Ramsundram, Nisha Khanam

Paper Title:

Impact of Climate Change on Reservoir Inflow Predictions: A Case Study

Abstract: Hydrological cycle is inherent of climate processes with lot of interactions thereby making the system to be complex. The modeling of hydrological cycle to simulate the water resource has been well researched for more than a decade. In the recent past the studies are initiated to capture the influence of climate parameters on the hydrological cycle. One of the major inferences from the above is that,influence of climate parameters in arid and semiarid climatic region is not very significant. To understand the generalized behavior stated by the research community on arid regions, in this research paper we explored the inflow database of stanely reservoir, Tamilnadu. A modeling framework has been developed that predicts the reservoir inflow considering the future climatic scenarios. From the developed model,we inferred that the generalized stated on arid region valid only in case of regional / macro modeling, and it does not valid for specific case as micro climate variables influences the hydrological cycle.

Keywords: hydrological cycle, reservoir inflow, prediction, climate variables.

References:

  1. Arnell N W,(2004),” Climate change and global water resources: SRES emissions and socio-economic scenarios”, Global Environmental Change, 14(1), 31-52.
  2. Askew,(1987), “Climate change and water resources:, in: S.I. Solomon, M. Beran, W. Hogg (Eds.), The Influence of Climate Change and Climatic Variability on the Hydrologic Regime and Water Resources, vol. 168IAHS Publishers, 421–430.
  3. Chattopadhyay N, M. Hulme, (1997),” Evaporation and potential evapotranspiration in India under conditions of recent and future climate change:, Agricultural and Forest Meteorology, 87(1),55-73.
  4. Fischer, G., F.N. Tubiello, H. van Velthuizen, and D.A. Wiberg, (2007),”Climate change impacts on irrigation water requirements: Effects of mitigation, 1990-2080”. Technol. Forecasting Soc. Change, 74, 1083-1107.
  5. Leavesley G.H., (1994),”Modeling the effects of climate change on water resources–a review”, Climatic Change, 28, 159–177.
  6. Mall R.K, A. Gupta, R. Singh, R.S. Singh, L.S. Rathore, (2006),”Water resources and climate change: an Indian perspective”, Current Science, 90, 1610–1626.
  7. Mujumdar, P. P., and S. Ghosh (2008), Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India, Water Resour. Res., 44
  8. Mukerrji R, (2009), “Vulnerability and adaptation experiences from Rajasthan and Andhra Pradesh: water resource management”, SDC V&A Programme, India.
  9. Rodriguez Diaz J.A., E.K.Weatherhead., J.W.Knox and E.Camacho (2007), Climate Change impacts on irrigation water requirements in the Guadalquivir river basin in spain, Reg Environ Change, 7, 149–159.
  10. Samuel Hitz, Joel Smith, (2004),” Estimating global impacts from climate change”, Global Environmental Change, 14(3), 201-218.
  11. Sathaye, J and Shukla, PR and Ravindranath, NH, (2006) “Climate change, sustainable development and India: Global and national concerns”. In: Current Science, 90 (3). 314-325.

132-135

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33.

Authors:

V. Jeyasudha, Satheesh Kumar KRP

Paper Title:

Study and Comparison of Steel Haunched and Tapered Beam

Abstract: Steel framed buildings are flexible, ductile and light weight compared to that of reinforced concrete buildings. Intense research had been conducted in the last decades regardingthe fatigue and ductility behaviour of structural steel beam. Prismatic beams are the beams with uniform cross-section in the entire span of the beam. Non-prismatic beams are used to increase the efficiency of the beam, by increasing the strength per unit mass than that of prismatic beam. In this study, the load-deformation, stress-strain analysis, the fatigue and ductile behaviour of non-prismatic steel beams with hunched and tapered ends was compared with prismatic beam for different loading condition. The beams were subjected to static loading conditions during analysis.

Keywords: Prismatic beam, non-prismatic beam, stress-strain analysis, static loading, fatigue and ductile behavior.

References:

  1. NimbalkarAmol N. and Laxman V. Awadhani. Experimental and Numerical Analysis of Trapezoidal Corrugated Web Beam to Determine its Strength and Mode Shapes. International Engineering Research Journal, 1955-1961.
  2. Anu Jolly, VidyaVijayan 2. (2015). Structural Behaviour of Reinforced Concrete Haunched Beam A Study on ANSYS and ETABS. International Journal of Innovative Science, Engineering & Technology, 3(8), 495-500.
  3. Abinayaa, A., Ramadevi, K. (2018). Analytical investigation of all - Steel buckling restrained braces. International Journal of Civil Engineering and Technology, 9(3),232-239.
  4. Premalatha, J., Manju, R., Senthilkumar, V. (2017). Seismic response of multistoreyed steel frame with viscous fluid-scissor jack dampers. International Journal of Civil Engineering and Technology, 8(8),289-312.

136-138

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34.

Authors:

Senthil Kumar P, Sivakumar K, Kannan P. R.

Paper Title:

Fuzzy Sliding Mode Control for Active Vibration Control of Hydraulic Actuated Vehicle Suspension

Abstract: Fuzzy Sliding model control (FSMC) has been suggested to control hydraulic actuated nonlinear active suspension using full car model. The development of efficient classical controller for active suspension has been difficult due to complex nature of full car mathematical model, nonlinear effect in suspension and unpredictable behaviour of hydraulic actuator. The sliding mode controller (SMC) has an ability to handle uncertain parameter, nonlinearity and complex mathematical model of dynamic system. Chattering phenomenon remains to be the only obstacle for nonlinear sliding mode controller which can be eliminated by integrating fuzzy logic with sliding mode control. In this work, fuzzy sliding mode controller proposed for hydraulic actuated nonlinear active suspension to handle nonlinearity, uncertainty in parameter variation and chattering effect. The simulation is carried out using Seven degree of freedom based full car model to measure body displacement, acceleration, roll and pitch angle of vehicle. The response of full car model confirms the feasibility of fuzzy sliding mode controller for hydraulic actuated active suspension.

Keywords: Fuzzy sliding mode control, Vibration control, Active suspension, Actuator dynamics.

References:

  1. Hrovat D. “Optimal active suspension structures for quarter-car vehicle models”. Automatica. 1990; 26:845-60.
  2. Thompson AG, Davis BR. “Optimal linear active suspensions with derivative constraints and output feedback control”. Vehicle System Dynamics. 1988; 17:179-92.
  3. Thompson A. “An active suspension with optimal linear state feedback. Vehicle System Dynamics”. 1976; 5(4):187-203.
  4. Wilson DA, Sharp RS, Hassan SA. “The application of linear optimal control theory to the design of active automotive suspensions. Vehicle System Dynamics”. 1986; 15(2):105-18.
  5. Hrovat D. “Applications of optimal control to advanced automotive suspension design”. Journal of Dynamic Systems Measurement and Control. 1993; 115: 328.
  6. Alleyne AG, Liu R. “Systematic control of a class of nonlinear systems with application to electrohydraulic cylinder pressure control”. IEEE Transactions on Control Systems Technology. 2000; 8: 623-34.
  7. Yagiz N, Hacioglu Y, Taskin Y. “Fuzzy sliding-mode control of active suspensions”. IEEE Transactions on Industrial Electronics. 2008; 55: 3883-90.
  8. Sun W, Gao H, Yao B. “Adaptive robust vibration control of full-car active suspensions with electrohydraulic actuators”. IEEE Transactions on Control Systems Technology. 2013; 21: 2417-22.
  9. Gohrle C, Schindler A, Wagner A, et al. “Design and vehicle implementation of preview active suspension controllers”. IEEE Transactions on Control Systems Technology. 2014; 22: 1135-42.
  10. Utkin V. “Variable structure systems with sliding modes”. IEEE Transactions on Automatic Control. 1977; 22(2):212-22.
  11. Ertugrul M, Kaynak O, Sabanovic A. “A comparison of various VSS techniques on the control of automated guided vehicles”. IEEE Transactions on Industrial Electronics, 1995; 2: 837-842.
  12. Jafarov EM, Tasaltin RA. “Robust sliding-mode control for the uncertain MIMO aircraft model F-18”. IEEE Transactions on Aerospace and Electronic Systems. 2000; 36(4):1127-41.
  13. Kim C, Ro PI. A sliding mode controller for vehicle active suspension systems with non-linearities. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 1998; 212(2):79-92.
  14. Sam YM, Osman JH, Ghani MR. “A class of proportional-integral sliding mode control with application to active suspension system”. Systems & Control Letters. 2004; 51(3):217-23.
  15. Yagiz N, Yuksek I, Sivrioglu S. “Robust control of active suspensions for a full vehicle model using sliding mode control”. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing. 2000; 43(2):253-8.
  16. Zadeh LA. “Fuzzy sets”. Information and Control. 1965; 8:338-53.
  17. Sathishkumar M, Sakthivel R, Kwon OM, Kaviarasan B. “Finite-time mixed H∞ and passive filtering for Takagi–Sugeno fuzzy nonhomogeneous Markovian jump systems”. International Journal of Systems Science. 2017; 48(7):1416-27.
  18. Sakthivel R, Sathishkumar M, Mathiyalagan K, Anthoni SM. “Robust reliable dissipative filtering for Markovian jump nonlinear systems with uncertainties”. International Journal of Adaptive Control and Signal Processing. 2016.
  19. Sakthivel R, Sathishkumar M, Ren Y, Kwon OM. “Fault-tolerant sampled-data control of singular networked cascade control systems”. International Journal of Systems Science. 2017: 1-12.
  20. Pan H, Sun W, Gao H, Jing X. “Disturbance observer-based adaptive tracking control with actuator saturation and its application”. IEEE Transactions on Automation Science and Engineering. 2016; 13(2):868-75.
  21. Pan H, Sun W, Gao H, Jing X. “Disturbance observer-based adaptive tracking control with actuator saturation and its application”. IEEE Transactions on Automation Science and Engineering. 2016; 13: 868-75.
  22. Sun W, Pan H, Gao H. “Filter-based adaptive vibration control for active vehicle suspensions with electrohydraulic actuators”. IEEE Transactions on Vehicular Technology. 2016; 65(6):4619-26.
  23. Lin J, Lian RJ, Huang CN, et al. “Enhanced fuzzy sliding mode controller for active suspension systems”. Mechatronics. 2009; 19: 1178-90.

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35.

Authors:

V. Chelladurai, P. Karthikeyan, S. Thangamani

Paper Title:

Urbanization Effects on the Surface Water Resources and Land Use in Udumal Petregion using RS & GIS

Abstract: The continuous increase in population and the urbanization resulted in over exploitation of natural resources which directly affects the land use pattern and use of water. A study was framed to assess the changes in the land and water resources management in the Udumalpet taluk region during 1991-2009. The changes in land use pattern, growth and reduction in surface water body area in the region was analysed using ArcGIS 9.3 software.The land use pattern analysis results showed that the cultivable land and build-up land level increased during the study period (1991-2009), and a trend of decrease in water holding structures and uncultivable land area. The growth of area with buildings was 82.64%, in this region and in the urban area switch of land to buildings was 2.16 times of overall build up area increase. The area of surface water bodies at the urban and rural areas were declined by 7.31% and 4.78%, respectively during this study period (1991-2009).The portable water demand forecast analysis showed that the portable water requirement in 2041 would be 2.31 and 1.63 times higher than the portable water requirement in 2011 at the urban, and rural areas, respectively.

Keywords: Land use pattern change, land cover change, Urbanization, portable water requirement

References:

  1. FAO, 2007. Coping with water scarcity - Challenge of the twenty-first century, World water day issue.
  2. Census of India. 2011. Rural-Urban Distribution of Population, Provisional Population Totals, Government of India Publications, New Delhi.
  3. Montgomery, M.A., and M. Elimelech. 2007. Water and sanitation in developing countries: including health in the equation. Environmental Science & Technology. 1: 12–24.
  4. USEPA, 2008. Environment Protection Agency (EPA) Report on Environment. United State Environment Protection Agency.
  5. Vink, A.P.A., 1983. Landscape Ecology and Land Use. Longman, New York. 171.
  6. Chowdhury, R. K. and R. Rahman, 2008. Multi-criteria decision analysis in water resources management: The Malnichara channel improvement. International Journal of Environmental Science and Technology, 5(2): 195-204.
  7. Chaudhary, B.S., 2003. Integrated Land and Water Resources Management in Southern Part of Haryana using Remote Sensing and Geographical Information Systems (GIS).D. Thesis, University of Rajasthan, Jaipur.
  8. Latha, J.C., S. Saravanan and K. Palanichamy, A Semi – Distributed Water Balance Model for Amaravathi River Basin using Remote Sensing and GIS. International Journal of Geomatics and Geosciences, 1(2): 252-263.
  9. Kandasamy, P. and M. Chellamuthu, 2012. Dry Spell Analysis for Water management Planning. International Journal of Applied Science and Engineering Research, 1(1): 127-137.
  10. Sundarakumar, K., M. Harika, S.K. Aspiya Begum, S. Yamini and K. Balakrishna, Land use and land cover change detection and urban sprawl analysis of Vijayawada city using multi-temporal Landsat data. International Journal of Engineering Science and Technology, 4(1): 170-178.
  11. Tan, M.L., H. Xie and C. Lu, 2005. Urban land expansion and arable land loss in China - A case study of Beijing-Tianjin-Hebei region. Land Use Policy, 22: 187–196.
  12. Cetin, M. 2009. A satellite-based assessment of the impact of urban expansion around a lagoon. International Journal of Environmental Science and Technology, 6(4): 579-590.
  13. Ningrui, D., H. Ottens and R. Sliuzas, 2010. Spatial impact of urban expansion on surface water bodies - A case study of Wuhan, China. Landscape and Urban Planning, 94: 175–185.
  14. Yin, J., Z. Yin, H. Zhong, S. Xu, X. Hu, J. Wang and J. Wu, 2011. Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environ Monit Assess, 177:609–621.

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36.

Authors:

G. M. Tamilselvan, V. Ashishkumar, S. Jothi Prasath, S. Mohammed Yusuff

Paper Title:

IoT Based Automated Water Distribution System with Water Theft Control and Water Purchasing System

Abstract: The expanded living spaces in provincial and urban territory require great quality water dissemination administration framework. In this way, there is a basic prerequisite to outline a programmed water supply framework to accomplish rise to measure of water conveyance to every one of the natural surroundings. Internet of things, another idea is employed as a part of the planned framework for programmed water circulation and fault identification. The key idea of this paper is to plan a cost proficient framework to accomplish better water supply by regular supervising and furthermore controlling it from a central server to eliminate problems in the supply of water to the habitats. The proposed design utilizes an Arduino as minicomputer, water flow sensor, and solenoid valves. Arduino is utilized to control the valve and flow meter. The purchase of water should be possible by utilizing cayenne application in which the requisite of water for any of the habitats can be fixed. To associate the Arduino board with the internet the Arduino Ethernet Shield V1 is utilized. It depends on the Wiz net W5100 Ethernet chip (datasheet). The Wiz net W5100 gives a system (IP) stack fit for dealing with both TCP and UDP packets. The proposed design takes care of the issue of overflow, over utilization, acquiring of water and makes an appropriate distribution.

Keywords: Arduino, Distribution, Monitoring, Purchasing of Water, Solenoid Valves, Water Flow Sensor, Water Supply.

References:

  1. V. Ebere , Oladipo Onaolapo Francisca , “Microcontroller based automated water level control system”, International Journal of Innovative Research in Computer and Communication Engineering, Vol.1, No. 6. 2013, pp.1-6.
  2. Gaikwad Sonali Ashok, “Water antitheft and quality monitoring system by using PLC and SCADA”, International journal of electrical and electronic engineering research, ISSN 2250-155X. Vol.3, No.2, June 2013, pp.355-364.
  3. Hassaan Th.H.Thabet, “Design and implementation of a Pi Controller for an automated building water supply system using PLC techniques,” Journal of Theoretical and Applied Information Technology, Vol. 5,May 2011.
  4. MukeshAravind, S.Sukhumar, S.Karthik, “PLC based automatic corporation water distribution system using solar energy”, International Journal of Engineering Research and Technology (IJERT), Vol.2, No.12, December 2013.pp.3100-3105.
  5. J. Whittle, M. Allen, A. Preis and M. Iqbal “Sensor networks for monitoring and control of water distribution system” The 6th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Hong Kong 9-11 December 2013, Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, Vol. 6, No.1, pp. 4-23.
  6. Sagar Khole, Tushar Kople and A.P More, “Automated Drinking water supply system and theft identification using Embedded technology”, International Journal of Innovative Research in Computer and Communication Engineering,Vol.3, 2015,pp.1-6.
  7. Ahmad T. Jaiad, and Hamzah Sabr Ghayyib, “Controlling and Monitoring of Automation of Water Supply system based on IOT with theft identification”, International journal of research –GRANTHAALAYAH, Vol.5, No.5, 2017.

151-156

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37.

Authors:

J. Rajasingh, S. Sivasakthi, M. Thirumalaimuthukumaran

Paper Title:

Existence of Solution of Hypothalamo- Pituitary -Adrenocortical Mathematical Model

Abstract: Homotopy analysis method is attempted to evaluate the hypothalamo- pituitary - adrenocortical mathematical model. The effects of hypothalamo- pituitary adrenocortical,corticotrophin releasing hormone, denocorticotropin, are discussed in this work.
AMS Subject Classiftcation: 65L11, 65L99, 92C50

Keywords: hypothalamo- pituitary -adrenocortical, corticotrophin, adenocorticotropin.

References:

  1. Tsigos C, Chrousos GP, Hypothalamic-pituitary-adrenal axis, neuroendocrine factors and stress. J Psychosom Res ; 53(2002): 865-71.
  2. Paunovic VR, Babinski T. Biological psychiatry 1: The molec- ular basis of mental processes. Faculty of Medicine, University of Belgrade, Belgrade,Serbian, (1995).
  3. Gonzalez-Heydrich J, Steingard RJ, Kohane I. A computer simulation of the hypothalamo-pituitary-adrenal axis. In: Eighteenth annual proceedings of the symposium for computer applications in medical care (SCAMC), (1994)
  4. Lenbury Y, Pacheenburawana Modelling fluctuation phe- nomena in the plasma cortisol secretion system in normal man. Biosystems; 26(1991):117-25.
  5. .Londergan CH, Peacock-Lopez E. Dynamic model of hor- monal systems coupled by negative feedback. Chem. (1998);73 :85-107
  6. .Mehdi Ganjiani Hossein Ganjiani, Solution of coupled sys- tem of nonlinear differential equations using homotopy analysis method. Nonlinear Dyn. 56 (2009): 159-167.
  7. Veldhuis J D, Iranmanesh A, Naftolowitz D, Tatham N, Cas- sidy F, Carroll BJ. Corticotropin secretary dynamics in hu- mans under low glucocorticoid feedback. Clin. Endocrinol Metab ;86 (2001): 5554-63.
  8. Brown EN, Meehan PM, Dempster A stochastic differen- tial equation model of diurnal cortisol patterns. Am. J. Physiol Endocrinol Metab ;280 (2001): E 450-61

157-161

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38.

Authors:

J.Rajasingh, R.Murugesu, S.John Alexis

Paper Title:

Existence of Solution of Forest Cross-Diffusion Model

Abstract: Homotopy perturbation method is attempted to evaluate the cross-diffusion forest boundary dynamics. The effects of exothermic and endothermic parameters are are discussed. AMS Subject Classiftcation: 37N25, 35Q92, 92D40, 92D25, 35B20, 37N25.

Keywords: Homotopy perturbation method, forest boundary dynamics.

References:

  1. M.Y. Antonovsky, A. Aponia, Y.A. Kuznetsov, Spatial- temporal structure of mixed- age forest boundary: The sim- plest mathematical model, International institute for Applied Systems Analysis (Laxenburg, Austria,1989), pp. WP 89-54.
  2. Y. Antonovsky, W. Clark, Y.A. Kuznetsov, The influence of pests on forest age structure dynamics: The simplest math- ematical models, International institute for Applied Systems Analysis (Laxenburg, Austria, 1987) , pp. WP-87-70.
  3. Y. Antonovsky, R. Fleming, Y.A. Kuznetsov, W. Clark, Forest pest interaction dynamics: The simplest mathematical models, Theor.Popul.Biol. 37 (1990) 343-357.
  4. Y. Antonovsky, M.D. Korzukhin, Mathematical modelling of economic and ecological -economic processes.Integrated Global Monitaring of Environmental pollution, Proc.2nd Inter- national Symp., (Leningrad:Hydromet,Tbilisi, USSR, 1981), pp. 353-358.
  5. Arslanturk, Performance analysis and optimization of radi- ating fins with a step change in thickness and variable thermal conductivity by Homotopy Perturbation Method, Heat Mass Transfer 47(2011) 131-138.
  6. S.H. Chowdhury, I. Hashim, Analytical solutions to heat transfer equations by homotopy perturbation method, Phys. Lett. A 372(2008) 1240-1243.
  7. H. Chuan, Tohru Tsujikawa, Atsushi Yagi,Statinary Solu- tions to Forest Kinematic Model, Glasgow Math. J 51 (2009)1- 17.
  8. S.H. Cowdhury, I. Hashim, O. Abdulaziz, Comparison of Homotopy Analysis Method And Homotopy-Perturbation Method for purely nonlinear fin-type problems, Commun Non- linear Sci. Numer. Simul. 14(2009) 371-378.
  9. Gianluca Mola, Atsushi Yagi, A Forest model with Memory,
  10. Funcialaj Ekvacioj 52 (2009) 19-40.
  11. H. He,Homotopy perturbation technique,Comput. Methods Applied Mech. Eng., 178 (1999)257-262.
  12. A. Kuznetsov, M.Y. Antonovsky, V.N. Biktashev and A. Aponina, A cross-diffusion model of forest boundary dynamics, J Math. Biol. 32 (1994) 219-232.
  13. Quanqua, Qualitative Analysis for a cross diffusion model of forest, Science in China(series A), 41(1998) (6).
  14. P. Wu, Stability of Travelling Waves for a Cross-Diffusion Model, Journal Of Mathematical Analysis And Applications 215 (1997) 388-414.

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39.

Authors:

S.P.Siddique Ibrahim, M. Sivabalakrishnan, S.P. Syed Ibrahim

Paper Title:

Lazy Learning Associative Classification in Map Reduce Framework

Abstract: he core objective of the work is to propose a distributed environment based lazy learning Associative Classification (AC). Associative Classification is a hybrid version of data mining tasks which integrated both Association Rule Mining (ARM) and Classification technique to construct accurate classifier. Unfortunately, the AC used for learning these classifier are less popular in real time for building application due to its higher computation time complexity and memory constraints in large volume of datasets. Moreover, single processor’s CPU resources and memory are limited, which makes the algorithm incompetent to handle such datasets. To overcome such downsides, we proposed a distributed and parallel computing for lazy learning associative classification for accelerating algorithm performance by projecting the testing instances with large training datasets. In this work, we have implemented MapReduce based algorithms which reduce the computation by eliminates the need of constructing generalized classifier. It also well handled rare rules and generated institutive rules. The proposed algorithm may be suitable in area such as network intrusion detection, fraud detection, crowd analysis, rare disease prediction and crime analysis. Our algorithm has been compared with well known existing algorithms in relations of precision and running time. The experiments result has strengthened the proposed algorithm well handle the rare rules in distributed environment and is making better performance even the size of the datasets is huge.

Keywords: Association Rule Mining, Rare rules, Lazy Learning, Associative Classification.

References:

  1. Agrawal R. and Srikant R. “Fast algorithms for mining association rule” Proceedings of the twentieth International conference on very large databases. 1994, pp. 487-499.
  2. Liu and Y. Ma, “Mining association rules with multiple minimum supports,” in Proc. fifth ACM International Conf. Knowledge discovery data mining, 1999, pp. 337–341.
  3. Apache Drill, http://drill.apache.org May 2015).
  4. Apache Spark, https://spark.apache.org (Accessed: May 2015).
  5. Apache Storm, https://storm.apache.org (Accessed: May 2015).
  6. I. A. Ajlouni, W. Hadi, J. Alwedyan, Detecting phishing websites using associative classification, European Journal of Business and Management 5 (15) 2013, pp. 36-40.
  7. Abdelhamid A, Ayesh .F “A Multi class Associative Clasfication algorithm” Information Knowledge Management, 2012.
  8. Baralis, P. Garza, “ A Lazy approach to pruning classification rules” Proceedings of IEEE Internaltional conference on DataMining, 2012, pp. 35-42.
  9. Liu, W.Hsu, et.al., “Integrating classification and association rule mining”, In proc. of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998, pp. 80-86.
  10. E, Chiusano. S, A Lazy Approach to Associative Classification, IEEE Transactions on Knowledge and Data Engineering, v.20 n.2, p.156-171, February, 2008.
  11. J, “Induction of decision trees”. Machine Learning, pp, 81–106, 1986.
  12. J, and Pei. J, “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules,” Proc. of IEEE International Conference on Data Mining (ICDM ’01), Nov. 2001.
  13. [Guoqing Chen, et.al., “A new approach to classification based on association rule mining”, Science Direct, Decision Support Systems 42 ,2006, pp. 674– 689.
  14. Dean, S. et.al., “A MapReduce: A flexible data processing tool”, Comm. ACM 53 (1), 2010, pp.72–77.
  15. Apache Spark: October 2015. (Https:// spark.apache.org)
  16. Neumeyar, B.Robbings, “ Distributed stream computing platform” Proc. of IEEE Int. conference on data mining workshop, 2010, pp. 170-177.
  17. [Apache Storm: October 2015. (https:// storm.apache.org)
  18. Melnik, A.Gubarev, “ Interactive analysis of web-scale datasets”, Proc. VLDBndow, 2010, pp. 330-339.
  19. Apache Drill: October 2015. (http:// drill.apache.org)
  20. :October 2015(http://hunch.net/~vw/).
  21. Bifet,G.Holmes, “Massive online analysis”, 2010, pp. 1601-1604.
  22. Wu, X., al. 2007.Top 10 algorithms in data mining. Knowledge Information System, 2008, pp. 1-37.
  23. Cendrowska J. “An Algorithm for inducing modular rules” Internal. Jour. of Man-Machine Studies. Vol.27, 1987, 349-370.
  24. Syed Ibrahim. S.P, Nataraj R.V., “LLAC: Lazy Learning in Associative Classification” in the Springer Lecture Series (CCIS) Part I, 2011, PP. 631 – 638.
  25. UCI Machine Learning Repository: Data Sets [Online] (2010). Available: http://archive.ics.uci.edu/ml/datasets.html.

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40.

Authors:

M.N. Saroja, S. Kannan, K.R. Baskaran

Paper Title:

Analysing the Purchase Behavior of a Customer for Improving the Sales of a Product

Abstract: Modern techniques such as predictive analytics have gained a lot of research attraction these days. In the competitive world, it is important for a business people to predict the pulse of customer to shine. With predictive analytics, it is possible to see what a customer will buy next. The goal is to increase the profit earned by a company. In this paper, various hypothesis tests have been conducted for analysing the purchases of a customer. Initially, the purchases have been analysed by grouping the purchases gender wise and by analysing what group of people buy more products. It also finds out which group prefers for promotion codes and discounts and for what type of products they preferred more. In which store, the sales of products are more and in which state, the sales are maximum. Based on this, techniques for improving the sales of a product is suggested.

Keywords: Data Analytics, Purchasing behaviour, product recommendation, Predictive Analytics, Data mining

References:

  1. Mahsa Familmaleki, Alireza Aghighi and Kambiz Hamidi, “Analyzing the Influence of Sales Promotion on Customer Purchasing Behavior”, International Journal of Economics & Management Sciences, Volume 4, 2015
  2. Aurangzeb Mughal, Asif Mehmood, Ammar Mohi-ud-deen, Bilal Ahmad, “The Impact of Promotional Tools on Consumer Buying Behavior: A Study from Pakistan” , Journal of Public Administration and Governance, Volume 4, 2014
  3. V.Sangvikor and Hemant J.Katole, “A study of consumer purchase behaviour in organized retail outlets” Journal of Business and Retail Management Research, Volume 7, Issue 1, October 2012
  4. https://www.rstudio.com
  5. AshminKaul, MansiVirani, ChaitanyaKaul and TejaGummalla “Evaluating Techniques for Mining Customer Purchase Behavior and Product Recommendation” International Journal of Computer Applications (0975 – 8887) Volume 126 – No.5, September 2015.
  6. Bagozzi, R., Gurhan-Canli, Z., Priester, J., “The Social Psychology Of Consumer Behaviour”, Open University Press, Buckingham, PA, 2002, pp 60-63.
  7. Belch G, Belch, M.A Kerr G and Powell I “Advertising and Promotion Management” An Integrated Marketing Communication Perspective, McGraw-Hill, Sydney, Australia, 2009, p.13.
  8. R.Baskaran, C. Kalaiarasan, “Improved Performance By Combining Web Pre-Fetching Using Clustering With Web Caching Based On Svm Machine Learning Method”, International Journal of Computers Communications & Control, ISSN 1841-9836,Vol.11, No.2, April 2016, pp. 166-177
  9. R. Baskaran, C. Kalaiarasan, “Pre-Eminence Of Combined Web Pre-Fetching And Web Caching Based On Machine Learning Technique”, Arabian Journal For Science And Engineering
  10. (Springer Journal), ISSN 1319-8025, Vol. 39, No.11, November 2014, 7895-7906

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41.

Authors:

X.Francis Jency, V.P.Sumathi, Janani Shiva Sri

Paper Title:

An Exploratory Data Analysis for Loan Prediction Based on Nature of the Clients

Abstract: In India, the number of people applying for the loans gets increased for various reasons in recent years. The bank employees are not able to analyse or predict whether the customer can payback the amount or not (good customer or bad customer) for the given interest rate. The aim of this paper is to find the nature of the client applying for the personal loan. An exploratory data analysis technique is used to deal with this problem. The result of the analysis shows that short term loans are preferred by majority of the clients and the clients majorly apply loans for debt consolidation. The results are shown in graphs that helps the bankers to understand the client’s behaviour.

Keywords: Loan analysis, exploratory data analysis technique, client’s analysis, financial categories analysis

References:

  1. Goyal and R. Kaur, “A survey on Ensemble Model for Loan Prediction”, International Journal of Engineering Trends and Applications (IJETA), vol. 3(1), pp. 32-37, 2016.
  2. J. Hamid and T. M. Ahmed, “Developing Prediction Model of Loan Risk in Banks using Data Mining”.
  3. Shaath, “Credit Risk Analysis and Prediction Modelling of Bank Loans Using R”.
  4. Goyal and R. Kaur, “Accuracy Prediction for Loan Risk Using Machine Learning Models”.
  5. Sudhakar, and C.V.K. Reddy, “Two Step Credit Risk Assessment Model for Retail Bank Loan Applications Using Decision Tree Data Mining Technique”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), vol. 5(3), pp. 705-718, 2016.
  6. Gerritsen, R. (1999). Assessing loan risks: a data mining case study. IT professional, 1(6), 16-21.
  7. Hsieh, N. C., & Hung, L. P. (2010). A data driven ensemble classifier for credit scoring analysis. Expert systems with Applications, 37(1), 534-545.
  8. https://en.wikipedia.org/wiki/Exploratory_data_analysis
  9. https://pandas.pydata.org/pandas-docs/stable/
  10. https://www.experian.com/blogs/ask-experian/credit-education/score-basics/what-is-a-good-credit-score/

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42.

Authors:

P. Parameswari, C.Ramachandran, R. Rassika

Paper Title:

Heart Disease Prediction System using Enhanced Apriori

Abstract: Heart disease is frightening the people around the world and in some countries it is the number one disease which leads to death. Biomedical research efforts help to prevent and treat heart disease in a better way. Handling large amount of data is often very tedious with traditional methods which lead into problems, particularly in high level of complexity and vagueness factors. Mining frequent patterns from large databases has emerged as an important area in data mining research and knowledge discovery community; this also contributes so much to health care domain. This heart prediction system helps to predict heart related problems at an early stage. The proposed system predicts heart related issues of a person based on questions and the answers given to the prediction system. To have better results in minimum time duration an Enhanced Apriori algorithm was introduced which is an improvement of Apriori algorithm. The experimental results proved that the proposed approach performs faster and memory efficient with more number of patterns. It was also proved that the prediction rate of Enhanced Apriori was also good (94%) than Apriori (87%).

Keywords: Prediction, Data Mining, Heart Disease, Apriori, Association Rule Mining

References:

  1. Wilson, F. N. 1948. The clinical value of chest leads. British Heart Journal 10 (2): 88–91.
  2. Parameswari, P, Abdul Samath, J & Saranya, S 2015, Efficient birch clustering algorithm for categorical and numerical data using modified co-occurrence method, International Journal of Applied Engineering
  3. Research, vol. 10, no. 11, pp. 27661-27673 .
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  5. G. and H. Blackburn. 1968. Cardiovascular survey methods: WHO technical report series no. 56. Geneva: World Health Organization..
  6. Aribarg, T., Supratid, S., Lursinsap, C, 2012. Optimizing the modified fuzzy ant-miner for efficient medical diagnosis. Applied Intelligence ;37(3):357–376.
  7. Silverstein, C., Brin, S., Motwani, R. and Ullman, J, 1998. Scalable Techniques for Mining Causal Structures, Technical Report, Department of Computer Science, Stanford University.
  8. Luepker, R. V., A. Evans, P. McKeigue, and K. S. Reddy, 2004. Cardiovascular Survey Methods. 3rd edition Geneva: World Health Organization.
  9. Blair S. N., J. B. Kampert, H. W. Kohl, C. E. Barlow, C. A. Macera, R. S. Paffenbarger, and L. W. Gibbons, 1996. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. Journal of the American Medical Association 276: 205-210.
  10. Rakesh Agrawal and Ramakrishnan Srikant , 1994. Fast algorithms for mining association rules in large databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September.

180-182

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43.

Authors:

R.K. Kavitha, W. Jaisingh

Paper Title:

A Study on the Student Experiences in Blended Learning Environments

Abstract: In recent times, teaching and learning methods have a direct impact on students' learning experiences. Blended learning is a combination of face-to-face and online delivery methods which influences students' perceptions on the learning environments to a great extent. Learning analytics is a growing trend at all levels of education. The objective of the paper is to examine the student’s experiences in blended learning environments. Relevant data has been collected from undergraduate and postgraduate students who are exposed to a blended learning environment while learning programming subjects. Learning analytics has been applied on the collected data. It can be inferred from the results that the blended learning approach is more beneficial for students who are skilled in using certain computer programs and applications. The study results also provide new insights into the student preferences for learning in such knowledge sharing collaborative environments.

Keywords: Blended learning, Learning analytics, Collaboration, Knowledge sharing.

References:

  1. Kintu, M. J., & Zhu, C. (2016). Student characteristics and learning outcomes in a blended learning environment intervention in a Ugandan University. Electronic Journal of e-Learning, 14(3), 181–195.
  2. Chen, C.C., & Jones, K.T. (2007). Blended learning vs. traditional classroom settings: Assessing effectiveness and student perceptions in an mba accounting course. The Journal of Educators Online, 4(1), 1-15.
  3. Akkoyunlu, B., & Soylu, M.Y. (2008). A study of student’s perceptions in a blended learning environment based on different learning styles. Educational Technology & Society, 11(1), 183-193.
  4. Chandra, V., & Fisher, D.L. (2009). Students’ perceptions of a blended web-based learning environment. Learning Environment Research, 12, 31-44.
  5. Donnely, R. (2010). Harmonizing technology with interaction in blended problem-based learning. Computers & Education, 54(2), 350-359.
  6. López-Pérez, M., Pérez-López, M. C., & Rodríguez-Ariza, L. (2011). Blended learning in higher education: Students’ perceptions and their relation to outcomes. Computers & Education, 56(3), 818-826.
  7. Yeh, Y. C., Huang, L. Y., & Yeh, Y. L. (2011). Knowledge management in blended learning: Effects on professional development in creativity instruction. Computers & Education, 56(1), 146-156.
  8. Goyal, E., & Tambe, S. (2015). Effectiveness of Moodle-enabled blended learning in private Indian Business School teaching NICHE programs. The Online Journal of New Horizons in Education, 5(2), 14–22.
  9. Islam, A. K. M. N. (2014). Sources of satisfaction and dissatisfaction with a learning management system in
  10. post-adoption stage: A critical incident technique approach. Computers in Human Behaviour, 30, 249–261.
  11. Kwak, D. W., Menezes, F. M., & Sherwood, C. (2013). Assessing the impact of blended learning on student performance. Educational Technology & Society, 15(1), 127–136.
  12. Kavitha,R.K., Jalaja Jayalakshmi, V., Rassika, R., (2018). Collaborative learning in Computer Programming Courses using E-Learning Environments. International Journal of Pure and Applied Mathematics, Volume 118 No. 8 2018, 183-189
  13. Sarka Hubackova., Ilona Semradova., (2016). Evaluation of Blended Learning. Procedia - Social and Behavioral Sciences 217, 551 – 557.
  14. M.Manikantan, Mr.R.Lakshmana Kumar, Ms.Amala Jayanthi.M., (2017). Improvising the Web Search Results Using Enhanced Lingo Algorithm in Big Data Analysis for Health care, Journal of Advanced Research in Dynamical and Control Systems Vol. 9. Sp– 14 / 2017.
  15. Parameswari, P, Abdul Samath, J, Saranya, S (2015), ‘Efficient birch clustering algorithm for categorical and numerical data using modified co-occurrence method’, International Journal of Applied Engineering Research, vol. 10, no. 11, pp. 27661-27673 .

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44.

Authors:

Gunasekaran M, Gopalakrishnan B, Manikantan. M

Paper Title:

EMDAI: An Emergency Message Diffusion for Accident Information in Vehicular Ad Hoc Networks

Abstract: A Vehicular Ad Hoc Networks (VANETs) brings the breakthrough in industries. In highways the road safety and travel comfort are accomplished through vehicle-to-vehicle and vehicle-to-roadside unit communications. A major issue in safety application is to diffuse emergency message to all the other vehicles immediately without redundancy. There are number of works have been proposed to disseminate the emergency message, but those works suffer from delay and bandwidth consumption due to redundancy. This paper considers vehicle-to-vehicle communication for emergency message diffusion without the assistance of roadside units. In this paper, an Emergency Message Diffusion for Accident Information (EMDAI) approach is proposed for the efficient communication of emergency message. The EMDAI approach ensures the broadcasting of Short Range Message (SRM) and Long Range Message (LRM) to all the vehicles with minimum delay. A Non-Redundant Acyclic Group (NAG) technique is introduced to form a group to avoid the broadcast storm problem. In addition, the proposed EMDAI approach provides assistance to the ambulance to reach the Point of Incidence (PoI) in a short span of time. The performance analysis is done on reliability, channel occupancy, delay, message transmission per involved vehicle and traffic clearance delay. The results prove that the proposed EMDAI approach outperforms the existing protocol.

Keywords: Vehicular Ad Hoc Networks, Emergency Message Diffusion, Accident Information, Ambulance Assistance and Broadcast Storm Problem.

References:

  1. Chen, R., Jin, W-L., Regan, A. (2010). Broadcasting safety information in vehicular networks: Issues and Approaches, IEEE Network, vol. 24, no. 1, pp. 20-25.
  2. Rahman, S, A., Mourad, A., Barachi, M, E., Orabi, W, A. (2018). A novel on-demand vehicular sensing framework for traffic condition monitoring, Vehicular communications, Elsevier, vol. 12, pp. 165-178.
  3. Chou, Y-H., Chu, T-H., Kuo, S-Y., Chen, C-Y. (2018). An adaptive emergency broadcast strategy for vehicular ad hoc networks, IEEE Sensor Journal, vol. 18, no. 12, pp. 4814-4821.
  4. Panichpapiboon, S., Pattara-Atikom, W. (2012). A review of information dissemination protocols for vehicular ad hoc network, IEEE Communications Surveys & Tutorials, vol. 14, no. 3, pp. 784-798.
  5. Tseng, YC., Ni, SY., Chen, YS., Sheu, JP. (2002). The broadcast storm problem in a mobile ad hoc network, Wireless Networks, vol. 8, no. 2/3, pp. 153-167.
  6. Ghodrati, AD. (2013). Reduces broadcast storm using clustering in VANTEs, International Research Journal of Applied and Basic Sciences, vol. 7, no. 13, pp. 979-987.
  7. Huang, J., Huang, Y., Wang, J. (2014). Vehicle density based forwarding protocol for safety message broadcast in VANET, The Scientific World Journal, vol. 2014, pp. 1-9.
  8. Sun, MT., Feng, WC., Lai, TH., Yamada, K., Okada, H. (2000). GPS-based message broadcast for adaptive inter-vehicle communications, in of the 52nd Vehicular Technology Conference, pp. 2685-2692.
  9. Cha, SH. (2014). A Survey of Broadcast Protocols for Vehicular Ad-hoc Networks, Smart Computing Review, vol. 4, no. 4, pp. 246-255.
  10. Dedicated short range communications (DSRC), http://www.leearmstrong.com/DSRC/ htm.
  11. Wu, J., Li, H. (2001). A Dominating-set-based routing scheme in Ad Hoc Wireless Networks, Telecommunications Systems, vol. 18, no.1-3, pp. 13-36.
  12. Korkmaz, G., Ekici, E., Ozguner, F., Ozguner, U. (2004). Urban multihop boradcat protocol for inter-vehicle communication systems, Proceedings of the 1st ACM International Workshop on vehicular ad hoc networks, pp.76-85.
  13. Osafune, T., Lin, L., Lenardi M. (2006). Multi-hop vehicular broadcast, 6th International conference on ITS Telecommunications Proceedings, Chengdu, pp. 757-760.
  14. Yu, S., Cho, G. (2006). A selective flooding method for propagating emergency messages in vehicle safety communications, in of International Conference on Hybrid Information Technology, pp. 556-561.
  15. Vegni, AM., Stramacci, A., Natalizio, E. (2012). SRB: A selective reliable broadcast protocol for safety applications in VANET, in of International Conference on Selected Topics in Mobile & Wireless Networking.
  16. Muthamizh, B., Sathya, SS., Chitra, M. (2014). Spanning tree based broadcasting for VANET, International Journal of P2P Network Trends and Technology, vol. 7, pp. 21-25.
  17. Nakorn, NN., Rojviboonchai, K. (2010). DECA: Density-aware reliable broadcasting in vehicular ad hoc networks, in of the International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology, pp. 598-602.
  18. Wu, D., Zhang, Y., Bao, L., Regan, AC. (2013). Location-based crowd sourcing for vehicular communications in hybrid networks, IEEE Transactions on Intelligent Transportation systems, vol. 14, no. 2, pp. 837-846.
  19. Wu, Q., Domingo-FERRER J., Gonzalez, U. (2010). Balanced trustworthiness, safety and privacy in vehicle-to-vehicle communication, IEEE Transactions on vehicular technology, vol.59, no. 2, pp. 559-573.
  20. Lytrivis, P., Thomaidis, G., Tsogis, M., Ambitis, A. (2011). An advanced cooperative path prediction algorithm for safety applications in vehicular networks, IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 669-679.
  21. Javier, F., Ruiz, PM., Stojmenovic, I. (2012). Acknowledgment-based broadcast protocol for reliable and efficient data dissemination in vehicular ad hoc networks, IEEE Transactions on Mobile Computing, vol.11, no.1, pp.33-46.
  22. Jeong, J., Guo, S., Gu, Y., He, T., Du, DHC. (2011). Trajectory-based data forwarding for light-traffic vehicular ad hoc networks, IEEE Transactions Parallel and Distributed Sytems, vol. 22, no. 5, pp. 743-756.
  23. Xu, Q., Sengupta, R., Jiang, D., Chrysler, D. (2003). Design and analysis of highway safety communication protocol in 5.9 GHz Dedicated Short Range Communication Spectrum, Proceedings of IEEE VTC, vol.57, no. 4, pp. 2451-2455.
  24. Nekovee M. (2009). Epidemic algorithms for reliable and efficient information dissemination in vehicular ad hoc networks, IET Intelligent Transportation System, vol. 3, no. 2, pp. 104-110.
  25. Chu, Y-C., Huang, N-F. (2011). An efficient traffic information forwarding solution for vehicle safety communications on Highways, IEEE Transactions on Intelligent Transportation Systems, vol 13, No 2, pp. 631-643.
  26. Ros, FJ., Ruiz, PM. (2013). Minimum broadcasting structure for optimal data dissemination in vehicular networks, IEEE Transactions on vehicular technology, vol. 62, no. 8, pp. 3964-3972.
  27. Zhuang, Y., Pan, J., Luo, Y., Cai, L. (2011). Time and location- critical emergency message dissemination for vehicular ad hoc networks, IEEE Journal on Selected Areas in Communications, vol. 29, no. 1, pp. 187-196.
  28. Parameswari, P., Manikantan, M.,(2017). GEO - INTELLIGENCE SYSTEM: A Framework for Agricultural Improvements, International Journal of Pure and Applied Mathematics, vol. 116, no.12, pp. 117-125.

187-197

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45.

Authors:

W. Jai Singh,R.K. Kavitha

Paper Title:

A Novel Method for Detection of Retinal Lesions Using Statistical Based Segmentation with Supervised Classifier

Abstract: Lesion from the retinal images are one among the main sources of visual deficiency. It impacts veins in the light-sensitive tissue known as retina. Various kinds of marks in Diabetic Retinopathy (DR) will represent the abnormalities in the retina. The automated lesion segmentation in retinal pictures is a vital task in computer-aided detection systems. The research article proposes a computational framework for detection of lesion in retina images. In the initial process, Gabor filtering technique is used to enhance the lesion regions. Second, the segmentation of the suspicious region is based on expectation maximization bootstrap subgroup and mathematical morphology. A hybrid feature set is selected from the suspicious region. Finally, a classification method is applied to pin-point the lesions in the suspicious region. The projected technique has been evaluated on two public databases: DRIVE and STARE. The experimental result shows the proficiency and viability of the proposed strategy, and it can possibly be utilized to analyze DR clinically.

Keywords: Lesion, Diabetic Retinopathy, lesion, Segmentation, Feature extraction, Classification, Computer aided detection.

References:

  1. Lim, Registration of new blindness in Singapore for 1985–1995. Singapore Medical Journal. 39 (3), 1998, 104–106.
  2. International Diabetes Federation, [Online] http://www.idf.org/wdd-index/.
  3. Ramon Pires and Herbert F. Jelinek, Assessing the Need for Referral in Automatic Diabetic Retinopathy Detection, IEEE Transactions on Biomedical Engineering, Vol. 60, 2013, pp 3391 – 3398.
  4. Taylor, J. Xie, S. Fox, R. Dunn, A. Arnold, and J. Keeffe, “The prevalence and causes of vision loss in indigenous australians: The national indigenous eye health survey,” Med. J. Aust., vol. 192, no. 6, pp. 312–318, 2010.
  5. Liu, Beiji Zou, Jie, Wei Yue, Zailiang Chen, and Guoying , “A location-to-segmentation strategy for automatic exudatesegmentation in colour retinal fundus images”, Computerized Medical Imaging and Graphics, Vol. 55 2017 78–86.
  6. HeHuang and HeMa Automatic detection of neovascularization in retinal images using extreme learning machine, Neuro computing, Vol 277, 2018, pp. 218-227.
  7. UsmanAkram M, ShehzadKhalid, Anam Tariq, ShoabA.Khan and FarooqueAzam, “Detection and classification of retinal lesions for grading of diabetic retinopathy”, Computers in Biology and Medicine 45(2014)161–171.
  8. B, “Bootstrap methods: Another look at the Jacknife”, Annals of Statistics, Vol.7, No. 1, 1979, pp: 1-26.
  9. Desire Sidibe and Ibrahim Sadek, Discrimination of retinal images containing bright lesions using sparse coded features and SVM, Vol. 62, 2015, pp. 1750184.
  10. Singh, W. J., and Nagarajan, B. Automatic diagnosis of mammographic abnormalities based on hybrid features with learning classifier. Computer methods in biomechanics and biomedical engineering, Vol 16, 2013, 758-767.
  11. Walter, T., Klein, Massin, P., Erginay, A contribution of imageprocessing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Transaction on Medical Imaging, Vol 21, 1236–1243, 2002.
  12. Qing, Beiji Zou, Jie Chen, Wei, Kejuan Yue, Zailiang and Guoying Zhao, “A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images”, Computerized Medical Imaging and Graphics 55, 2017.
  13. Jai Singh W, S. Devaarul and R.K. Kavitha, “Detection of Abnormalities in Color Fundus Images of Diabetic Retinopathy using Bootstrap Segmentation with Learning Classifier”, International Journal of Pure and Applied Mathematics, Volume 116 No. 12 2017, pp: 87-95.

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46.

Authors:

K. Muthukumar, M. Suman Mohan

Paper Title:

Hazard Operability Study (HAZOP) in a Fertiliser Plant

Abstract: Hazard Operability (HAZOP) study is especially valuable for recognizing shortcomings in frameworks (existing or proposed) including the stream of materials, individuals or information, or various occasions or exercises in an arranged grouping or the methodology controlling such a succession. The HAZOP study was carried out in one of the fertiliser industries in India. The investigation was done for five unique tasks having 17 hubs, unusual working conditions and bothersome exercises which may happen are recognized and considered amid the examination utilizing suitable guide words like No, More, Less, As well as and so on. More stream, more temperature, more weight and additionally in the hubs are having unfavourable impact and prompt consideration is required. 101 reasons for deviation were distinguished. The causes are as given: disappointments in the dimension estimation instrument, non direction of the siphon, execution of creation process physically, erosion of the caustic way, obstruction of the passages, blemished check valves, mechanical issues of check valves, spillage of the channels, and blending of water in the framework. 35.7 % of all dangers were inadmissible, 37% risky, 19.8 % satisfactory yet required re examination, and 7.5% worthy with no requirement for any restorative activity. To keep any calamitous results, we prescribe to do HAZOP think about.

Keywords: HAZOP, Analysis, Guide words, Nodes, Abnormal, Consequences.

References:

  1. 61882:2001 Hazard and operability studies (HAZOP studies). Application guide.
  2. Dunjo J, Fthenakis V, Vílchez JA, Arnaldos J. Hazard and operability (HAZOP) analysis. A literature review. J Hazard Mater 2010; 173 (1–3): 19-32.
  3. Wang F, Gao J, Wang H. A new intelligent assistant system for HAZOP analysis of complex process plant. J Loss Prev Process Ind 2012; 25 (3): 636-642.
  4. Khan FI, Abbasi SA. Major accidents in process industries and an analysis of causes and consequences. J Loss Prev Process Ind 1999; 12(5): 361-78.
  5. Johnson RW. Beyond-compliance uses of HAZOP/LOPA studies. J Loss Prev Process Ind 2010; 23 (6): 727-733.
  6. Rossing NL, Lind M, Jensen N, Jorgensen S. A Goal Based HAZOP Assistant Original. COMP AID CH 2009; 26: 1129- 1134.
  7. Kitajima T, Fuchino T, Shimada Y, Naka Y, Yuanjin L, Iuchi K, Kawamura K. A New Scheme for Management-of-Change Support Based on HAZOP Log. COMP AID CH 2010; 28: 163- 168.
  8. Preisig HA, Manenti F HAZOP - an automaton-inspired approach. COMP AID CH 2012; 30: 1242-1246.
  9. Reza A, Christiansen E. A case study of a TFE explosion in a PTFE manufacturing facility. Process Saf Prog 2007; 26(1): 77- 82.
  10. Kwamura K, Naka Y, Fuchino T, Aoyama A, Takagi N. Hazop support system and its use for operation. COMP AID CH 2008; 25: 1003-1008.
  11. Mata JL, Rodriguez M. HAZOP studies using a functional modeling framework COMP AID CH 2012; 30:1038-1042.
  12. Labovsky J, Lassak P, Markos J, Jelemensky L. Design, optimization and safety analysis of a heterogeneous tubular reactor by using the HAZOP methodology. COMP AID CH 2007; 24: 1241-1246.
  13. Kletz TA. Hazop-past and future. Reliable Eng Sys Safety 1997; 55(3): 263-6.
  14. Mearns K, Whitaker S, Flin R. Benchmarking safety climate in hazardous environments: a longitudinal, inter-organizational approach. Risk Anal 2001; 21(4): 771–786.
  15. Jeerawongsuntorn C, Sainyamsatit N, Srinophakun T. Integration of safety instrumented system with automated HAZOP analysis: An application for continuous biodiesel production. J Loss Prev Process Ind 2011; 24 (4): 412-419.
  16. Shirazeh Arghami, Sedigheh Abbasi, Shakiba Bakhtom ,Mansour Ziaei 2014 “Comparing of HAZOP and ETBA Techniques in Safety Risk Assessment at Gasoline Refinery Industry” by African Journal of Basic & Applied Sciences 6 (1): 01-05, 2014. 17. Siddiquia N.A., Abhishek Nandana, Madhuben Sharmaa and Abhinav Srivastavaa, , “Risk Management Techniques HAZOP & HAZID Study” International Journal On Occupational Health & Safety, Fire & Environment – Allied Science-Vol. 1 Issue 1 July-Sept,2014 (005-008).
  17. Dennis P, Nolan PE. Application of HAZOP and What-If Safety Reviews to the Petroleum, Petrochemical and Chemical Industries. 1st ed. Noyes Publications. USA, 1994: pp. 67-84.
  18. Earthy JV. Hazard and operability study as an approach to software safety assessment. Hazard Anal. 1992; 9(5): 50-9.
  19. Fencott C, Hebbron BD. The application of HAZOP studies to integrated requirements models for control systems. ISA Trans. 1995; 34(3): 297-308.
  20. Redmill F, Chudleigh MF, Catmur JR. Principles underlying a guideline for applying HAZOP to programmable electronic systems. Reliab Eng Sys Safety.1997; 55(3): 283-93.
  21. Muhlbauer WK. Pipeline Risk Management Manual: Ideas, Techniques, and Resources. Gulf Professional Publishing. 2004; pp. 29-36.
  22. Selva Kumar V and Ramanathan L “Generalized Hazop Analysis for Process Plant” International Journal for Scientific research and development Volume : 3, Issue : 1 2015.

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47.

Authors:

M.C.S. Geetha, K. Divya Sri

Paper Title:

A Study on Traffic Analyses in Social Media Network of Twitter through Data Mining Techniques

Abstract: Social media helps to share the data around the globe. In this world, data is shared everywhere with the advent of social media, so that people from one place can share the data and their comments to others. Social Media provides comfortable way for sharing one information out of their interests. It can be used to contact their friends as well as making more friends and also search person with same kind of ideas and interests. It is very useful for any disasters occurred. At the time of tragedies social media paves a way for finding them and assist them. However sharing of the information in the social media like twitter, the traffic problem might be occurred due to heavy traffic. The social media user wants a minimum traffic to transmit the data without gap. In order to overcome this problem, this paper helps to study the comparison of various data mining techniques for smooth transfer of data.

Keywords: Social Media Twitter, Data Mining technique, content sharing and traffic analyses.

References:

  1. De Choudhury, Y.-R. Lin, H. Sundaram, K. S. Candan, L. Xie, and A. Kelliher. How does the data sampling strategy impact the discovery of information di_usion in social media? In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pages 34{41, 2010.
  2. Go, L. Huang, and R. Bhayani. Twitter sentiment classification using distant supervision. In CS224N Project Report, Stanford, 2009.
  3. Harshita Rajwani, “Dynamic Traffic Analyzer Using Twitter”
  4. Maximilian Walther and Michael Kaisser, ”Geo-spatial Event Detection in the Twitter Stream”, P. Serdyukov et al. (Eds.): ECIR 2013, LNCS 7814, pp. 356367, 2013.cS pringer VerlagBerlinHeidelberg 2013.
  5. Peter F. Klemperer, Yuan Liang, Michelle L. Mazurek, “Tag, You Can See It! Using Tags for Access Control in Photo Sharing”, Conference on Human Factors in Computing Systems, May 2012.
  6. Kapadia, F. Adu-Oppong, C. K. Gardiner, and P. P. Tsang, “Social circles: Tackling privacy in social networks,” in Proc. Symp. Sable Privacy Security, 2008.
  7. Sergej Zerr, Stefan Siersdorfer, Jonathon Hare, Elena Demidova , I Know What You Did Last Summer!:Privacy-Aware Image Classification and Search , Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, 2012
  8. Anna Cinzia Squicciarini, “Privacy Policy Inference of User-Uploaded Images on Content Sharing Sites”, IEEE Transactions On Knowledge And Data Engineering, vol. 27, no. 1, January 2015.
  9. B.Hema and Ms.S.Sivagami, “Survey on secure and time confined image sharing on websites”.
  10. Anna C. Squicciarini, Mohamed Shehab, Federica Paci “Collective Privacy Management in Social Networks”.
  11. J 1, Kavitha, “An Improved Privacy Policy Inference over the Socially Shared Images with Automated Annotation Process”.
  12. Neha Mehta, Mamta Kathuria, Mahesh Singh, “Comparison of Conventional & Fuzzy Clustering Techniques: A Survey”.
  13. Vikram Singh and Balwinder Saini “An Effective Tokenization Algorithm for Information Retrieval System” CS & IT-CSCP 2014
  14. Takeshi Sakaki, Makoto Okazaki, Yutaka Matsuo,” Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors”, 2010.
  15. Takemura and K. Tajima, “Tweet classification based on their lifetime duration,” in Proc. 21st ACM Int. CIKM, Shanghai, China, 2012,pp. 2367–2370.
  16. C.S.Geetha, I.Elizabeth Shanthi, “A Survey and Analysis on Regression Data Mining Techniques in Agriculture”, International Journal of pure and applied mathematics, Volume 118 No. 8 Feb 2018, pg no. 341-347 ISSN: 1311-8080 (print) ISSN: 1314-3395 (online).
  17. Parameswari, P., Manikantan, M.,(2017). GEO - INTELLIGENCE SYSTEM: A Framework for Agricultural Improvements, International Journal of Pure and Applied Mathematics, vol. 116, no.12, pp. 117-125.
  18. Ratan Mishra and Anant Jaiswal, “Ant colony Optimization: A Solution of Load balancing in Cloud”,in International Journal of Web & Semantic Technology (IJWesT), Vol.3, No.2, pp. 33-50, 2012.

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48.

Authors:

Ramalatha Marimuthu, Navaneethakrishnan R , Alagu Meenakshi, Uma Maheswari S

Paper Title:

Catch Them Young: Importance of Career Planning in Indian School Education Systems

Abstract: Higher Education Systems receive a lot of attention from the researchers on identifying good teaching practices in higher education to make it student attractive since it is the nearest point of education towards career and early life education is often ignored. But the primary factor in the choice of higher education in India is the current popularity of the stream (on the basis of the mass and media opinion) and not the aspiration and interest of the students. The lack of systematic student profiling to understand their strengths and encourage them in the correct career path is the major drawback of the Indian School Education System. This paper provides a framework for the steps to be followed to introduce life career planning education in schools and the various factors to be considered while profiling the students.

Keywords: student profiling, SWOT analysis, school education, career and life planning

References:

  1. https://timesofindia.indiatimes.com/home/education/ news/ 60-of-engineering- graduates- unemployed/ articleshow/ Cms
  2. https://www.gnu.org/education/edu-system-india.en.html
  3. Arjit Ghosh, Rittika Chanda Parruk, Sasha Sheppard, " Indian School Education System An Overview", The British Council, India, 2014.
  4. Kamlesh Gakhar, Harjeet Kour, "Scenario Of Present Education System: A Comparative Study Of Haryana And Its Neighbouring States", International Journal of Social Science & Interdisciplinary Research Vol.1 Issue 8, August 2012
  5. Gretchen Rhines Cheney, Betsy Brown Ruzzi and Karthik Muralidharan, "A Profile of the Indian Education System", National Center on Education and the Economy, 2006
  6. Gautam, Mohan & Singh, Sunny & Fartyal, Gopal & Tiwari, Ankit & Singh Arya, Kuldeep. (2016). Education System in Modern India. International Journal of Scientific Research And Education. 10.18535/ijsre/v4i01.16.
  7. Urvashi Sahni, "Primary Education in India:Progress and Challenges" Brookings Report, January 2015
  8. Akash A.R., Ramalatha Marimuthu, Navaneethakrishnan R, Kanagaraj S, "Cultural factors impacting the Global Energy transition- a review, International Conference on Renewable Energies, Power Systems and Green Inclusive Economy, 23-24, April 2018, Casablanca, Morocco.
  9. V.Mohana Sundaram, SWOT analysis of Indian Higher Education, ECONSPEAK, A Journal Of Advances In Management, IT and Social Sciences, Volume 1, Issue 3 (September, 2011)
  10. Guide on Life Planning Education and Career Guidance for Secondary Schools, Career Guidance Section, School Development Division, Education Bureau, (May 2014)
  11. Ramalatha Marimuthu, S.Sathyavathi, “Impact of service learning and social immersion on education and career building of young Indian Engin eering graduates – A case study”, IEEE International Women in Engineering Conference on Electrical, Electronics and Com[puter Engineering, Pune December 2016.

212-216

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49.

Authors:

N. Jayakanthan, M. Manikantan, R. Rassika

Paper Title:

An Investigation of Quality Enhancement in Online Shopping and Inventory Maintenance

Abstract: Online shopping is the culture of current e-commerce scenario. It provides lot of choices and opportunities. Every day million of transactions are performed and billion dollars are traded. But the major drawback of online shopping system is latency in order fulfillment and inventory management. There is a imperative for a system to address the time efficiency of the above process. To improve the performance of online commerce here with we propose a model called “Shoppy Do”. The Bee colony optimization modes performs stock clustering. The Pathrouter, a greedy algorithm is used to optimize the short path to improves the efficiency. The proposed model address the latency issues in order fulfillment

Keywords: E-Commerce, Efficiency, ACO algorithm, Resource allocation

References:

  1. Abdul Gaffar Khan, ”Electronic Commerce: A Study on Benefits and Challenges in an Emerging E-conomy”,Golobal Journal of Management and Business Research, Vol. 6, Issue.1, 2016.
  2. Babar Alam Iqbal,”E-Commerce Vs Mobile Commerce” International Journal of Applied Research Vol.2, 2013,pp.02-24.
  3. Cecil Eng Huang Chua, “The evaluation of E-commerce research A Stake holders perspective”, Journal of Electronic Commerce Research, Vol 6, No.4, 2005.
  4. Rajneesh Shahjee, “The Impact of Electronic Commerce on Business Organization “, Scholarly Research Journal on Inter disciplinary studies, Vol.4, Issue.27, 2006.
  5. Irene Bertschek, “The Adoption of Business to Business E-Commerce: Emperical Evidance for German Companies “, Discussion Paper, pp.1-25.
  6. Diyan Ivanov, The impact of e-commerce on small-size companies in Sweden, June 2012.
  7. Shaji Thomas, “Recent Trends in E-Commerce”, International Research Journal of Engineering and Technology, Vol.6, Issue.6,2015.
  8. Jayakanthan, M.Manikantan and R.Viveak, The enhanced learning model to improve the quality of Eshopping and stock management“, International Journal of Pure and Applied Mathematics, Vol. 115, No. 2, 2018, pp. 837-841.
  9. Jayakanthan and A.V.Ramani, Graph based Classifier to Detect Malicious URLs, International Journal of Mechanical and Production Engineering Research and Development Vol.7, Issue.5, pp. 223-234, 2017.

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50.

Authors:

Amala Jayanthi.M, Lakshmana Kumar.R, Hari Priya.K.P

Paper Title:

Influence of Students’ Personality Traits on Learning Style

Abstract: Students are the soul of Academics. The Goal of the education is students centric. The Motto of the education is to groom the students’ knowledge, skillsets and behavior under well-trained academician supervision in academics. Bloom’s theory on learning activity states, as the learning progress, a person’s not only grows in his/her knowledge and mental skills (i.e. Cognitive) but also in his/her emotions (i.e. Affective). Educational Data Mining is one of the budding research area in educational sector. It helps to enhance the standard of the academic system by understanding the educational method and their involvement within the higher manner. Personality is a blend of an individual emotional, Attitudinal, Behavioral responses. Personality is an intrinsic influence of emotions. As personality preferred learning style also an influence of attitude/behavior. This research is to study the influence of the personality trait of students on their Learning Style. The personality is of the students is evaluated using Eysenck Personality Inventory. The Learning form of the students is decided by the Honey and Mumford Learning Inventory. This paper finds out the impact of personality on the Learning style preferences of the students in their learning process by employing the supervised and unsupervised techniques on the students’ dataset. Navies Bayes Classification and K-Means clustering is utilized to classify the learners under their Learning and Personality classes. The study determines the existence of positive association between the students’ Personality and Learning Style through descriptive and predictive modelling using mapping or function. This investigation allows the teaching community to understand students’ Personality and Learning Style in the learning environment provide education with appropriate teaching style.

Keywords: Navies Bayes Classification, K-Means Clustering, Bloom’s Taxonomy, Affective Domain, Eysenck Personality Inventory, Personality Types, Honey and Mumford Learning Styles and Inventory.

References:

  1. Hans Jürgen Eysenck&Sybil B. G. Eysenck (1975). Manual of the Eysenck Personality Questionnaire. London: Hodder and Stoughton
  2. Sybil B. G. Eysenck, Hans Jürgen Eysenck& Paul Barrett (1985).Arevised version of the Introvert scale". Personality and Individua Differences6 (1): 21–29. DOI:1016/0191-8869(85)90026-1.
  3. Brown, D. H. (2000). Principles of language learning & (4th New York: Longman. (pp. 142-152)
  4. Relations Between Affect and Personality: Support for the Affect-Level and Affective-Reactivity Views. JamesJ. Gross, Steven K. Sutton andTimothy Ketelaar
  5. A study on the relationship between extroversion-introversion and risk-taking in the context of second language acquisition, Zafar Shahila, Meenakshi, K.International Journal of Research Studies in Language Learning 2012 January, Volume 1 Number 1, 33-40
  6. Khalian,M.,Borounjeni,F.Z.,Mustapa,N.,Sulaiman,M.N. : k-Means Divide and Conquer Clustering . In : International Conference on Computer and Automation Engineering, PP.306-309.IEEE Computer Society , Los Alamitos (2009).
  7. Choi, S.C., Hart, P.E., Stork, D.G,: Pattern Classification, 2nd edn.John . Wiley& sons Inc., Chichester (2000).
  8. X. Kumar, V., Quinlan. J.R., Ghosh. J., Yang. Q., Motoda.H. McLachalan, G.J., Ng, A., Liu B., Yu P.S., Yu, P.S., Zhou, Z,-h., Steinbach, M., Hand, D.J., Steinberg. D.: Top 10 Algorithms in Data mining Knowl.laf.Syst.14:!-37(2008)
  9. Ayers, E., Nurgent,R., Dean , N.: Skill Set Profile Clustering Based on Student Capability Vectors Compute from Online Tutoring Data .In : Baker,R.S.J.D.,Barnes.T., Beck,J.E.(eds) Proceedings of 1st International Conference on Educational Data Mining ,Montreal,Qubec,Canada,June 20-21,pp210-217(2008)
  10. Pavik Jr., P.I., Cen , H., Wu, L., Koedinger, K.R.: Using Item-type Performance Covariance to Improve the skill Model of an Existing Tutor . In: Proceedings 1 st International Conference on Educational Data mining, Canada, June 20-21.pp.77-86(2008)
  11. Green, T.M., Jeong, D.H., Fisher. B.: Using Personality Factors to Predict Interface Learning Performance. In: 43 rd Hawaii International Conference on System Sciences. IEEE Computer Society, Honolulu, HI, January 5-8, pp. 1-10. IEEE Computer Society, Los Alamitos (2010)
  12. Chiu, C .: Cluster Analysis for Cognitive Diagnosis : Theory and Applications . Ph.D.Dissertation, Educational Psychology, University of Illinois at Urbana Champaign (2008)
  13. , Nugent , R., Dean ,N: A Comparison of student skill Knowledge Estimates Educational Data mining In: 2nd International Conference on Educational Data mining, Cordoba ,Spain, July 1-3 ,pp.1-10(2009)
  14. Nghe,N.T., Janecek,P., Haddawy.P: A Comparative Analysis of Techniques for predicting Academic Paper Presented at 37 th ASEE/IEEE Frontiers in Education Conference, Milwaukee,WI, October 10-13(2007)
  15. L, Austin, M.: The relationship between software skills and Subject specific Knowledge, Theory and Practice .Learning and Teaching Projects.
  16. Arockiam., S.Charles, V.Arul Kumar., P.Cijo. A Recommender System for Rural and urban Learners.Trends in Computer Science, Engineering and Information TechnologyCommunications in Computer and Information Science, 2011, Volume 204, Part 1, 619-627, DOI:10.1007/978-3-642-24043-0_63
  17. J., Kamber, M.: Data mining Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers. San Francisco (2006)
  18. Gabriela-Alina Sauciuc , Categorization in the Affective Domain , In: Kokinov, B., Karmiloff-Smith, A., Nersessian, N. J. (eds.) European Perspectives on Cognitive Scienc, New Bulgarian University Press, 2011 ISBN 978-954-535-660-5
  19. Frijda, N. H. (1986). The emotions. New York:Cambridge University Press.
  20. Weiner, B., & Graham, S. (1984). An attributional approach to emotional development. In C. E. Izard, J. Kagan, & R. B. Zajonc (Eds.), Emotions, cognition,and behavior. New York: Cambridge University Press.
  21. 1norazlina Khamis ,Sufian Idris “Issues and Solutions in Assessing Object-oriented programming Skills in the Core Education of Computer Science and Information Technology”, 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, 2008.
  22. Xindong Wu • Vipin Kumar • J. Ross Quinlan • Joydeep Ghosh Qiang Yang • Hiroshi Motoda • Geoffrey J. McLachlan • Angus Ng Bing Liu Philip S. Yu • Zhi-Hua Zhou • Michael Steinbach • David J. Hand • Dan
  23. Steinberg, “Top 10 algorithms in data mining”, Springer-Verlag London Limited, Knowl Inf Syst 14:1–37, 2008.
  24. Ayers, E, Nugent, R, Dean, N. .Skill Set Profile Clustering Based on Student Capability Vectors Computed from Online Tutoring Data..Educational Data Mining 2008: 1st International Conference on Educational Data Mining,Proceedings ,R.S.J.d. Baker, T. Barnes, and J.E. Beck (Eds), Montreal, Quebec, Canada, June 20-21, pp.210-217, 2008
  25. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining, Journal of Computing, Volume 1, Issue 1, December 2009.
  26. Madjid Khalilian, Farsad Zamani Boroujeni, Norwati Mustapha, Md.Nasir Sulaiman, "K-Means Divide and Conquer Clustering,", International Conference on Computer and Automation Engineering ,IEEE Computer Society, pp. 306-309, 2009.
  27. Adrian Furnham"Personality and Learning Style – A Study of Three Instruments,"Person.individ.diff.Vol.13,No.4, pp. 429-438, 1992.
  28. Kavitha, RK & Irfan Ahmed, MS 2015, ‘Knowledge sharing through pair programming in learning environments: An empirical study’, Education and Information Technologies, Springer, US, vol. 20, no. 2, pp. 319-333.
  29. Kavitha,R.K., Jalaja Jayalakshmi, V., Rassika, R., (2018). Collaborative learning in Computer Programming Courses using E-Learning Environments. International Journal of Pure and Applied Mathematics, Volume 118 No. 8 2018, 183-189
  30. Kavitha,R.K., Jalaja Jayalakshmi, V., Kaarthiekheyan, V., (2017). Adoption of Knowledge Management Framework in Academic Setting – An Experimental Study Conducted for Capturing Student’s Learning in Computer Laboratories. International Journal of Pure and Applied Mathematics, Volume (116) No. 12, pp. 77-85.

220-223

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51.

Authors:

Amala Jayanthi. M, Lakshmana Kumar.R, Hari Priya.K.P

Paper Title:

Impact of Learning Style and Personality Traits on Students’ in Academics

Abstract: The objective of education exclusively aims on students. The goal of education said to be achieved once the students’ knowledge, skill set and attitude is groomed under the efficient supervision of well-trained educators. Benjamin Bloom’s theory on learning process, a person not only get groomed in his knowledge and mental abilities (i.e. Cognitive) but also in his emotions (i.e. Affective).One’s personality is defined as the unification of emotional, attitudinal, behavioral responses (i.e. Affective). Learning is the way toward getting new data or capacity to fabricate data from definitely known data.Learning is influenced by the human behavior (i.e. Affective).Human behavior is influenced by the living environment. Education aims to provide efficient and peaceful environment to mold students’ personality and learning skills. Educational Data Mining is one of the budding applications in educational sector. It helps to understand better the students’ learning activity and their overall involvement in the activity. This allows the further improvement of the quality and the productivity of the educational system. Eysenck Personality Inventory and Criterion Reference Model used to determine the personality of the students. This exploration is to contemplate the impact of the Personality characteristics and Learning Styles on the scholarly execution of the understudies as indicated by Bloom’s Theory. Eysenck Personality Inventory and Criterion Reference Model used to decide the identity of the understudies. Supervised and unsupervised techniques are used to analyze students’ dataset. Students are clustered based on the Personality, Learning Style and Performance by employing Multi-Layer Perceptron and EM clustering Technique. Descriptive and Predictive modelling is applied to determine the association between students’ Personality, Learning Styles and Academic Performance using mapping or function. The study depicts the existence of positive correlation between student’s Personality traits, Learning styles and Academic Performance. This research helps the educators to understand students’ Behavioral, Attitudinal and Emotional Growth during the learning process as a Personality and their learning ability. It helps the educators’ to provide appropriate training for improving their expertise in academics accordingly.

Keywords: Multi-Layer Perceptron (MLP), Expectation Maximization (EM) clustering, Criterion Reference Model, Bloom’s Taxonomy, Honey and Mumford’s Learning Questionnaire, Eysenck Personality Questionnaire, Personality Types.

References:

  1. Hans Jürgen Eysenck & Sybil B. G. Eysenck (1975). Manual of the Eysenck Personality London: Hodder and Stoughton.
  2. Sybil B. G. Eysenck, Hans Jürgen Eysenck & Paul Barrett (1985). "A revised version of the Introvert scale". Personality and Individual Differences 6 (1): 21–29. DOI:1016/0191- 8869(85)90026-1.
  3. Brown, D. H. (2000). Principles of language learning & (4th ed.). New York: Longman. (pp. 142-152)
  4. Relations Between Affect and Personality: Support for the Affect-Level and Affective-Reactivity Views. James J. Gross, Steven K.Sutton and Timothy Ketelaar
  5. A study on the relationship between extroversion- introversion and risk-taking in the context of second language acquisition, Zafar Shahila, Meenakshi, K. International Journal of Research Studies in Language Learning 2012 January, Volume 1 Number 1, 33-40
  6. Choi, S.C., Hart, P.E., Stork, D.G,: Pattern Classification, 2nd edn.John . Wiley& sons Inc., Chichester (2000).
  7. X. Kumar, V., Quinlan. J.R., Ghosh. J., Yang. Q., Motoda.H. McLachalan, G.J., Ng, A., Liu B., Yu P.S., Yu, P.S., Zhou, Z,-h., Steinbach, M., Hand, D.J., Steinberg. D.: Top 10 Algorithms in Data mining Knowl.laf.Syst.14:!-37(2008)
  8. Ayers, E., Nurgent,R., Dean , N.: Skill Set Profile Clustering Based on Student Capability Vectors Compute from Online Tutoring Data .In : Baker,R.S.J.D.,Barnes.T., Beck,J.E.(eds) Proceedings of 1st International Conference on Educational Data Mining ,Montreal,Qubec,Canada,June 20-21,pp210-217(2008)
  9. Pavik Jr., P.I., Cen , H., Wu, L., Koedinger, K.R.: Using Item-type Performance Covariance to Improve the skill Model of an Existing Tutor . In: Proceedings 1 st International Conference on Educational Data mining, Canada, June 20-21.pp.77-86(2008)
  10. Green, T.M., Jeong, D.H., Fisher. B.: Using Personality Factors to Predict Interface Learning Performance. In: 43 rd Hawaii International Conference on System Sciences. IEEE Computer Society, Honolulu, HI, January 5-8, pp. 1-10. IEEE Computer Society, Los Alamitos (2010)
  11. Chiu, C .: Cluster Analysis for Cognitive Diagnosis : Theory and Applications . Ph.D.Dissertation, Educational Psychology, University of Illinois at Urbana Champaign (2008)
  12. , Nugent , R., Dean ,N: A Comparison of student skill Knowledge Estimates Educational Data mining In: 2nd International Conference on Educational Data mining, Cordoba ,Spain, July 1-3 ,pp.1-10(2009)
  13. Nghe,N.T., Janecek,P., Haddawy.P: A Comparative Analysis of Techniques for predicting Academic Paper Presented at 37 th ASEE/IEEE Frontiers in Education Conference, Milwaukee,WI, October 10-13(2007)
  14. L, Austin, M.: The relationship between software skills and Subject specific Knowledge, Theory and Practice .Learning and Teaching Projects.
  15. Arockiam., S.Charles, V.Arul Kumar., P.Cijo. A Recommender System for Rural and urban Learners. Trends in Computer Science, Engineering and Information Technology Communications in Computer and Information Science, 2011, Volume 204, Part 1, 619-627, DOI:10.1007/978-3-642-24043-0_63
  16. J., Kamber, M.: Data mining Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers. San Francisco (2006)
  17. Gabriela-Alina Sauciuc , Categorization in the Affective Domain , In: Kokinov, B., Karmiloff-Smith, A., Nersessian, N. J. (eds.) European Perspectives on Cognitive Scienc, New Bulgarian University Press, 2011 ISBN 978-954-535-660-5
  18. Frijda, N. H. (1986). The emotions. New York: Cambridge University Press.
  19. Weiner, B., & Graham, S. (1984). An attributional approach to emotional development. In C. E. Izard, J. Kagan, & R. B. Zajonc (Eds.), Emotions, cognition, and behavior. New York: Cambridge University Press.
  20. 1norazlina Khamis ,Sufian Idris “Issues and Solutions in Assessing Object-oriented programming Skills in the Core Education of Computer Science and Information Technology”, 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, 2008.
  21. Xindong Wu • Vipin Kumar • J. Ross Quinlan • Joydeep Ghosh Qiang Yang • Hiroshi Motoda • Geoffrey J. McLachlan • Angus Ng Bing Liu Philip S. Yu • Zhi-Hua Zhou • Michael Steinbach • David J. Hand • Dan
  22. Steinberg, “Top 10 algorithms in data mining”, Springer-Verlag London Limited, Knowl Inf Syst 14:1–37, 2008.
  23. Ayers, E, Nugent, R, Dean, N. .Skill Set Profile Clustering Based on Student Capability Vectors Computed from Online Tutoring Data..Educational Data Mining 2008: 1st International Conference on Educational Data Mining,Proceedings ,R.S.J.d. Baker, T. Barnes, and J.E. Beck (Eds), Montreal, Quebec, Canada, June 20-21, pp.210-217, 2008
  24. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining, Journal of Computing, Volume 1, Issue 1, December 2009.
  25. Madjid Khalilian, Farsad Zamani Boroujeni, Norwati Mustapha, Md.Nasir Sulaiman, "K-Means Divide and Conquer Clustering,", International Conference on Computer and Automation Engineering ,IEEE Computer Society, pp. 306-309, 2009.
  26. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
  27. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
  28. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
  29. Adrian Furnham"Personality and Learning Style – A Study of Three Instruments,"Person.individ.diff.Vol.13,No.4, pp. 429-438, 1992.
  30. Kavitha, RK & Irfan Ahmed, MS 2015, ‘Knowledge sharing through pair programming in learning environments: An empirical study’, Education and Information Technologies, Springer, US, vol. 20, no. 2, pp. 319-333.
  31. Kavitha,R.K., Jalaja Jayalakshmi, V., Rassika, R., (2018). Collaborative learning in Computer Programming Courses using E-Learning Environments. International Journal of Pure and Applied Mathematics, Volume 118 No. 8 2018, 183-189
  32. Kavitha,R.K., Jalaja Jayalakshmi, V., Kaarthiekheyan, V., (2017). Adoption of Knowledge Management Framework in Academic Setting – An Experimental Study Conducted for Capturing Student’s Learning in Computer Laboratories. International Journal of Pure and Applied Mathematics, Volume (116) No. 12, pp. 77-85.

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52.

Authors:

T. Anand, P. Sachin Prabhu, H.A. Nishaant

Paper Title:

Improvement of Project Performance by Constraint Analysis and Root Cause Analysis of Last Planner System

Abstract: An effective planning is required to overcome cost over-run and time over-run problems in construction which could be achieved with the help of lean concept. The aim of lean is to maximize the value, in other words minimizing the wastes. Last Planner System(LPS) is one such lean concept developed in order to improve the project performance by reducing the inefficiencies faced in construction project.The objective of this paper to present the results obtained from implementing the Last Planner System in the construction of an apartment project. A list of constraints have been found out during constraint analysis and various remedial measures have been suggested for look ahead planning. Based on number of occurrences of constraints, root cause analysis was done to determine the causes for variation in Percentage of Plan Completed(PPC). The effectiveness and reliability of the project was found to be increased about 75% after implementation of Last Planner System(LPS). A list of success factors and barriers for improvement of project performance have also been listed.

Keywords: Last Planner System, Look ahead planning, Constraint analysis, Root cause analysis, Percentage of plan completed.

References:

  1. Yong Woo Kim-’Management Thinking in the Earned Value Method System and the Last Planner System’, pp 223-228, 2010.
  2. DhananjaySubhashraoPatil, Amit Shankar Munje - ‘Comparative study of Last Planner System over Traditional Construction prcesses’, ISSN: 2279- 0535, Volume: 3, Issue: 4 (June-July 2014).
  3. Nieto-Morote and F. Ruz-Vila- ‘Last Planner Control System Applied to a Chemical Plant Construction’, Vol. 138, No. 2, February 1, 2012.
  4. Brad W. Wambeke, M.ASCE, Min Liu, M.ASCE and Simon M. Hsiang – ‘Using Last Planner and a Risk Assessment Matrix to Reduce Variation in Mechanical Related Construction Tasks’, Vol. 138, No.4, pp 491–498,2012.
  5. Jose L. Fernandez-Solis, Ph.D., Vishal Porwal, SarelLavy, Ph.D., M.ASCE, Ali Shafaat, Zofia K. Rybkowski, Ph.D. Kiyoung Son, Ph.D. and Nishi Lago(2013) – ‘Survey of Motivations, Benefits, and Implementation Challenges of Last Planner System Users’, Vol. 139, No. 4, pp 354-360April 1, 2013.
  6. RemonFayek Aziz, Sherif Mohamed Hafez – ‘Applying lean thinking in construction and performance improvement’, pp 679-695, May 27, 2013.
  7. UsamaHamedIssa – ‘Implementation of lean construction techniques for minimizing the risks effect on project construction time’, pp 697-704, 2013.
  8. Anand T – ‘Implementing challenges of extended producer responsibility’, pp-1188-1194, August 2017.
  9. Anita S – ‘Integrated Panchayat Response system using open source GIS’, pp 273-278, August 2017.

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53.

Authors:

J. Premlatha, G. L. Sathyamoorthy, S. Anita

Paper Title:

Utilization of Plastic Waste and Foundry Waste in Flexible Pavements

Abstract: The materials used for pavement is bitumen, coarse aggregate, fine aggregate and soil, concerned about this throughout the globe research works are under progress to find alternative materials for pavement construction and obviously the plastic waste from municipal solid waste and foundry sand from industrial waste materials are one such category. Plastic is a toxic and persistent material. The municipal solid waste which is a major environmental threat contains about 16 to 25% of plastic. Another environmental threat is foundry sand which is the waste product from casting industries.The foundry sand can be used in various engineering applications. This will solve the depletion of regular use materials and also disposal of foundry waste. There is a need for bulk use of plastic waste from municipal solid waste and foundry sand from foundry industrial wastes in our country. This paper elaborates about the materials and its suitability for flexible pavement construction that is economically feasible. Industrial waste replaced from cumulative weight of aggregate to understand the load carrying capacity of the flexible pavement

Keywords: plastic waste, foundry sand, bitumen

References:

  1. Use of recycled plastic in concrete: a reviewR Siddique, J Khatib, I Kaur - Waste management, 2008 - Elsevier
  2. Emerging road materials and innovative applicationsA Goel, A Das - National conference on materials 2004 - researchgate.net
  3. Using foundry sand in green infrastructure construction” SL Bradshaw, CH Benson, EH Olenbush on the State of the Art , 2010 - ascelibrary.org
  4. Geotechnical performance of highway embankment constructed using waste foundry sandP Fox, D Mast - Joint Transportation Research Program, 1998 - docs.lib.purdue.edu
  5. Use of waste polyethylene in bituminous concrete mixes” B Prusty - 2012 - ethesis.nitrkl.ac.in
  6. Utilization of Waste Foundry Sand (WFS) as Impermeable Layer (Drainage Blanket) for Pavement Structures” P Solmaz, AG Gedik, - Advances in Transportation Engineering 2008 - books.google.com
  7. “Partial replacement of waste foundry sand and recycled aggregate in concrete”-SaritaChandrakanth and Asst.Prof. Ajay.A.Hamane- IJMTER-2016
  8. https://avestia.com/ICCSTE2016_Proceedings/files/paper/138.pdf
  9. https://www.aboutcivil.org/marshall-stability-test-astm-d6927-06-standard.html
  10. “Experimental study on bitumen mix using geo fabric increasing the california bearing ratio of flexible pavement”-Dhavashankaran D1 Harikannan S2-Technical Research organization
  11. “Use of Waste Plastic in Flexible Pavements-Green Roads”-YashMenaria, RupalSankhla-Centre for Environmental Planning & Technology, Ahmedabad, India
  12. “An Experimental Study On Behavier Of Modified Bitumen Using Recycled Plant” Dr.A.Gandhimathi , S.Abinaya, International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 8, August 2017
  13. “Use of polyethylene terephthalate in concrete-A brief review” A.Vishnu, V.Ponmalar, International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 7, July 2017

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54.

Authors:

G.L. Sathyamoorthy

Paper Title:

Substrate Removal Kinetics for Anaerobic Hybrid Reactor (AHR) Treating Dairy Industrial Wastewater

Abstract: Anaerobic Hybrid Reactor (AHR) is one of the most commonly used high rate reactors for treating the domestic and industrial wastewater which offers the advantages of Up flow Anaerobic Sludge Blanket Reactor (UASBR) and Up flow Anaerobic Filter Reactor (UAFR) in a single reactor. In the present study determination of kinetic constants of the AHR was aimed by conducting experimental studies on a laboratory scale Anaerobic Hybrid Reactor(AHR) using different shapes of Poly Propylene Inter Media in the AHR and the dairy industrial wastewater as substrate. The mathematical equations Grau second-order model and Stover Kincannon model were found appropriate models for the design of Anaerobic Hybrid Reactors (AHRs).

Keywords: Anaerobic Hybrid Reactor (AHR), Kinetic Constants, Inert Media, Dairy Wastewater, Organic Loading Rate (OLR), Substrate

References:

  1. Banu J.R et al, “Treatment of domestic waste water using upflow anaerobic sludge blanket reactor”, International Journal of Environmental Science and technology(4)3,pp363-370,(2007)
  2. R.F et al, “Startup operation monitoring and control of high rate anaerobic treatment systems”, Water Science Technology(24),pp207-255,(1991)
  3. Hu Hong-Ying et al, “Effects of adding inert spheres into the filter bed on the performance of biofilters for gaseous toluene removal”, Biochemical engineering journal (23),pp123-130,(2005)
  4. H.N et al, “Treatment of dairy waste water using an up flow anaerobic sludge blanket reactor”, Journal of Agricultural Engineering Research(73),pp59-63,(1999)5.
  5. Najafpour G.D et al, “Biological Treatment of Dairy waste water in an up flow Anaerobic Sludge-Fixed Film bioreactor,American-Eurusian J.Agric.& Environ,Sci 4(2),pp251-257,(2008)
  6. Nurdan Bủyủkkamaci,Ayse Filibeli “Determination of kinetic constants of an anaerobic hybrid reactor”,Process Biochemistry 38,pp73-79(2002)
  7. Nurdan Bủyủkkamaci,Ayse Filibeli,”Concentrated Waste water treatment studies using an anaerobic hybrid Reactor”, Process Biochemistry,38,pp771-775 (2002)
  8. Standard Methods for the Examination of Water and Wastewater. 18th American Public Health Association (APHA)/American Water Works Association/Water Pollution Control Federation, Washington DC, USA. (1992)
  9. L.Sathyamoorthy”A Novel Approach to Sago Industrial Wastewater using Anaerobic Hybrid Reactor (AHR) International Journal of Civil Engineering and Technology, 8(7), 2017, pp1229–1238, ISSN No: 0976-6316 (2017)
  10. Thanikal J.V., Torrijos M., Habouzit F., Moletta R. “Treatment of distillery vinasse in a high rate anaerobic reactor using low-density polyethylene supports”. Water Sci. Tech, 56(2), pp17–24. (2007)
  11. Wilson F.et al “Influence of media-packing ratio on performance of anaerobic hybrid reactor”,Biosource Technology,71,pp151-157,(2000)

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Authors:

Bhavna Bharath, Suganthi N

Paper Title:

A Comprehensive Survey of Multimodal Image Fusion Schemes

Abstract: Multimodal images are scenes with anatomy details that are captured using two different devices. Different imaging techniques give complementary details about what is visualized. Infrared and visual images are examples of multimodal images that are fused together in order to obtain a single comprehensive fused image. Combining multimodal images yield enhanced features for image analysis, feature extraction and detection. Infrared and Visual image fusion will fuse the source images into single extensive image to raise image quality .This will in turn decrease the redundancy in image data. This is broadly used in different applications to improve the perception of the scene. The reliability, accuracy and complementary details of the scene in the resultant fused image makes these approaches be used in multiple areas. Recently, many fusion methods have been formulated due to the sprouting demands & advancement of image depiction schemes. However, a unified survey paper about this field has not been published in a few years. Consequently, we make a survey report to record the methodical advancements of visual and infrared image fusion. In this paper, firstly the overview of applications of IR and VI image fusion is represented. Secondly, we present the existing state of the art fusion techniques. Finally, image quality metrics are discussed to measure the efficiency of the fusion algorithm. Although, this survey halts with various fusion methods that have been proposed earlier there is still room for improvement in research in the field of multimodal image fusion.

Keywords: Multimodal, Image Analysis, Image Fusion.

References:

  1. Zhang Bahua, Zhao Ying ," A fusion algorithm for infrared and visual based saliency analysis and non-sub sampled shearlet transform", Infrared Phys. Tech. 73(2015).
  2. Zhadong Liu, Yi Chai , "A novel fusion scheme for visual and infrared images based on compressive sensing", Optics Comm. 335(2015).
  3. Xudong Kang, Haitao Yin , "Pixel level image fusion: a survey of the state of the art", Information fusion 33(2017).
  4. Huajun Feng, Qi Li "Detailed preserved fusion of visible and infrared images using regional saliency extraction and multiscale image decomposition", Optical Comm 341(2015).
  5. Xinan Fan, Min Li ,"A thermal infrared and visible image fusion based approach for multitarget detection under complex environment", Math. Problem Engg(2015).
  6. Bhatnagar, Liu, "A novel image fusion framework for night-vision navigation and surveillance.9(2015).
  7. Alex James, Belur ,"Medical image fusion: a survey of the state of the art", Inform. fusion 19(2014).
  8. Amanda Muler, Narayan," Cognitively engineered multisensor image fusion for military applications,Inform, fusion 10(2009).
  9. Zhang, L.Yuan, "Infrared target detection and location for visual surveillance using fusion scheme of visible and infrared images", Math problems engg .3(2013).
  10. Tarek, Nizar, "Finite asymmetric generalized gaussian mixture models learning for infrared object detection", Comp.Vis.Image Underst.117(2013).
  11. Alcanatis, T.Burks, "Image fusion of visible and thermal images for fruit detection",Biosys.Eng.103(2009).
  12. Homa, W.Arnold, "Fusion of visual and infrared thermography images for advanced assessment in non destructive testing",Rev.Sci.Inst 84(2013).
  13. [.Gonzalez, Z.Fang, "Pedestriam detection at day/night time with visible and FIR cameras: a comparison", Sensors 16(2016).
  14. Dixon, J.Lewis, "Task based scan path assessment of multisensor video fusion in complex scenarios",Inform .fusion.11(2010).
  15. Mohammad Talha, Rustam Stokin, "Particle filter tracking of camouflaged targets by adaptive fusion of thermal and visible spectra camera data",IEEE sensors.14(2014).
  16. Seong Kong, Jing Heo, "Recent advances in visual and infrared face recognition-a review",Comput.Vis.Image Underst.97(2005).
  17. George Bebi, Aglika Gyaourov ," Face recognition by fusing thermal infrared and visible imagery",Image Vis.Comput.24(2006).
  18. Hermosilla, G.Faras, "Fusion of visible and thermal descriptors using GA for face recognition", Sensors 15(2015).
  19. [R.Ragavendran , Ashok Rao," Pso based fusion of near infrared and visible images for improved face verification",Pattern regn.44(2011).
  20. Buzha Wuuile, Yilng Yang,"Direct fusion of geostationary meteorological satellite visible and infrared images based on thermal physical properties",Sensors(2015).
  21. Liu, L.Jiao, "Multi contourlet based adaptive fusion of infrared and visinle remote sensing images",IEEE geosci.Remote sensing Letters 7(2010).
  22. U.Lang, D.J.L.Quing, "Visible and infrared video fusion using unifrom curvelt and saptio temporal informatiom(2015).
  23. Fuyan Xu, Kan Ren,"Super resolution images fusion via compressed sensing and low rank matrix decomposition,Infrared tech.68(2015).
  24. [Gema Piella ," A general framework for multi resolution image fusion from pixels to regions,"Inform fusion 4 (2003).
  25. Yan, J.Zhang,"Objective quality evaluation of visible and infrared color image fusion",Opt.Engg.50(2011).
  26. Jinping Fan, Xiayoi Feng,"Fusion method for infrared and visible images by using non-negative sparse representation",Inf. Phys.Tech.67(2014).
  27. Qan Jiang, Rencan Nie ,Xin Jin,"A survey of infrared and visual image fusion methods",Infrared Phy Tech.85(2017).
  28. L Latha, S Thangasamy," On improving the Performance of Multimodal biometric authentication through Ant colony optimization", Wseas Transactions on Information Science and Applications, Issue 12, Volume 8, December 2011.
  29. L Latha, M Pabitha, S Thangasamy," Effectual human authentication for critical security applications using retinal images", ICTACT Journal on Image and Video Processing, 2010.
  30. L Latha, S Thangasamy, "Providing multimodal biometric authentication using five competent traits" , The Imaging Science Journal, 2013.
  31. L Latha, S Thangasamy ," Robust Way of Multimodal Biometric Score Normalization",Journal of Applied Security Research, 2012.

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56.

Authors:

Nithya Roopa S., Prabhakaran M, Betty.P

Paper Title:

Speech Emotion Recognition using Deep Learning

Abstract: Emotion recognition is the part of speech recognition which is gaining more popularity and need for it increases enormously. Although there are methods to recognize emotion using machine learning techniques, this project attempts to use deep learning and image classification method to recognize emotion and classify the emotion according to the speech signals. Various datasets are investigated and explored for training emotion recognition model are explained in this paper. Some of the issues on database, existing methodologies are addressed in the paper. Inception Net is used for emotion recognition with the paper. Inception Net is used for emotion recognition with IEMOCAP datasets. Final accuracy of this emotion recognition model using Inception Net v3 Model is 35%(~).

Keywords: speech recognition; emotion recognition; automatic speech recognition; deep learning; image recognition; speechtechnology; signal processing; image classification

References:

  1. Furui, T. Kikuchi, Y. Shinnaka, and C. Hori, “Speech-to-Text and Speech-to-Speech Summarization,” vol. 12, no. 4, pp. 401–408, 2004.
  2. El Ayadi, M. S. Kamel, and F. Karray, “Survey on speech emotion recognition: Features, classification schemes, and databases,” Pattern Recognit., vol. 44, no. 3, pp. 572–587, 2011.
  3. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015.
  4. Schmidhuber, “Deep Learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85–117, 2015.
  5. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal Deep Learning,” Proc. 28th Int. Conf. Mach. Learn., pp. 689–696, 2011.
  6. Dipl and T. Vogt, “Real-time automatic emotion recognition from speech,” 2010.
  7. Lugovic, I. Dunder, and M. Horvat, “Techniques and applications of emotion recognition in speech,” 2016 39th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2016 - Proc., no. November 2017, pp. 1278–1283, 2016.
  8. Schuller, G. Rigoll, and M. Lang, “Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine - belief network architecture,” Acoust. Speech, Signal Process., vol. 1, pp. 577–580, 2004.
  9. G. Koolagudi and S. R. Krothapalli, “Emotion recognition from speech using sub-syllabic and pitch synchronous spectral features,” Int. J. Speech Technol., vol. 15, no. 4, pp. 495–511, 2012.
  10. Rong, G. Li, and Y. P. P. Chen, “Acoustic feature selection for automatic emotion recognition from speech,” Inf. Process. Manag., vol. 45, no. 3, pp. 315–328, 2009.
  11. Noroozi, N. Akrami, and G. Anbarjafari, “Speech-based emotion recognition and next reaction prediction,” 2017 25th Signal Process. Commun. Appl. Conf. SIU 2017, no. 1, 2017.
  12. Graves, A. Mohamed, and G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 6645–6649.
  13. -W. Huang and S. S. Narayanan, “Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition,” pp. 1–19, 2017.
  14. M. Fayek, M. Lech, and L. Cavedon, “Evaluating deep learning architectures for Speech Emotion Recognition,” Neural Networks, vol. 92, pp. 60–68, 2017.
  15. M. Badshah, J. Ahmad, N. Rahim, and S. W. Baik, “Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network,” 2017 Int. Conf. Platf. Technol. Serv., pp. 1–5, 2017.
  16. Busso et al., “IEMOCAP: Interactive emotional dyadic motion capture database,” Lang. Resour. Eval., vol. 42, no. 4, pp. 335–359, 2008.
  17. Szegedy, V. Vanhoucke, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” 2014.

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Authors:

Yamunathangam.D, Kirthicka.G, Shahanas Parveen

Paper Title:

Performance Analysis in Olympic Games using Exploratory Data Analysis Techniques

Abstract: The Olympic games are international sports events with more than 200 nations participating in various competitions. The Sportspersons from various countries participate in competitions and make their countries proud of their excellence in sports. Despite massive population, many most populous countries fail to grab many medals at the Olympic games. The primary objective of this paper is to analyse the Olympic dataset using python to compare overall performance of countries and to evaluate the contribution of each country in Olympics. These analyses will give deeper insight into the performance of countries in Olympics over the years and helps sportspersons to quickly analyse their own and the competitor’s performance. In this paper, the exploratory data analysis techniques are used to provide comparison between performance of various countries and the contribution of each country in Olympics. Visualization of Olympics dataset in many aspects provides the status of countries in Olympics and helps countries with poor performance to produce quality players and improve nation’s performance in Olympics.

Keywords: International, Excellence, Performance Analysis, Visualization.

References:

  1. Antarlina Sen and Gaurang Margaj,“A prediction model for which country will win highest number of ‘Gold’ , 2016
  2. Leonardo De Marchi,“Data mining of Sports performance data”, 2011.
  3. Huang-Chiashih,”Survey on content-aware Video Analysis for Sports” , IEEE Transactions on Circuits and Systems for Video Technology, Vol. 99, No. 9, January 2017.
  4. Chandra Segar Thirumalai and Monica Sankar,“Heuristic Prediction of Olympics using Machine Learning” , International Conference on Electronics, Communication and Aerospace Technology ,April 2017.
  5. Alexander Rathke and Ulrich Woitek,“Economics and Olympics: An Efficiency analysis” , January 2007.
  6. Summer Olympics Dataset, Available: https://www.kaggle.com/the-guardian/olympic-games/data

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Authors:

M. Mohankumar, M Anand Kumar, S.Aruna Devi, R.Suresh Kumar

Paper Title:

Requirement Analysis Document in Google Drive for Green and Sustainable Software Engineering Approach

Abstract: This study shows how a requirement analysis can help to organizations become more environmentally sustainable in a structured and efficient manner, for this we have analyzed the Google Drive document as a requirement analysis document with the help of that document we try to cover the software requirement specification from the customer, then we try to observe the if that document located in desktop pc what is the cumulative processor energy, IA energy and GT energy, if that document shared with cloud environment minimum and maximum communication of resource sharing details are analyzed for user base and data center of various regions, finally the load event details are observed for the requirement document shared in the Google drive , This result show that the technologies delivers specific suggestions for improvement both on reducing the environmental foot print of ICT and on using ICT as a green solution for software requirement analysis process.

Keywords: Green ICT, IA Energy, GT Energy, Google Drive, Software Requirement Specification

References:

  1. Chitchyanet al., "Sustainability Design in Requirements Engineering: State of Practice," 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), Austin, TX, 2016, pp. 533-542.
  2. Mendez Fernandez, S. Wagner Naming the Pain in Requirements Engineering: Design of a Global Family of Surveys and First Results from GermanyIn: Proc. of the 17th International Conference on Evaluation and Assessment in Software Engineering (EASE ’13), ACM, 2013.
  3. Dhingra, Savithri G, M. Madan and Manjula R, "Selection of prioritization technique for software requirement using Fuzzy Logic and Decision Tree,"2016 Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, 2016, pp. 1-11.
    doi: 10.1109/GET.2016.7916822
  4. Umma Khatuna Jannat ,” Green Software Engineering Adaption In Requirement Elicitation Process”2016 international journal of scientific & technology research volume 5, issue 08, august 2016 issn 2277-8616
  5. Hankel and P. Lago, "How organisations can assess and improve their green ICT activities in a standard and efficient way," 2016 ITU Kaleidoscope: ICTs for a Sustainable World (ITU WT), Bangkok, 2016, pp. 1-6
  6. Erik Jagroep” Extending software architecture views with an energy consumption perspective Computing, 2017, Volume 99, Number 6, Page 553
  7. Paul p.k,”Is green computing a social software engineering domain?”,2016 international journal of applied science and engineering 4(2).PP.67-73
  8. Becker, Colin (2016) Requirements: The Key to Sustainability. IEEE Software, 33 (1). pp. 56-­65. ISSN 0740­7459
  9. C. Venters et al., "Characterising Sustainability Requirements: A New Species Red Herring or Just an Odd Fish?," 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS), Buenos Aires, 2017, pp. 3-12.
  10. Vivek Shukla, Dhirendra Pandey and Raj Shree. Article: Requirements Engineering: A Survey. Communications on Applied Electronics3(5):28-31, November 2015. Published by Foundation of Computer Science (FCS), NY, USA
  11. Becker, D. Walker and C. McCord, "Intertemporal Choice: Decision Making and Time in Software Engineering," 2017 IEEE/ACM 10th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), Buenos Aires, 2017, pp. 23-29
  12. Marimuthu K. Chandrasekaran," Software Engineering Aspects of Green and Sustainable Software: A Systematic Mapping Study". Proceedings of the 10th Innovations in Software Engineering Conference Jaipur, India 2017 PP[ 34-44]
  13. Komeil raisian,” Current Challenges And Conceptual Model of Green And Sustainable Software Engineering,” Journal of Theoretical and Applied Information Technology
    31st December 2016 -- Vol. 94. No. 2 – 2016
  14. Torre, G. Procaccianti, D. Fucci, S. Lutovac and G. Scanniello, "On the Presence of Green and Sustainable Software Engineering in Higher Education Curricula," 2017 IEEE/ACM 1st International Workshop on Software Engineering Curricula for Millennials (SECM), Buenos Aires, 2017, pp. 54-60.
  15. Rashid,” Developing Green and Sustainable Software Using Agile Methods in Global Software Development: Risk Factors for Vendors”,2017 Proceedings of the 11th International Conference on Evaluation of Novel Software Approaches to Software Engineering ENASE 2016 PP 247—253
  16. Maqbool Ahmed Muhammad Azeem,” Requirement Engineering the backbone of a project ”, 2017, researchgate.net/ publication/ 318787262,
  17. Reza, R. Sehgal, J. Straub and N. Alexander, "Toward model-based requirement engineering tool support," 2017 IEEE Aerospace Conference, Big Sky, MT, 2017, pp. 1-10.
  18. H. Khan, M. N. bin Mahrin and S. bt Chuprat, "Situational requirement engineering framework for Global Software Development," 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, 2014, pp. 224-229.
  19. Sitthithanasakul and N. Choosri, "Application of software requirement engineering for ontology construction," 2017 International Conference on Digital Arts, Media and Technology (ICDAMT), Chiang Mai, 2017, pp. 447-453.
  20. Aruna devi, Dr.Vijeta Iyer, “A Study on M/M/C Queueing Model under Monte Carlo Simulation in Traffic Model”, 2017, International Journal of pure and Applied Mathematics(IJPAM), No:12, vol 116, pg:199-207.
  21. http://sustainabilitydesign.org

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59.

Authors:

V. Jeyasudha, Satheesh Kumar KRP

Paper Title:

Study and Comparison of Steel Haunched and Tapered Beam

Abstract: Steel framed buildings are flexible, ductile and light weight compared to that of reinforced concrete buildings. Intense research had been conducted in the last decades regardingthe fatigue and ductility behaviour of structural steel beam. Prismatic beams are the beams with uniform cross-section in the entire span of the beam. Non-prismatic beams are used to increase the efficiency of the beam, by increasing the strength per unit mass than that of prismatic beam. In this study, the load-deformation, stress-strain analysis, the fatigue and ductile behaviour of non-prismatic steel beams with hunched and tapered ends was compared with prismatic beam for different loading condition. The beams were subjected to static loading conditions during analysis.

Keywords: Prismatic beam, non-prismatic beam, stress-strain analysis, static loading, fatigue and ductile behavior.

References:

  1. NimbalkarAmol N. and Laxman V. Awadhani. Experimental and Numerical Analysis of Trapezoidal Corrugated Web Beam to Determine its Strength and Mode Shapes. International Engineering Research Journal, 1955-1961.
  2. Anu Jolly, VidyaVijayan 2. (2015). Structural Behaviour of Reinforced Concrete Haunched Beam A Study on ANSYS and ETABS. International Journal of Innovative Science, Engineering & Technology, 3(8), 495-500.
  3. Abinayaa, A., Ramadevi, K. (2018). Analytical investigation of all - Steel buckling restrained braces. International Journal of Civil Engineering and Technology, 9(3),232-239.
  4. Premalatha, J., Manju, R., Senthilkumar, V. (2017). Seismic response of multistoreyed steel frame with viscous fluid-scissor jack dampers. International Journal of Civil Engineering and Technology, 8(8),289-312.

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60.

Authors:

PA.Prabakaran, G.L.Sathyamoorty, M.Adhimayan

Paper Title:

An Experimental and Comparative Study on Canal Lining Exploitation Geo Synthetic Material, Cement Mortar and Material Lining

Abstract: This project is principally supported water insufficiency, a serious cause for individuals for all functions chiefly for irrigation. to beat this and to boost the potency of water flow and discharge in canals, the lining ways for canals ought to be modified as less permeable , increase in velocity and discharge. Canal lining is that the method of reducing flow loss of irrigation water by adding an imperviable layer. Technological development and producing of recent materials helps in varied functions. One such issue was the event of geosynthetic materials that was wide employed in construction fields in conjunction with concrete or as a separate material because the replacement for concrete. we have a tendency to selected PVC geosynthetic material for lining the canal rather than concrete, brick masonry and traditional material lining for canals. we have a tendency to create a comparative study for 3 canal linings like PVC, brick masonry and material lining close to Pollachi of alittle paradigm model in Mr. Sekar farm and notice the foremost economical material appropriate for canal lining altogether forms

Keywords: canal analysis’s, Effective discharge, most economical-comparative study.

References:

  1. A report on “Studies on issues related on gap between
  2. Irrigation potential created and utilised”, IIM,Lucknow.
  3. A technical report on “Canal lining demonstration project” year 7durability report, September 1999
  4. Mishra et.al(2001), Hydraulic modeling of kangsabatimain canal for performance assessment, Journal of Irrigation and Drainage Engineering, Vol. 127, No. 1, January/February, 2001. Conference on geotextiles,Geomembranes and Related products”, Singapore,59.Pg 573-578.
  5. K. Rastogi(1992), FEM modelling to investigate seepage losses from the lined Nadiad branch canal, India, Journal of Hydrology,Elsevier,Vol.138,Issue1-2, sept.,1992, pages 153-168.
  6. B J Batliwala,J N Patel,P D Porey,2014, “Seepage Analysis of Kakrapar Right Bank Main Canal of KakraparProject, Gujarat,India” IJSRD, Vol11
  7. Charles M. Burt et.al(2010), Canal Seepage Reduction by Soil Compaction , Journal of Irrigation and Drainage Engineering, Vol. 136, No. 7, July 1, 2010. ©ASCE
  8. David McGraw et.al(2011), Development of tools to estimate conveyance losses in the Truckee River, USA ,Hydrogeology Journal Springer-Verlag 2011 Economic Analysis Guidebook,Department of Water Resource, California.
  9. ErhanAkkuzu et.al(2007), Determination of Water Conveyance Loss in the Menemen Open Canal Irrigation Network, Turk J Agric For 31 (2007) 11-22 c TUB‹TAK
  10. ErhanAkkuzu1 (2011), The Usefulness of Empirical Equations in Assessing Canal Losses Through Seepage in Concrete-Lined Canal, Journal of Irrigation and Drainage Engineering. /(ASCE)IR.1943-4774.0000414.
  11. Eric Leigh et.al, (2002), Seepage Loss Test ResultsIn Cameron County Irrigation District No. 2, Report Prepared for Cameron County Irrigation District No. 2 by Eric Leigh and Guy Fipps, P.E.2 in December 18, 2002
  12. Garg SK, Irrigation And hydrolic structure by Khanna Publishers 2006
  13. J. McGowen1(2001), Identifying channel seepage using pre-dawn thermal imagery, Geoscience and Remote Sensing Symposium, IEEE 2001,On page(s): 1631-1633 vol.4
  14. P. Giroud,J.G. Zornberg, and A. Zhao,7 October 2000,“Hydraulic Design of Geosynthetic and Granular Liquid Collection Layers”Geosynthetics International is published by the Industrial FabricsAssociation International, Special Issue on Liquid Collection Systems, Vol. 7, Nos. 4-6, pp. 285-380.
  15. I.Comer, September1994, “Water Conservation strategies using Geosynthetic.Fifth International

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Authors:

V.Vanitha, V.P.Sumathi, R.Kalaiselvi

Paper Title:

Automatic Ticket Validation System for Indian Railways

Abstract: Railway system places a vital role in public transportation. Railways are widely used commutation by the public. There are many services provided by it like a ticket, catering, etc. Ticketing system has evolved from paper ticketing system to electronic ticketing system. In a metro train, the system provides smart card where a passenger can recharge and buy tickets using that, this may be regular or seasonal ticket smart card which will calculate the fare for travel. In order to book tickets for long distance travel, passengers can buy e-ticket via an internet or can in person at railway stations. In case of e-ticket, the tickets would be validated by ticket examiner (TTE) with a valid original identity proof. Passengers will be in trouble if they forget to take the ID card. Lack of Ticket Examiner leads minimal verification of the passenger's ticket. In the proposed system by the use of online services with the internet, passengers can add their own unique national Identity proof (Aadhaar card) while booking tickets, which helps automatic ticket validation. Biometric checks of the passenger take place at the entrance and exit of each compartment of the train. With the help of cloud storage, the details can be validated by comparing Aadhaar database. In case of mismatching tickets alarm rings and alert message will be sent to Ticket Examiner. Using GPS on a train, location can be obtained and the source and destination of the passenger can be validated. Checking at the exit path, the destination can be checked and can avoid the persons travelling long distance with short distance travelling ticket. Also, the system prevents the person travels without buying tickets. The proposed system is implemented using Raspberry Pi, fingerprint scanner and GPS Receiver.

Keywords: Indian Railways, automatic ticket validation, biometric checking, Aadhaar data base.

References:

  1. M. D, A. K. Scariah, L. R. Pannapara, M. Jessica, and J. Joseph, “Smart Ticketing System for Railways in Smart Cities using Software as a Service Architecture,”International conference on I-SMAC (IoT in Social,Mobile,Analytics and cloud) pp. 828–833, 2017.
  2. Chen, Z. Zhou, and J. Zhang, “Railway Passenger Service Mode on ‘ Internet + ,’” Springer International Publishing AG 2018 Advances in smart vehicular technologyvol. 3, no. 2016.
  3. Arnone, T. Delmastro, G. Giacosa, M. Paoletti, and P. Villata, “The Potential of E-ticketing for Public Transport Planning: The Piedmont Region Case Study,” Transp. Res. Procedia, vol. 18, no. June, pp. 3–10, 2016.
  4. He, Y. He, and M. M. Tentzeris, “Modeling, design and experimentation of a UHF RFID tag antenna embedded in railway tickets,” IEEE Antennas Propag. Soc. AP-S Int. Symp., vol. 2015–October, pp. 1416–1417, 2015.
  5. Yang, J. Zhou, D. Fan, and H. Lv, “Design of intelligent recognition system based on gait recognition technology in smart transportation,” Multimedia. Tools Appl., vol. 75, no. 24, pp. 17501–17514, 2016.
  6. Karthick and A. Velmurugan, “Android suburban railway ticketing with GPS as ticket checker,” Proc. 2012 IEEE International Conference Advanced Communication Control Computing Technoogy. ICACCCT 2012, no. 978, pp. 63–66, 2012.
  7. Patil, “An Intelligent Ticket Checker Application for Train using QR Code,” National Conference on Advancements in Computer & Information Technology pp. 15–20, 2016.
  8. Li et al., "Client/server framework-based passenger line ticket system using the 2-D barcode on a mobile phone," Proc. Int. Conf. E-bus. E-Government, ICEE 2010, pp. 97–100, 2010.
  9. M. Swarup, A. Dwivedi, C. Sonkar, R. Prasad, M. Bag, and V. Singh, “A QR Code Based Processing For Dynamic and Transparent Seat Allocation in Indian Railway,”IJCSI International Journal of Computer Science Issuesvol. 9, no. 3, pp. 338–344, 2012.
  10. Tanwar, A. K. Nazari, V. Deep, and N. Garg, “Railway Reservation Verification by Aadhar Card,”International Conference on Computational Modelling and IssuesProcedia Comput. Sci., vol. 85, no. Cms, pp. 970–975, 2016.
  11. Meenakumari, “Enhanced &Integrated E-Ticketing-An One Stop Solution,” International Journal of Advanced Research on Computer Science and Management Studies, vol. 7782, pp. 400–403, 2015.
  12. Jadhav, P. Dolas, M. Kurrey, K. Dhawale, and P. C. V Rane, “Railway Ticket Scanner System,” International Journal of Advanced Research of Computer and Communication Engineering vol. 5, no. 3, pp. 36–37, 2016.
  13. Chandrappa, D. Lamani, S. Vital, and N. U. Meghana, “Automatic Control of Railway Gates and Destination Notification System using Internet of Things ( IoT ),” I.J. Education and Management Engineering no. September, pp. 45–55, 2017
  14. L. Ghìron, S. Sposato, C. M. Medaglia, and A. Moroni, “NFC ticketing: A prototype and usability test of an NFC-based virtual ticketing application,” Proc. - 2009 1st Int. Work. Near F. Commun. NFC 2009, pp. 45–50, 2009.
  15. Fan et al., “NFC Secure Payment and Verification Scheme with CS E-Ticket,”Hindawi Security and Communication Networks vol. 2017, 2017.
  16. -P. Pelletier, M. Trépanier, and C. Morency, “Smart card data use in public transit: A literature review,” Transp. Res. Part C Emerg. Technol., vol. 19, no. 4, pp. 557–568, 2011.
  17. Janani R and Vanitha V “A Survey on Smart Ticketing and Verification System for Indian Railways” Fifth International Conference on Current Trends in Engineering & Technology
  18. Vanitha, V.P.Sumathi, J.Cynthia and B.Illakia, “NEXT GENERATION VEHICLE DIAGNOSTIC SYSTEMS”, International Journal of Pure and Applied Mathematics, Volume 116 No. 11, 2017, 251-259
  19. Suganthi N, Arun R, Saranya D and Vignesh N published a paper titled “Smart Security Surveillance Rover” in International Journal of Pure and Applied Mathematics, Vol. 116, No.12, 2017, 67-75.

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62.

Authors:

Rameswari.R, Divya.N

Paper Title:

Smart Health Care Monitoring System Using Android Application: A Review

Abstract: The innovation is changing the scene of the world and driving us towards a down to earth specialized world. The rising part of ICT and IOT has made an enormous effect on human services. It enhances the nature of care, builds the patient security and information insurance and limit working and regulatory cost. The media transmission gadgets are easier to understand and utilized by everybody around the globe which have decreased the correspondence hole to a zero level. The paper clarifies a portion of the correspondence innovation and the particular conventions for moving the information in a protected way (i.e.,) how the imperative indications of patients of patients of patients are sent to the medical consultant for the further treatment.

Keywords: IoT, ICT, Media transmission, Conventions, Information assurance, Security.

References:

  1. Ayaskanta Mishra, Biswarup Chakraborty,” AD8232 based Smart Healthcare System using Internet of Things (IoT),”International Journal of Engineering Research & Technology (IJERT) ,Vol. 7 Issue 04, April-2018 .
  2. Ayaskanta Mishra, AkankshaKumari, PoojaSajit, PranjalPandey,”Remote Web Based Ecg Monitoring Using MQTT Protocol For IOT In Healthcare,” International Journal Of Advance Engineering And Research Development Volume 5, Issue 04, April -2018.
  3. BhaskarNiraghatam,M V Ramanamurthy.” Heart Beat Monitoring System And Security Using Android,” International Journal of Advanced Research in Computer Science Volume 8, No. 7, July – August 2017.
  4. Harini, B. Rama Murthy , K.Tanveer Alam,”Development Of ECG Monitoring System Using Android App,” International Conference on Emerging Trends in Engineering , Science And Management 17th And 18th March 2017.
  5. Gaurav Raj, Neelam Rup Prakash, Jagjit Singh Randhawa.”IoT Based EMG Monitoring System,”International Research Journal of Engineering And Technology (IRJET), Volume: 04 Issue: 07 July -2017.
  6. Hao-Yun Kao, Chun-Wang Wei, Min-Chun Yu, Tyng-Yeu Liang, Wen-HsiungWu,Yenchun Jim Wu.” Integrating a Mobile Health Applications for Self-Management to enhanceTelecare System,”Telematics and Informatics 35 (2018) 815–825.
  7. Godavarthi Rajesh, M.K. Srilekha.”Advanced Healthcare Monitoring System Using Cc3200microcontroller,” International Journal Of Pure And Applied Mathematics Volume 115 No. 8 2017, 419-424.
  8. SpurthyTalakala, M.Hari Krishna.” Instantaneous Health Care Monitoring System d Smart Phone,” Journal Of Electronic Control Systems And Control Instrumentation Engineering Volume 2 Issue 2
  9. Devashri Deshmukh1, Ulhas B. Shinde2, Shrinivas R. Zanwar3 .” Android Based Health Care Monitoring System,” International Journal Of Advance Scientific Research And Engineering Trends Volume 2 ,Issue 7 ,Jan 2017.
  10. .FarahNasri, AbdellatifMtibaa.,” Smart Mobile Healthcare System Based On WBSN And 5G,” International Journal Of Advanced Computer Science And Applications, Vol. 8, No. 10, 2017.
  11. SarfrazFayaz Khan.” Health Care Monitoring System In Internet Of Things (Lot) By Using RFID,” 2017 The 6th International Conference On Industrial Technology And Management.
  12. .Prashant Salunke1 | Rasika Nerkar2,”Iot Driven Healthcare System For Remote Monitoring Of Patients,”International Journal For Modern Trends In Science And Technology Volume: 03, Issue No: 06, June 2017.
  13. .OlutayoBoyinbode.” A Cloud-Based Body Area Sensor Network Mobile Healthcare System,” International Journal OfAdvanced Research In Computer Science And Software EngineeringVolume 7, Issue 5, May 2017.
  14. .Higinio Mora ID , David Gil ID , Rafael Muñoz Terol D , Jorge Azorín,D AndjulianSzymanski.” An Iot-Based Computational Framework For Healthcare Monitoring In Mobile Environments,”Sensors 2017.
  15. Y, Lavanya.M, Mounika.A, Sasirekha.KN.VignaVinod Kumar.” A Secure Iot-Based Modern Healthcaresystem Using Body Sensor Network,” International Journal Of Innovative Research In Science, Engineering And Technology, Volume 6, Special Issue 3, March 2017.
  16. .Ranjeet Kumar, RajatMaheshwari, AmitAggarwal, M. ShanmugasundaramAnd Sundar S.” Iot Based Health Monitoring System Using Android App,” ARPN Journal of Engineering and Applied Sciences Vol. 12, No. 19, October 2017.
  17. Syed Muhammad Waqas Shah, Maruf Pasha,” IoT-Based Smart Health Unit,”Journal of Software, Volume 12, Number 1, January 2017.
  18. AhmedImteaj and Muhammad KamrulHossain.” A Smartphone based Application to Improve theHealth Care System of Bangladesh,”
  19. Won-Jae Yi, JafarSaniie,Patient Centered Real-Time Mobile HealthMonitoring System,” E-Health Telecommunication Systems and Networks, 2016, 5, 75-94
  20. Joon-Soo Jeong, Oakyoung Han2 And Yen -You You.”A Design Characteristics Of Smart Healthcare System As The Iot Application,” Indian Journal Of Science And Technology, Vol 9(37), October 2016.
  21. Hanqing Chao, Yuan Cao, Junping Zhang, , Fen Xia, Ye Zhou, and Hongming Shan.”Population Density-based Hospital Recommendation with Mobile LBS Big Data.”2018 IEEE International Conference on Big Data and Smart Computing.
  22. Edison R. Valencia-Nuñez, Hamilton V. Montenegro López, Lorenzo J. Cevallos-Torres,” Probabilistic Model for Managing the Arrival Timesof Pre-Hospital Ambulances Based on theirGeographical Location (GIS),” 2018 IEEE.
  23. .Maradugu Anil Kumar, Y.RaviSekhar,” Android Based Health Care Monitoring System,” IEEE Sponsored 2nd International Conference On Innovations In Information Embedded And Communication Systems ICIIECS'15.
  24. Muhammad WasimMunir, Syed Muhammad Omair, M.ZeeshanUlHaque,” An Android based Application for Determine a Specialized Hospital Nearest to Patient's Location,” International Journal of Computer Applications (0975 – 8887) Volume 118 – No. 9, May 2015.
  25. .Daryl Abel, BulouGavidi, Nicholas Rollings and Rohitash Chandra,” Development of an Android Application for anElectronic Medical Record System in an Outpatient Environment for Healthcare in Fiji,” Technical Report, Aicrg, Software Foundation, Fiji, March 2015.

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63.

Authors:

Jenice Aroma R, Syed Ali Fathima S.J, Raniya Harini R

Paper Title:

A Short Investigation on Effective Spectral Properties of Multispectral and Hyper Spectral Images for Object Detection

Abstract: The Satellite based imaging system which is based on a network of artificial satellites is more efficient for remote monitoring of our ecosystem. It provides geospatial positioning and high precision information regarding the local time which can be used in global positioning, air and sea traffic and so on. Effective monitoring of our ecosystem has been achieved through remote sensing which extracts even fine spatial details of the earth, thus producing an image with good resolution for better clarity to be analyzed. This paper discusses about two different variants of satellite imaging on spatial objects which varies with the spatial significance. These satellite imaging instruments are primarily stresses on the pixel count for ejecting an accurate and more specific image. Increase in spatial resolution produces an accurate and precise overview about the chosen spatial entity thereby supporting the data collection technology and offers effective data interpretation. This can bring out better discrimination among the various resolution strategies and their relevance to a specific need.

Keywords: air and sea traffic and so on.

References:

  1. Remote Sensing - [Online]: https://www.usgs.gov/faqs/what-remote-sensing-and-what-it-used-0?qt-news_science_products=7#qt-news_science_products
  2. Shefali Aggarwal, "Principles of Remote Sensing, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology", Proceedings of the Training workshop, pp. 39-65, 2003.
  3. Resolution of Remote Sensing - [Online]: edc.uri.edu/nrs/classes/NRS409/RS/Lectures/HowRemoteSensonWork.pdf
  4. Jenice Aroma R, Kumudha Raimond , "An Overview of Technological Revolution in Satellite Image Analysis", Journal of Engineering Science and Technology Review ,9 (4), 1- 5, 2016.
  5. SWIR- [Online]: https://www.geoimage.com.au/SWIR%20Series/resolution
  6. Menglong Li, HuanXie, Weidong Wang, Xiuhua Li, Chaowei Wang, Yinghai Zhang , "An Advanced Anti-Collision Algorithm Based on Inter-Tag Communication Mechanism in RFID- Sensor Network", Mobile Ad Hoc and Sensor Systems (MASS), 2015.
  7. Satellite Navigation - [Online]: https://www.techopedia.com/definition/30440/satellite-navigation
  8. Aseffa M. Melesse, Qihaoweng, Prasad S. Thenkabail and Grabriel B. Senay, "Remote sensing sensors and application in environmental resource mapping and modelling", Sensors, 7(12), 2007.
  9. Multispectral vs. Hyperspectral- [Online] :iirs.gov.in/iirs/sites/default/files/StudentsThesis/VARUN_Mtech_2013-15.pdf
  10. Lanaras, E. Baltsavias, K. Schindler, "Advances in hyperspectral and multi spectral image fusion and spectral unmixing", MDPI Sensors, 2007.
  11. Spatial resolution in Digital Images - [Online]: http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/-
  12. High (spatial) Resolution vs. Low Resolution Images - A Planner’s viewpoint –Mahavir.
  13. Jonathan R. B. Fisher, Eileen A. Acosta, P. James Dennedy-Frank, Timm Kroeger & Timothy M. Boucher, "Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality", .
  14. Sarah A. Boyle,Christina M. Kennedy, Julio Torres, Karen Colman, Pastor E. Pérez-Estigarribia, Noé U. de la Sanch , "High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology", PLOS ONE, 2014 .
  15. Very high resolution satellite data - [Online]: http://earth.space/blog/satellite-data-need-geoscience-application/
  16. About high resolution satellite images - [Online]: http://dhi-gras.com/products/satelliteimages
  17. Quick-bird - [Online]: http://glcf.umd.edu/data/quickbird/description.shtml
  18. GEOEYE-1 – [Online]: https://spacedata.copernicus.eu/web/cscda/missions/rapideye
  19. IKONOS - [Online]: http://www.euspaceimaging.com/satellites/ikonos
  20. RAPIDEYE - [Online]: https://spacedata.copernicus.eu/web/cscda/missions/rapideye
  21. RAPIDEYE - [Online]: http://www.harrisgeospatial.com/DataImagery/SatelliteImagery/MediumResolution/RapidEye.aspx
  22. PLEIADES - [Online]: https://pleiades.cnes.fr/en/PLEIADES/GP_applications.htm
  23. SPOT-5 - [Online]: https://www.geoimage.com.au/satellite/spot-5
  24. Hyperspectral Remote Sensing – [Online]: http://www.csr.utexas.edu/project/rs/hrs/hyper.html
  25. Multispectral vs. Hyperspectral Imagery Explained - [Online] : http://gisgeography.com/multispectral-vs-hyperspectral-imagery-explained/
  26. Emma Underwood, Susan Ustin, Deanne DiPietro, "Mapping non-native plants using hyperspectral imagery", Remote Sensing of Environment, Vol. 86, 2003.
  27. Tapas R. Martha, K.Babu Govindharaj, K. Vinod Kumar, Damage and geological assessment of the 18 September 2011 Earthquake in Sikkim, India using very high resolution satellite data, Geoscience Frontiers, 2014.
  28. Hyperion – [Online]: https://e01.usgs.gov/sensors/hyperion
  29. Hyperspectral Imagery - [Online]: https://www.hyspex.no/hyperspectral_imaging/
  30. Eyal Ben Dor, Tim Malthus, Antonio Plaza, and Daniel Schlapfer, Hyperspectral Remote Sensing 2, 15-18.
  31. Staen , Terrestrial Imaging Spectroscopy – Some Future Perspectives.
  32. Jenice Aroma R and Kumudha Raimond, "A Review on availability of Remote Sensing Data", TIAR 2015, 150-155.
  33. Google Earth Explorer - [Online]: https://earthexplorer.usgs.gov/.
  34. Senthil S, Suguna M, and Cynthia .J,” mapping the vegetation soil and water region analysis of tuticorin district using landsat images”, International Journal on Innovations in Engineering Sciences and Technology, 3(01),2018.

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64.

Authors:

Nalini Nagendran, Kriti Asrani, Devaki P

Paper Title:

An Online Question & Answer Platform

Abstract: (A Question and Answer Software is a software that focuses on answering the questions one might put up on an online platform. These are usually implemented by large organizations which aim on implementing a platform where users can clear their doubts about their respective fields. It varies from small scale to large scale or from topic to topic. Many of these platforms may restrict access to either their employees or make it a public one. The disadvantages of the existing systems are less security, searching efficiency is less, chances of faking answer. One may access these sites/applications from another's system in case they are not public or access the ones which are and post answers which aren't relevant. Sometimes people even lie on such platforms and there might not be any checker to cross-analyze these answers. Now one might also want similar answers to their question which may resolve their query beforehand. Problems which are undertaken are first, making sure that there is more security and safety. Second, helping the users to search answers for similar questions which may answer their question beforehand and even highlight other important points worth knowing. There had to be a way to find if the answers are worth trusting. One can’t just blindly trust anything they read on the internet. They either look for other users who've said the same thing or maybe a trustworthy person like an expert. So in order to resolve these problems, the software created focuses on ensuring that a user has to make an account in order to access the website. Both users and experts can make their accounts and help out people with their queries. Third, there would be a similarity check that would allow the person to review similar questions and get more information. Lastly, this is a system which allows you to grade the answer you read with respect to how much it helped a person so others can trust the answer and its eligibility and see if it’s legit. One can find a number of such platforms, varying from technical to a know-all domain. Quora or Yahoo! Answers are standalone Question and Answer Softwares and along with Stack Overflow, Qhub, and they all are open source.)

Keywords: Software, questions, answers, platform, similar, query.

References:

  1. R. Morris, J. Teevan, and K. Panovich. A Comparison of Information Seeking Using Search Engines and Social Networks. In In Proc. of ICWSM, 2010.
  2. R. Morris, J. Teevan, and K. Panovich. What do People Ask Their Social Networks, and Why? A Survey Study of Status Message Q&A Behavior. In Proc. of CHI, 2010.
  3. Gyongyi, G. Koutrika, J. Pedersen, and H. Garcia-Molina. Questioning Yahoo! Answers. In Proc. of QAWeb, 2008.
  4. Yahoo! Answers Team. Yahoo! Answers BLOG. http://yahooanswers.tumblr.com
  5. Li and I. King. Routing Questions to Appropriate Answers in Community Question Answering Services. In Proc. of CIKM, 2010.
  6. Reed, M. G., Syverson, P. F., & Goldschlag, D. M. (1998). Anonymous connections and onion routing. IEEE Journal on Selected areas in Communications, 16(4), 482-494.

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Authors:

Nalini Nagendran, Ashwini Kolhe

Paper Title:

Security and Safety With Facial Recognition Feature for Next Generation Automobiles

Abstract: This is the era of automated cars or self-driving cars. All car vendors are trying to come up with different advancements in the cars (Like Automatic car parking, Automatic Lane changing, automatic braking systems, android auto, car connect, Vehicle to external environment technology etc). In the automation industry, TESLA, Google and Audi are the most competent leader among each other as well as for other automation business also. Modern vehicles are all equipped with different technologies like navigation system, driver assistant mode, weather mode, Bluetooth, and other safety features which brings broader impact to quality of human’s life, environmental sustainability. This paper explains how the proposed feature, unlocks the semiautonomous cars or autonomous cars safely and provides the safety to the entry level cars. The acknowledged pictures are put away in the picture database amid confront acknowledgment by utilizing Support Vector Machine (SVM) classifier. Information from confront pictures through picture pressure utilizing the two-dimensional discrete cosine change transformation (2D-DCT). A self-arranging map (SOM) utilizing an unsupervised learning method is utilized to order DCT-based element vectors into gatherings to distinguish if the picture is "available" or "not available" in the picture database. The face is detected by the event that the framework perceives faces, only the authentic users are able to start the ignition of the car and untheorized users are not allow to start the ignition.

Keywords: Face detection, Controller, Autonomous vehicles, safety , new feature , driverless cars , SVM

References:

  1. Hteik Htar Lwin, Aung Soe Khaing, Hla Myo Tun “Automatic Door Access System Using Face Recognition” INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 4, ISSUE 06, JUNE 2015 ISSN 2277-8616 294
  2. Zhaoxia Zhu , Fulong Chen “Fingerprint Recognition-Based Access Controlling System for Automobiles” 2011 4th International Congress on Image and Signal Processing
  3. Yongmin Lia,*, Shaogang Gongb , Jamie Sherrahc , Heather Liddellb “Support vector machine based multi-view face detection and recognition” Y. Li et al. / Image and Vision Computing 22 (2004) 413–427
  4. Unnati A. Patel, Dr. Swaminarayan Priya R. « Development of Student Attendence Management System Using RFID and Face Recognition : A Review “International Journal of Advance Reasearch in Computer Science And Management Stsudies. Vol. 2, issue 8,August 2014
  5. Yeka Joseph Abueh and Hong Liu “Message Authentication in Driverless Cars” 978-1-5090-0770-7/16/$31.00 ©2016 IEEE
  6. Keshav Bimbraw “Autonomous Cars: Past, Present and Future”IEEE
  7. Brian Markwalter “The Path to Driverless Cars The Path to Driverless Cars” Digital Object Identifier 10.1109/MCE.2016.2640625 APRIL 2017 ^ IEEE Consumer Electronics Magazine
  8. William B. Rouse “The Systems, Man, and Cybernetics of Driverless Cars”
  9. Paul Viola, Michael J. Jones, Robust Real-Time Face Detection, International Journal of Cumputer Vision 57(2), 2004.
  10. Ayushi Gupta, Ekta Sharma, NehaSachan and Neha Tiwari. Door Lock System through Face Recognition Using MATLAB. International Journal of Scientific Research in Computer Science and Engineering, Vol-1, Issue-3, 30 June 2013.
  11. Yugashini, S. Vidhyasri, K.Gayathri Devi, Design And Implementation Of Automated Door Accsessing System With Face Recognition, International Journal of Science and Modern Engineering(IJISME), Volume-1, Issue-12, November 2013.
  12. Liton Chandra Paul, Abdulla Al Sumam. Face Recognition Using Principal Component Analysis Method. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET),Volume 1, Issue 9, November 2012
  13. Liam Ellis1, Nicolas Pugeault2, Kristoffer O¨ fja¨ll1, Johan Hedborg1, Richard Bowden2, Michael Felsberg1 “ Autonomous Navigation and Sign Detector Learning ” 978-1-4673-56478-03/123/$31.00 ©20123 IEEE
  14. Young-Hwan Lee, Toun gseop Kim, Heung-jun Kim , In Kyoung Shin,Hyochang Ahn,YuKyong Lee “ Mdofied Active Shape Model for Realtime Facial Feature Tracking on iPhone ” 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
  15. Shaif Choudhury, Soummyo Priyo Chattopadhyay, Tapan Kumar Hazra “Vehicle Detection and Counting using Haar Feature-Based Classifier” 978-1-5386-2215-5/17/$31.00 ©2017 IEEE
  16. Chapelle, O. (1998). Support Vector Machines et Classification d'Images.

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66.

Authors:

T.K. Chandru, M. Dinesh Kumar, S. Karthikeyan, K. Saranya

Paper Title:

Interactive Coding Platform for Students

Abstract: Programming has become one of the most demanded skill of a working professional in almost every industry. Even though we have a lot of platform to work on and learn from, we are not properly trained in this domain . This has increased the need for a platform that is targeted only for the colleges students to develop a coding culture among them , right from the start. The project that we aim to develop solves this particular issue and will also enhance the skills of the students by continuous feedback learning. The end-product will be a web application which the teachers can use to set problems and give assignments while the students will use the application to solve the assignments. The application will be developed using : VueJS in the front-end , the database will be MongoDB and the back-end will be composed of ExpressJS and NodeJS entities. Thus, MEVN is the technology stack on which the web application will be built because most of the operations in the project will be I/0 based and NodeJS is the perfect tool to handle asynchronous calls. The data will be transferred in the form of a JSON contract for easy interpretation. The web application will be composed of REST api endpoints for performing various operations. The application will be built on Micro Services Architecture to support modularity, scalability and ease of use. Some of the features provided by the application are performance comparison of the students, customizable test environment, compilation and execution of the code, cloud storage for sensitive data and support for many languages. Thus, this web application will solve the critical need for skills that are to be possessed by the individuals graduating out of the college as demanded by the IT industry.

Keywords: Programming, Micro services, MEVN stack, REST

References:

  1. Jürgen Hausladen, Birgit Pohn, Martin Horauer, "A cloud-based integrated development environment for embedded systems", Mechatronic and Embedded Systems and Applications (MESA) 2014 IEEE/ASME 10th International Conference on, pp. 1-5, 2014.
  2. Shih-Chieh Su, Chih-Chang Yu and Chan-Hsien Lin, "Development of a web-based programming learning platform," 2016 International Conference on Fuzzy Theory and Its Applications (iFuzzy), Taichung, 2016, pp. 1-1.
  3. Anuradha Kanade , Arpita Gopal and Shantanu Kanade , “A study of normalization and embedding in MongoDB”,Gurgaon , 27 March 2014., 134-139.
  4. Thung, T. F. Bissyandé, D. Lo and L. Jiang, "Network Structure of Social Coding in GitHub," 2013 17th European Conference on Software Maintenance and Reengineering, Genova, 2013, pp. 323-326.
  5. Ning Zhang, Tianmei Wang, Shuyun Zhang and Xuefeng Li, "Platform construction and implementation of software development course group," 2011 International Conference on Computer Science and Service System (CSSS), Nanjing, 2011, pp. 3372-3375.
  6. Andrew John Poulter , Steven J. Johnston and Simon J.Cox,”Using the MEAN stack to implement a RESTful service for an Internet of Things”, Milan, Italy, 14-16 Dec.2015,pp. 10-12.

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67.

Authors:

Raju C, Krishnamoorthi M

Paper Title:

A Comprehensive Survey on Virtual Migration Techniques in Cloud Computing

Abstract: In recent growth of business challenges the landscape of the cloud computing under major change. Especially the management of datacenter is challenging task because of its increase in capital and operational expenses due to the spending on its workforce. The paramount importance of any data center is to reduce energy cost and computation cost by effectively utilizing the available physical machines. Virtualization is a key technology in datacenter to provide demand specific virtual resources as per application requirement. Migration helps in avoiding under utilization of resources also in overloaded conditions from the flood of requests coming in these days. The trend in choosing the right migration techniques in cloud data center depends on the nature of the deadline it follows from the utilization of the resources. The goals in virtual migration are the effective management in load balancing of servers, reduced power consolidation of server by right model, faster recognition to the server failure and minimizing the overall system maintenance. In this paper, we discuss some of the latest techniques used in the virtual migration based on their key performance metric like total transfer data, migration time and total down time.

Keywords: Cloud Computing, Migration, Pre-Copy, Post-Copy and Hybrid Copy.

References:

  1. Lei Shi, Yi Shi, Xing Wei, Xu Ding, and Zhenchun Wei “Cost Minimization Algorithms for Data Center Management”, IEEE Transactions on parallel and distributed systems, vol. 28, no. 1, January 2017
  2. Gandhi, V. Gupta, M. Harchol Balter, and A. Kozuch, “Optimality analysis of energy-performance trade-off for server farm management,” Elsevier Perform. Eval., vol. 67, no. 11, pp. 1155–1171, Nov. 2010.
  3. W. Ahmad et al., “A survey on virtual machine migration and server consolidation frameworks for cloud data centers,” J. Netw. Comput. Appl., vol. 52, pp. 11–25, Jun. 2015.
  4. R Yu, Y Zhang, S Gjessing, W Xia, K Yang, “Toward cloud-based vehicular networks with efficient resource managementIEEE Network (Volume: 27, Issue: 5, September-October 2013
  5. Zhang, X. Fu, and R. Yahyapour, “Layermover: Storage migration of virtual machine across data centers based on three-layer image structure,” in Proc. IEEE 24th Int. Symp. Modeling Anal. Simulat. Comput. Telecommun. Syst. (MASCOTS), London, U.K., 2016, pp. 400–405.
  6. Samadi, J. Xu, and K. Bergman, “Virtual machine migration over optical circuit switching network in a converged inter/intra data center architecture,” in Proc. Opt. Fiber Commun. Conf. Exhibit. (OFC), Los Angeles, CA, USA, 2015, pp. 1–3.
  7. W. Ahmad et al., “Virtual machine migration in cloud data centers: A review, taxonomy, and open research issues,” J. Super Comput., vol. 71, no. 7, pp. 2473–2515, 2015.
  8. Liu, H. Jin, C.-Z. Xu, and X. Liao, “Performance and energy modeling for live migration of virtual machines,” Cluster Comput.,vol. 16, no. 2, pp. 249–264, 2013.
  9. Kapil, E. S. Pilli, and R. C. Joshi, “Live virtual machine migration techniques: Survey and research challenges,” in IEEE 3rd Int. Adv. Comput. Conf. (IACC), 2013, pp. 963–969.
  10. Wood, “Improving data center resource management, deployment, and availability with virtualization,” Ph.D. dissertation, Dept. Comput. Sci., Univ. Massachusetts at Amherst, Amherst, MA, USA, 2011.
  11. Opara-Martins, R. Sahandi, and F. Tian, “Critical review of vendor lock-in and its impact on adoption of cloud computing,” in Proc. Int. Conf. Inf. Society (i-Soc.), London, U.K., 2014, pp. 92–97.
  12. Fei Zhang , Guangming Liu, Xiaoming Fu, Ramin Yahyapour, “A Survey on Virtual Machine Migration:
  13. Challenges, Techniques, and Open Issues, IEEE Communications Surveys & Tutorials, Vol. 20, No. 2, Second Quarter 2018.
  14. G. J. Leelipushpam and J. Sharmila, “Live VM migration techniques in cloud environment—A survey,” in Proc. IEEE Conf. Inf. Commun. Technol. (ICT), 2013, pp. 408–413.
  15. Shribman A, Hudzia B Pre-Copy and post-copy VM live migration for memory intensive applications. In: Proceedings of the Euro-Par 2012: Parallel Processing Workshops: Springer; 2013. p. 539–47.
  16. Xu et al., “iAware: Making live migration of virtual machines interference-aware in the cloud,” IEEE Trans. Comput., vol. 63, no. 12, pp. 3012–3025, Dec. 2014.
  17. Treutner and H. Hlavacs, “Service level management for iterative pre-copy live migration,” in Proc. 8th Int. Conf. Netw. Service Manag., Las Vegas, NV, USA, 2012, pp. 252–256.
  18. R. Hines, U. Deshpande, and K. Gopalan, “Post-copy live migration of virtual machines,” ACM SIGOPS Oper. Syst. Rev., vol. 43, no. 3, pp. 14–26, 2009.
  19. Hu, L., Zhao, J., Xu, G., Ding, Y., Chu, J.,” live virtual machine migration based on hybrid memory copy and delta compression.” Appl. Math. Inf. Sci. 7 (2L), 639–646, 2013
  20. Jin, H., Deng, L., Wu, S., Shi, X., Pan, X., “ Live virtual machine migration with adaptive, memory compression” IEEE International Conference on Cluster Computing and Workshops, pp. 1–10, 2009
  21. Riteau, P., Morin, C., Priol, T.. “ improving live migration of virtual clusters over WANs with distributed data deduplication and content-based addressing” In: Proceedings of the 17th International Conference on Parallel Processing - Volume Part I, Euro-Par’11. Springer-Verlag, pp. 431–442,2011
  22. Knauth, T., Fetzer, C., “Vecycle: recycling vm checkpoints for faster migrations.” In: Proceedings of the 16th Annual Middleware Conference, Middleware ’15. ACM, pp. 210–221,2015

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68.

Authors:

S. Sathyavathi, K. R. Baskaran, S. Kavitha

Paper Title:

Non-Invasive Diabetes Mellitus Detection Using Facial Block Color

Abstract: Diabetes Mellitus (DM) is a condition in which glucose level in the body is much higher than the normal. The traditional way to diagnosis DM is Fasting Plasma Glucose (FPG) test. As this method is slightly painful and uncomfortable several another method which are more comfortable and non-invasive are found. In this paper, we propose a new non-invasive method to detect DM based on facial block color features using various classification algorithms. Facial images are first captured using a specially designed non-invasive device, and calibrated to ensure consistency in feature extraction and analysis. Four facial blocks are extracted automatically from face image and used to represent a face features. A facial color gamut is constructed with six color centroids (red, yellow, light yellow, gloss, deep red, and black) to compute a facial color feature vector, characterizing each facial block. Finally, the features are classified using J48. ForJ48, two sub dictionaries, a Healthy facial color features sub dictionary and DM facial color features sub dictionary, are employed in the classification process. Apart from this we also use ZeroR, Support vector machine (SVM)[8] ,J48 to determine the accuracy, precision and recall using the data set that comprises of healthy and DM samples. Finally, we compare all these algorithms and choose the efficient one using its accuracy level.

Keywords: Non-Invasive, Algorithm Efficiency, Health Enhancement

References:

  1. Ganesh , A. Yang, J. Wright, S. Sastry and Y. Ma “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, Feb. 2009.
  2. Zhang and X. Wang, “An optimized tongue image color correction scheme,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 6, pp. 1355– 1364, Nov. 2010.
  3. Pang, D. Zhang and K. Q. Wang, “Tongue image analysis for appendicitis diagnosis,” Inf. Sci., vol. 175, no. 3, pp. 160–176, 2005.
  4. Lemahieu , J. M. A. D. Naeyaert, W. Philips and Y. V. Haeghen , “An imaging system with calibrated color image acquisition for use in dermatology,” IEEE Trans. Med. Imaging, vol. 19, no. 7, pp. 722–730, Jul. 2000.
  5. Kenet et al., “Clinical diagnosis of pigmented lesions using digital epiluminescence microscopy,” Arch. Dermatol., vol. 129, pp. 157–174, 1993.
  6. Schindewolf et al., “Evaluation of different image acquisition techniques for a computer vision system in the diagnosis of malignant melanoma,” J. Amer. Acad. Dermatol., vol. 31, no. 1, pp. 33–41, 1994.
  7. Pang, D. Zhang , N. M. Li, K. Q. Wang, and Z. Zhang, “Computerized diagnosis from tongue appearance using quantitative feature classification,” Amer. J. Chin. Med., vol. 33, no. 6, pp. 859–866, 2005.

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69.

Authors:

N. Suganthi, N. Rajathi, Farithul Inzamam M

Paper Title:

Elephant Intrusion Detection and Repulsive System

Abstract: Elephant intrusion causes a major problem like crop damage, human death and injuries. Elephant Intrusion has been on the rise in the forest border areas with groups of elephants entering into human habitation and creating a heavy loss to grown plants in agriculture land and their properties. The surveillance and tracking of elephants by humans alone may not always be effective. Mostly the elephants enter into the agriculture land in the night. Detecting elephant intrusion and driving it back is very difficult by the farmers because human cannot watch full night. So, we develop a system which detects the elephant intrusion, creates an alert and repel the elephant away from human habitat. Elephant intrusion detection are useful to avoid human elephant conflict as they stray into agriculture areas searching for food, resulting to economic losses and in extreme cases human casualties. Hence a system to detect elephant intrusion into human habitat and to alert the habitat and forest officials is essential.

Keywords: human death and injuries, agriculture land and their properties.

References:

  1. M. Prabhu “An Efficient Surveillance System to Detect Elephant Intrusion into Forest Borders Using Seismic Sensors”. International Journal of Advanced Engineering Technology E-ISSN 0976-3945, volume-7, issue-1, january-march, 2016.
  2. Maheshwari “Development of Embedded Based System to Monitor Elephant Intrusion in Forest Border Areas Using Internet of Things”. International Journal of Engineering Research ISSN 2319-6890, volume-5, issue-7, july, 2016.
  3. Hemalatha,T. Kanmani,C. Keerthana,S. Ponlatha,I. Selvamani “Detection And Prevention of Elephants Intrusion Into Crop Fields Near Forest Areas”. International Journal Of Innovative Research In Technology,Science & Engineering(IJIRTSE) ISSN: 2395-5619, volume-2, issue-6, june,2016.
  4. J. Sugumar and R. Jayaparvathy “An Improved Real Time Detection System for Elephant Intrusion along the Forest Border Areas”. The Scientific World Journal Article ID 393958, volume-2014, January, 2014.
  5. J. Sugumar,and R. Jayaparvathy “Design of A Quadruped Robot for Human-Conflict Elephant Conflict Mitigation”. Artificial Life and Robotics, Volume-18, December, 2013.
  6. Rizki Dian Rahayani, Arif Gunawan, Agus Urip Ariwibowo “Implementation of Radio Frequency as Elephant Presence Detector for the Human Elephant Conflict Prevention”. Innovative Systems Design and Engineering ISSN 2222-1727 (Paper) ISSN 2222-2871 (Online), Volume-5, Number-5, 2014.
  7. Kanchana “Survey Paper on Elephant Tracking Using Acoustic Sensor”. International Journal of Science and Engineering Development Research-IJSDR. ISSN: 2455-2631, Volume 1, Issue 3, March 2016.
  8. Newlin Shebiah and B.Deeksha “Early Warning System from Threat of Wild Animals Using Raspberry Pi”. SSRG International Journal of Electronics and Communication Engineering ISSN: 2348-8549, Special Issue, March 2017.
  9. King, L. E., Lawrence, A., Douglas-Hamilton, I. and Vollrath, F., “Beehive fence deters crop-raiding elephants”. J. Ecol., 2009, 47, 131–137
  10. Singh, A. P. and Chalisgaonkar, R., Restoration of corridors to facilitate the movement of wild Asian elephants in Rajaji–Corbett elephant range, Irrigation Department, India, May 2006.
  11. Venkataraman, A. B., Saandeep, R., Baskaran, N., Roy, M., Madhivanan, A. and Sukumar, R., Using satellite telemetry to mitigate elephant–human conflict: an experiment in northern West Bengal, India. Sci., 2005, 88, 1827–1831.
  12. Wijesinghe, L. et al., Electric fence intrusion alert system (eleAlert). In Global Humanitarian Technology Conference, IEEE Conference, Seattle, WA, 2011, pp. 46–50.
  13. Hao, Q., Brady, J., Guenther, B. D., Burchett, J. B., Shankar, M. and Feller, S., Human tracking with wireless distributed pyro electric sensors. IEEE Sensors J., 2006, 6, 1683–1696.
  14. Mainwaring, A. and Polastre, J., Wireless sensor networks for habitat monitoring. In WSNA’02, Atlanta, Georgia, USA, 28 September 2002.
  15. Juang, P., Oki, H., Wang, Y., Martonosi, M., Peh, L. and Rubenstei, D., “Energy-efficient computing for wildlife tracking: design trade-offs and early experiences with ZebraNet”. In Special Issue: Proceedings of the 10th Annual Conference on Architectural Support for Programming Languages and Operating Systems, San Jose, CA, December 2002, vol. 30.
  16. Graham, M. D., Adams, W. M. and Kahiro, G. N., Mobile phone communication in effective human–elephant conflict management in Laikipia County, Kenya. Oryx, 2012, 46, 137–144
  17. Suganthi N, Arun R, Saranya D and Vignesh N “Smart Security Surveillance Rover”, International Journal of Pure and Applied Mathematics, Vol. 116, No.12, 2017, 67-75.
  18. Vanitha, V.P.Sumathi, J.Cynthia and B.Illakia, “Next Generation Vehicle Diagnostic Systems”, International Journal of Pure and Applied Mathematics Volume 116 No. 112017,251-259.

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70.

Authors:

Jeba N, Sudha V

Paper Title:

A Comprehensive Survey on Waste Management and its Challenges

Abstract: Now-a-days waste management seems to be a challenge in every city right from its inception to its disposal. Waste management involves the collection of waste from its source, transportation and its disposal at the respective location. Garbage collections bins are flooded due to incremental increase in waste which emits foul odour causing health hazards, diseases and environmental pollution. In this paper, we survey on the mechanisms available for scientific collection and disposal of waste along with its challenges. From the descriptive survey we analyse the present scenario in waste management. It is explicit that issues are prevailing in policies and technologies available for the treatment and management of wastes and insufficient trained manpower to collect, dispose and process the wastes.

Keywords: IoT, Waste Management, Smart City, Waste Disposal.

References:

  1. Balandin, S. Andreev, and Y. Koucheryavy, Internet of Things, Smart Spaces, and Next Generation Networks and Systems, vol. 9247, no. June. Cham: Springer International Publishing, 2015.
  2. A. Al Mamun, M. A. Hannan, A. Hussain, and H. Basri, “Theoretical model and implementation of a real time intelligent bin status monitoring system using rule based decision algorithms,” Expert Syst. Appl., vol. 48, pp. 76–88, 2016.
  3. Sultana, P., Challa, S., Jayavel, S., "IOT based garbage monitoring system", IOT based garbage monitoring system, 2017, pp. 127 - 135.
  4. Tao and L. Xiang, “Municipal solid waste recycle management information platform based on internet of things technology,” in Proc. IEEE Int. Conf. Multimedia Inf. Netw. Secur., 2010, pp. 729–732.
  5. Ravi Kishore Kodali; VenkataSundeep Kumar Gorantla, "Smart solid waste management", 3rd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2017, pp. 200 – 204
  6. Shobana, G., Sureshkumar, R., "Automated garbage collection using GPS and GSM", International Journal of Pure and Applied Mathematics, 2018, pp. 751 - 755.
  7. Haribabu; Sankit R Kassa; J. Nagaraju; R. Karthik; N. Shirisha; M. Anila, "Implementation of an smart waste management system using IoT", International Conference on Intelligent Sustainable Systems (ICISS), 2017, pp. 1155 – 1156
  8. A. Al Mamun, M. A. Hannan, A. Hussain, and H. Basri, “Integrated sensing systems and algorithms for solid waste bin state management automation,” IEEE Sens. J., vol. 15, no. 1, pp. 561– 567, 2015
  9. Catania and D. Ventura, “An Approch for Monitoring and Smart Planning of Urban Solid Waste Management Using Smart-M3 Platform,” in Proceedings of 15th Conference of Open Innovations Association FRUCT, 2014, pp. 24–31.
  10. Chowdhury and M. U. Chowdhury, “RFID-based real-time smart waste management system,” in 2007 Australasian Telecommunication Networks and Applications Conference, 2007, pp. 175–180.
  11. Al Mamun, M. A. Hannan, A. Hussain, and H. Basri, “Wireless Sensor Network Prototype for Solid Waste Bin Monitoring with Energy Efficient Sensing Algorithm,” in 2013 IEEE 16th International Conference on Computational Science and Engineering, 2013, pp. 382–387.
  12. Reverter,M.Gasulla, and R. Pallas-Areny, “Capacitive level sensing for solid-waste collection,” in Proc. IEEE Conf. Sensors, 2003, pp. 7–11.
  13. Vicentini, A. Giusti, A. Rovetta, X. Fan, Q. He, M. Zhu, and B. Liu, “Sensorized waste collection container for content estimation and collection optimization,” Waste Manage., vol. 29, no. 5, pp. 1467–1472, 2009.
  14. Longhi, et al., “Solid waste management architecture using wireless sensor network technology,” in Proc. IEEE 5th Int. Conf. New Technol. Mobility Secur., 2012, pp. 1–5.
  15. Medvedev, P. Fedchenkov, A. Zaslavsky, T. Anagnostopoulos, and S. Khoruzhnikov, “Waste management as an IoT enabled service in Smart Cities,” in Proc. IEEE 15th Int. Conf. Next Generation Wired/Wirel. Advanced Netw. Syst. 8th Conf. ruSMART, 2015, pp. 104–115.
  16. Mes, M. Schutten, and A. Perez-Rivera, “Inventory routing for dynamic waste collection,” Waste Manage., vol. 34, no. 9, pp. 1564– 1576, 2014.
  17. T. Anagnostopoulos and A. Zaslavsky, “Robust waste collection exploiting cost efficiency of IoT potentiality in smart cities,” in Proc. IEEE 1st Int. Conf. Recent Adv. Internet Things, 2015, pp. 1–6.

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71.

Authors:

S. Kavitha, K. R. Baskaran, S. Sathyavathi

Paper Title:

Heart Disease with Risk Prediction using Machine Learning Algorithms

Abstract: Nowadays health connected issues are terribly high, and it can'tbe simply foretold earlier to avoid complications. Wellness heart condition cardiopathycar diovascular disease} (HD) may be a common disease for the individuals more matured cluster thirty-five to fifty. the sector of information mining has concerned within the medical domain, With the historical knowledge, mining algorithms are able to predict and classify the abnormality in conjunction with its risk levels. The previous studies related to predict heart problems have used several features which has been collected from patients. The accuracy level of prediction and the number of features is very less in the previous systems. To improve the prediction accuracy the planned system, consider additional range of options and implements a Weighted Principle Analysis (WPCA) and changed Genetic Algorithm(GA) The planned technique helps the medical domain for predicting HD with its numerous co-morbid (types of heart diseases) conditions. The system has 2 main objectives, that are rising diagnosing accuracy and reducing classification delay. The WPCA represents with the effective cacophonous criteria that has been applied into the genetic Algorithm. The system effectively identifies the disease and its sub types, the sub type which is referred as the level of class such as normal and mild or extreme.Using combinatorial methods from data mining decision making has been simplified and the proposed work achieved 96.34% accuracy, which is higher than the known approaches in the literature.

Keywords: Data mining, Classification, Weighted Principle Analysis (WPCA), Modified Genetic algorithm (GA), Heart Disease.

References:

  1. Thomas, J., and R. Theresa Princy. "Human heart disease prediction system using data mining techniques." Circuit, Power and Computing Technologies (ICCPCT), 2016 International Conference on. IEEE, 2016.
  2. Wilson, Peter WF, et al. "Prediction of coronary heart disease using risk factor categories." Circulation18 (1998): 1837-1847.
  3. http://www.americanbusinessmag.com/2010/04/symptoms-of-a-heart-attack-2/
  4. Palaniappan, Sellappan, and RafiahAwang. "Intelligent heart disease prediction system using data mining techniques." Computer Systems and Applications, 2008. AICCSA 2008.IEEE/ACS International Conference on.IEEE, 2008.
  5. Khaing, HninWint. "Data mining based fragmentation and prediction of medical data." Computer Research and Development (ICCRD), 2011 3rd International Conference on. Vol. 2. IEEE, 2011.
  6. Patel, Ajad, Sonali Gandhi, SwethaShetty, and BhanuTekwani. "Heart Disease Prediction Using Data Mining." (2017).
  7. Wghmode, MrAmol A., MrDarpanSawant, and Deven D. Ketkar. "Heart Disease Prediction Using Data Mining Techniques." Heart Disease(2017).
  8. http://archive.ics.uci.edu/ml/datasets/Heart+Disease
  9. Kanagaraj, N.Rajathi, R.Brahmanambika, K.Manjubarkavi,, “Early Detection of Dengue Using Machine Learning Algorithms” '' International Journal of Pure and Applied Mathematics " , Special Issue, February, 2018.
  10. R. Baskaran, V. Vijilesh, R. Nedunchezhian and R.S. Kumar
    “Combining Intelligent Web Caching with Web Pre-Fetching Techniques To Predict Tourist Places'', International Journal of Pure and Applied Mathematics " Volume 116 No. 12 2017, 97-105
  11. R.Baskaran, C. Kalaiarasan, “Improved Performance By Combining Web Pre-Fetching Using Clustering With Web Caching Based On Svm Machine Learning Method”, International Journal of Computers Communications & Control, ISSN 1841-9836,Vol.11, No.2, April 2016, pp. 166-177
  12. R. Baskaran, C. Kalaiarasan, “Pre-Eminence Of Combined Web Pre-Fetching And Web Caching Based On Machine Learning Technique”, Arabian Journal for Science and Engineering (Springer Journal), ISSN 1319-8025, Vol. 39, No.11, November 2014, pp. 7895-7906

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72.

Authors:

Alamelu M. Sindhuja, Balaji S

Paper Title:

Automatic water Irrigation System Approach for Smart Homes

Abstract: In agriculture, irrigation plays an important role. Normally, we have many issues in irrigating the plants. Over irrigation of plants leads to decay of plants and low irrigation of plants leads to retardation of crop growth, late flowering. To overcome from this issues the proposed system define a method called Automatic Irrigation system for Smart Homes (AI-SH). The proposed model build an automatic irrigation system approach using Arduino in which moisture sensor senses the moisture content present in the soil. According to the moisture content level water will be pumped to the soil by the DC motor. The sensor continuously monitors the soil moisture content, when it reaches the required water tank level will be used for planting water and its water level will be continuously monitored from an Ultrasonic sensor using distance value. If the range goes beyond the certain level, then the system will send message notification to the user mobile. By this proposed AI –SH approach the user can know the pouring method of water for home plants. If the user he/she are far away from their home can also monitor the plant water easily and quickly.

Keywords: soil moisture sensor, ultra sonic sensor, motor driver circuit

References:

  1. Abhishek Gupta , Shailesh Kumawat , Shubham Garg, “ Automatic Plant watering system ”, Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-4, 2016.
  2. Deweshvree Rane, P. R. Indurkar, D. M. Khatri, “Review paper based on automatic irrigation system based on RF module”, IJAICT Volume 1, Issue 9, January 2015
  3. B. Bhawarkar, D.P. Pande, R.S. Sonone,Mohd. Aaquib , P.A. Pandit, and P. D.Patil,“Literature Review for Automated Water Supply with Monitoring the Performance System”, International Journal of Current Engineering and Technology, Vol. 4, No. 5.2014.
  4. Kaja Rahamtulla, M.Harsha Priya, “Drinking Water Supply Control and Water Theft Identification System” , International Journal of scientific research and management Volume1,Issue 9, PP. 483-487,2013.
  5. R , Varunkumar.M.C, Tulsiram.M.P, “Automation in Urban Drinking Water Filtration, Water Supply Control, Water Theft Identification Using PLC and SCADA and Self Power Generation in Supply Control System ”, International Journal of Advanced Research in Electronics and Communication Engineering ,Volume 3, Issue 7, July 2014
  6. Alamelu, Ramalatha Marimuthu, “A survey on healthcare and social network collaborative service utilization using internet of things”, Vol.9,pp.1010-1030, Journal of Advanced Research in Dynamical and Control Systems,2017
  7. Alamelu, A.M.J. Mohamed Zubair Rahman, “Evaluation of service transactions and selection of quality offered services in a business environment”, American Journal of Applied Sciences Vol.11, Issue No.2,pp. 207-215, 2014.
  8. Alamelu, S.Karthikeshwar, V.Deelipan, C.T.Gowtham, “Mismatch cancer colour prediction analysis on Big Data”, Research Script, pp.4-7, 2016.
  9. Senthil, M.Alamelu, A.M.J. Zubair Rahman, “Optimized Service Level Agreement Negotiation system for web services”, International Journal of Computer Network and Security, Vol.4, No.1, 2012.
  10. Ramalatha Marimuthu, M.Alamelu, “Driver fatigue detection using image processing and accident prevention”, International Journal of pure and Applied mathematics, Vol.116, No.11, pp.91-99,2017.
  11. Alamelu, T.S.Pradeep kumar , “ PERM based service prediction model for Ground water level analysis”, TAGA journal of Technology, 1612-1621,2018
  12. Alamelu, AMJ Zubair Rahman ,‘Validation and classification of web services using Equalization Validation Classification’ Journal of Internet banking and commerce,Vol.17, Issue 3, pp.2-21,2012.

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73.

Authors:

M. Suguna, D. Prakash, Cynthia. J

Paper Title:

Secure Data Access Privacy Preserving using Cloud Services

Abstract: In spite of the tremendous computational advantages, outsourcing data to the public cloud is also preventing customers’ direct control over the systems that use their data, which unavoidably brings in new security challenges. Cloud computing gives numerous advantages and unparalleled convenience for the cloud customers to get the on-demand access of cloud provided that the local infrastructure limitations need not be taken into account. While accessing data, there may be a co-operative relationship among different users which makes sharing and exchanging of information, a tedious process. The view of current security solutions is mainly on authentication to apprehend that the data of an individual cannot be approached illegally, but there arose a privacy issue when a user request for data sharing to other users through cloud server. The users’ privacy may be exposed by challenged access request itself regardless of whether the data access permission for the user is obtained or not. In the proposed system, a privacy-preserving authentication protocol is employed to prevent the above privacy complications. In this technique, authority of data through shared access is achieved by the process of sending anonymous access request which gives privacy to the cloud users. Access control is based on attributes so that the cloud users can only access their own authorized data fields. Advanced encryption standard algorithm is used to achieve data anonymity and data protection. The proposed method dealt with secure privacy preserving data access authority is attractive for multiple-user in cloud real time storage.

Keywords: Cloud computing, authentication protocol,shared authority, privacy preservation, data anonymity.

References:

  1. Yang and X. Jia "An Efficient and Secure Dynamic Auditing Protocol for Data Storage in Cloud Computing", IEEE Trans. Parallel and Distributed Systems, vol. 24, no. 9, pp.1717 -1726, 2013.
  2. Nabeel , N. Shang and E. Bertino "Privacy Preserving Policy Based Content Sharing in Public Clouds", IEEE Trans. Knowledge and Data Eng., vol. 25, no. 11, pp.2602 -2614, 2013
  3. Sundareswaran , A.C. Squicciarini and D. Lin "Ensuring Distributed Accountability for Data Sharing in the Cloud", IEEE Trans. Dependable and Secure Computing, vol. 9, no. 4, pp.556 -568, 2012
  4. Wang "Proxy Provable Data Possession in Public Clouds", IEEE Trans. Services Computing, vol. 6, no. 4, pp.551 -559, 2012
  5. Wang , C. Wang , K. Ren , W. Lou and J. Li "Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing", IEEE Trans. Parallel and Distributed Systems, vol. 22, no. 5, pp.847-859 -25, 2011
  6. A. Dunning and R. Kresman "Privacy Preserving Data Sharing with Anonymous ID Assignment", IEEE Trans. Information Forensics and Security, vol. 8, no. 2, pp.402 -413, 2013
  7. Wang , Q. Wang , K. Ren , N. Cao and W. Lou "Toward Secure and Dependable Storage Services in Cloud Computing", IEEE Trans. Services Computing, vol. 5, no. 2, pp.220 -232, 2012
  8. Tang , P.C. Lee , J.C.S. Lui and R. Perlman "Secure Overlay Cloud Storage with Access Control and Assured Deletion", IEEE Trans. Dependable and Secure Computing, vol. 9, no. 6, pp.903 -916, 2012
  9. Ruj , M. Stojmenovic and A. Nayak "Decentralized Access Control with Anonymous Authentication for Securing Data in Clouds", IEEE Trans. Parallel and Distributed Systems, vol. 25, no. 2, pp.384 -394, 2014
  10. Chen , Y. Wang and X. Wang "On-Demand Security Architecture for Cloud Computing", Computer, vol. 45, no. 7, pp.73 -78, 2012
  11. Zhu , H. Hu , G. Ahn and M. Yu "Cooperative Provable Data Possession for Integrity Verification in Multi-Cloud Storage", IEEE Trans. Parallel and Distributed Systems, vol. 23, no. 12, pp.2231 -2244, 2012
  12. Liu , Y. Zhang , B. Wang and J. Yan "Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud", IEEE Trans. Parallel and Distributed Systems, vol. 24, no. 6, pp.1182 - 1191, 2013.

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74.

Authors:

Suguna.M, Priyanga B, Prakash D.

Paper Title:

Automated Generation of Question Answering System using Semantic Web

Abstract: Question answering system provides answer to user’s question according to the requirement. Question answering used in Information Retrieval processing, Natural Language, Artificial Intelligence (AI), Document Retrieval, Automatic evaluations. In QA system, once the question is posted by the client, the system need to find meaning of the words such as synonyms and provide correct answer to the user. It is very difficult to find answer from large collection of information available in Internet. The process involved in QA is question generation, answer filtering and store in the database. The proposed approach is used to retrieve the answers for the posted query in an efficient manner and reduce time consumption. WAD approach is used to analyze the answer and rank the accuracy of the answer with the existing method.

Keywords: Answer Ranking, Natural Language Processing, Cloud, Named Entity Recognition, Information Retrieval.

References:

  1. West, R., Gabrilovich, E., Murphy K, Sun, S., Gupta, R.: Lin D, Knowledge base completion via search-based question answering. In: ACM Proceedings of the 23rd international conference on World wide web, pp.515-526, (2016).
  2. Rini Wongso, Meiliana, Derwin Suhartono.: Question AnsweringSystem using Named Entity Recognition. In: 3rd Internationalconference on Information Tech.,Computer,and Electrical Engineering (ICITACEE), pp.19-21, (2016).
  3. MdMoinul Hoque, Paulo Quaresma: A Content-Aware HybridArchitecture for Answering Questions from Open-domain Texts. In:19th International Conference on Computer and Information Technology,pp.18-20, (2016).
  4. Pooja, P., Walke, ShivkumarKarale: Implementation Approaches for Various Categories of Question Answering System. In: IEEE Conference on Information and Communication Technologies, pp.8-15, (2015).
  5. Soricut, R., Brill, E.: Automatic question answering using the web: Beyond the factoid. In: Journal of Information Retrieval-Special Issue on Web Information Retrieval, Vol.9, pp.191-206, (2014).
  6. PetrSosnin: Question-Answer Modeling in Conceptual Design of Automated Systems. In: IEEE MELECON, pp.16-19, (2012).
  7. Lopez, V., Uren, V., Motta, E., Pasin, M., AquaLog: An ontology-driven question answering system for organizational semantic intranets. In: Web Semantics: Science, Services and Agents on the World Wide Web, Vol.5, No.2, pp.72-105, ( 2013).
  8. Lally, A., Prager, M., McCord, M., Boguraev, B., Patwardhan, S., Fan, S., Fodor, R., Chu-Carroll, J.: Question analysis: How watson reads a clue. In: IBM Journal of Research and Development, Vol.56, No.3.4, pp.2-1, (2012).
  9. Monahan, S., Lehmann, J., Nyberg, J., Plymale, J., Jung, A.: Cross-lingual cross-document conference with entity linking. In: TAC 2011 Workshop, (2011).
  10. Hierschman, L.; Gaizauskas, R.: Natural Language question Answering. In: ACM proceedings of the Natural Language Engineering vol7, No.4, pp 275-300, (2010).
  11. Suchanek, F., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: ACM proceedings on WWW, pp.697-706, (2011).
  12. Babanne, V., Patil, S., Joshi, D.: Intelligent Question answering System. In: International Journal of Scientific & Engineering Research, Volume 4, Issue 5, (2013).
  13. Web site: www.searchdocs.net.
  14. Web site:www.cs.cmu.edu/QA-data.

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75.

Authors:

P. Shenbagam, N. Rajkumar

Paper Title:

Predictive Analysis for Identifying the Relationship between Forest Cover and Tiger Population

Abstract: This paper analyzes the forest cover in India as a whole and also in terms of states. The species growth with respect to the forest cover and the survival of species based on their Kingdoms of Classification is studied. The dependencies between the forest covers of India with the flora of India is studied. With respect to fauna, this paper discusses the state population of India’s national animal, Tiger and its relationship to the degree of deforestation over years. This also studies the wasteland cover and the areas of improvement for the betterment of Indian flora and vegetation. This also checks for relationships, dependencies and variations between flora and fauna to obtain patterns for improving the Indian Ecosystem.

Keywords: environment, prediction analysis, forest cover, Deforestation, Tiger population

References:

  1. Sudhakar Reddy, Kalloli Dutta and C. S. Jha, “Analyzing the gross and net deforestation rates in India”, Forestry and Ecology Group, 2011.
  2. Kaushik Das, Abhijit Guha Roy, “Landscaping of Random Forests through controlled deforestation”, Second National Conference on Communication (NCC), 2016.
  3. Jean Francois Mas, Gabriela Cuevas, “Local Deforestation patterns in Mexico – An approach using geographically weighted regression”, 1st International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM), 2015.
  4. Menaka; S. Suresh Kumar; M.Bharathi, “Change detection in deforestation using high resolution satellite image with Haar Wavelet Transforms”, International Conference on Green High Performance Computing (ICGHPC), 2013.
  5. Rajathi, L.S. Jayashree, “Soil Moisture Forecasting Using Ensembles of Classifiers”, Springer, 2016.
  6. Sreenivasan Narayanan, “Saving the Indian Tiger - A study on the effects of Wildlife (Protection) Amendment Act 2006 and National Tiger Conservation Authority”, 2014.
  7. Neil Carter, Simon Levinb, Adam Barlowc, Volker Grimmd, “Modeling tiger population and territory dynamics using an agent-based approach”, Ecological Modelling, 2015.
  8. Aditya Joshi, Srinivas Vaidyanathan, Samrat Mondol, Advait Edgaonkar, Uma Ramakrishnan, “Connectivity of Tiger (Panthera tigris) Populations in the Human-Influenced Forest Mosaic of Central India”, PLOS Genetics, 2013.
  9. Sudhakar Reddy Et al., “Quantification and Monitoring of Deforestation in India over Eight Decades”, Biodiversity and Conservation, 2015.
  10. Bibek Yumnam, Yadvendradev V. Jhala, Qamar Qureshi, Jesus E. Maldonado, Rajesh Gopal, Swati Saini, Y. Srinivas, Robert C. Fleischer, “Prioritizing Tiger Conservation through Landscape Genetics and Habitat Linkages”, PLOS Genetics, 2014.
  11. Anuradha, N. Rajkumar, “A Novel Approach in Mining Specialized Coherent Rules in a Level-Crossing Hierarchy”, International Journal of Fuzzy Systems, 2017.
  12. R. Baskaran, C. Kalaiarasan, “Improved Performance by Combining Web Pre-Fetching Using Clustering with Web Caching Based on SVM Learning Method”, International Journal of Computers Communications & Control, ISSN 1841-9836, 11(2):167-178, April 2016.

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76.

Authors:

P. Devaki, S. Selvanayaki, R. Marudhachalam

Paper Title:

Impact of Academic and Social Factors on the Academic Performance of First Year Engineering Student

Abstract: To study about the learning outcome and measuring the same has considerable amount of complexity. Each student has his/her own way of learning pattern and every individual is prone to deviate from learning because of the pace of distraction for them. This study focuses on measuring and assessing students learning outcome. Assessment primarily starts with the measurement of outcomes. Measurable outcomes involve student’s behavior / outcome, assessment method and criteria for success. Learning outcomes are measured with student’s knowledge, skills, regularity in attending regular classes, daily activities / time spent on each activity and values on completion of a courses/program. It can be measured directly or indirectly. This study will formulate a strategy to improve the success rate of the students.

Keywords: outcomes, learning outcome, program outcome, assessment and students behavior

References:

  1. Laura j. Pyzdrowski, Ye sun, Reagan Curtis, David Miller, Gary Winn and Robin A. M. Hensel, “Readiness and attitudes as indicators for succession college calculus”, International journal of science and mathematics education (2013) 11:529y554, National science council, Taiwan 2012 pp. 530-554.
  2. Jamie c, Cavalier , James d, Kleinfrank j, Cavalie, “Effects of cooperative learning on performance, attitude, and group behaviors ina technical team environment” , Etr&d, vol 43, no. 3, 1995, pp. 61- 71, ISSN 1042-1629.
  3. Paul e. Brauchle, Joyce r. Mclarty, James Parker “A portfolio approach to using student performance data to measure teacher effectiveness”, Journal of personnel evaluation in education 3:pp:17-30, 1989.
  4. Darrell fisher & Tony Rickards, “Associations between teacher-student interpersonal behaviour and student attitude to mathematics”, Mathematics education research journal 1998, vol. 10, no.1, 3-15.
  5. A Russell SmithJr, Cathy Cavanaugh and W Allen Moore, “Instructional multimedia: An investigation of student and instructor attitudes and student study behavior”, Smith et al. BMC medical education 2011, 11:38. http://www.biomedcentral.com/1472-6920/11/38.
  6. King-dowsu, “The effects of a chemistry course with integrated Information communication technologies on university students learning and attitudes”, International Journal of Science and Mathematics Education (2008) pg : 225-249
  1. M’hamed ighezza, “Modeling relationships among learning, attitude, self-perception, and science achievement for grade 8Saudistudents”, International Journal of Science and Mathematics Education 2013.
  2. Zareenzaidi, Tara jaffery, Afshanshahid, ShaheenMoin, AhsenGilani, William Burdick, “Change in action: using positive deviance to mprove student clinical performance ”, Adv in Health SciEduc (2012) 17:pg: 95–105.
  3. Neng-tang Norman Huang ,Li-Jia Chiu , Jon-chao Hong, “Relationship among students’ problem-solving attitude,perceived value, behavioral attitude, and intention to participate in a science and technology contest”, International Journal of Science and Math Education.
  4. Alexander T. Jackson , Bradley J. Brummel , Cody L. Pollet , David D. Greer “An evaluation of interactive tabletops in elementary mathematics education”, Education Tech Research Dev (2013) 61:311–332.
  5. Kris M. Y. Law, KristijanBreznik, “Impacts of innovativeness and attitude on entrepreneurial intention: among engineering and non-engineering students”, Int J Technol Des Educ, Doi 10.1007/s10798-016-9373.
  6. ShiJerLou, YiHuiLiu, Ru Chu Shih ,Kuo Hung Tseng, “The senior high schoolstudents’ learning behavioral model of STEM in PBL” , Int JTechnolDes Educ (2011) 21:161-183, DOI 10.1007/s10798-010-9112, pp161–183.
  7. Apostolos Mavridis, AikateriniKatmada, ThrasyvoulosTsiatsos, “Impact of online flexible games on students’ attitudetowards mathematics”, Education Tech Research Dev.,DOI: 10.1007/s11423-017-9522-5.
  8. SuleTasliPektas, FeyzanErkip “Attitudes of design students toward computer usage in design”, International Journal of Technology and Design Education (2006) 16,pp 79-95, DOI: 10.1007/s10798-005-3175-0.
  9. IndhuPriya S & Dr. Devaki P., 2017,"Evaluating Students Performance in Placements Activity-A Survey" in International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Vol.6, no. 1.
  10. Indhu Priya S. & Devaki P, 2017, “ Performance Evaluation of Students Based on Skillset with Data Analytics”, International Journal of Pure and Applied Mathematics, Vol. 118, No. 182018, Pg. 3937 – 3945.

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77.

Authors:

Devaki.P, Renuka.T, Sridhevi.S

Paper Title:

Fishermen Helping System

Abstract: The idea of this project is to prevent the fishermen’s boat from crossing the boundary while going to the sea for fishing. When the boat is on board, its position is tracked and checked if it is crossing the border. Once the boat is about to cross the safe border and enter into the prohibited zone, a notification is sent to the control unit in the land and a buzzer is also set in the control unit. The control unit controls and turns the boat into the safe side. This prevents the boat from crossing the country’s border and the fishermen and their boat can also reach the shore safely. The position of the boat is tracked by using the GPS (Global Positioning System) and every movement of the boat can be noted. By using a GSM module, the boat can be connected to the control unit in the land. The position of boat is sent to the control unit using a GSM module via cloud. When the border is crossed by the boat, the buzzer goes on in the control unit and the boat is immediately controlled.

Keywords: GPS, GSM Module, Relay, Relay Driver.

References:

  1. Dinesh Kumar R, Shubin Aldo M, J. Charles Finny Joseph, 2016, "Alert System for Fishermen Crossing Border using Android", International Conference on Electrical, Electronics and Optimization Techniques, DOI link: https://doi.org/10.1109/ICEEOT.2016.7755630
  2. AmitKushwaha,VineetKushwaha, 2011,"Location Based Services using Android Mobile Operating System", International Journal of Advances in Engineering & Technology, Vol. 1,Issue 1,pp.14-20
  3. HYPERLINK "http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Majid%20A.%20Al-Taee.QT.&newsearch=true"Majid A. Al-TaeeHYPERLINK "http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Majid%20A.%20Al-Taee.QT.&newsearch=true" , Omar B. Khade, Nabeel A. Al-Saber, 2007,
  4. "Remote Monitoring of Vehicle Diagnostics and Location Using a Smart Box with Global Positioning System and General Packet Radio Service", International conference on Computer Systems and Applications.
  5. Etter, Costa, Broens, 2006,"A Rule-Based Approach Towards Context-Aware User Notification Services",International conference on Pervasive Services.
  6. PulathisiBandara, UdanaBandara, 2008,"A Practical Approach for Location-Aware and Socially-Relevant Information Creation and Discovery for Mobile Users",IEEE International Symposium on Wireless Communication Systems.
  7. Virrantaus, K. &Markkula, J, etal., 2001, "Developing GIS - supported location-based services",Second International conference on Web Information Systems Engineering.
  8. Fujita, T. Nawaoka& E. Hirohata, 1988 ,"Development of a mapping and guidance database for automobile navigation system",41stIEEE Vehicle Technology conference.
  9. E. Bardram,2004, "Applications of Context-Aware Computing in Hospital Work-Examples and Design Principles", Proceedings of the ACM Symposium on Applied Computing, Pg 1574 – 1579.
  10. Shiels(2008), "Boom times ahead for mobile web" 2008. BBC News Report: http://news.bbc.co.uk/2/hi/technology/7522305.stm
  11. G. Brown, P. W. McBurney, 1988. "Self-Contained GPS Integrity Check Using Maximum Solution Separation", Navigation/ Vol 35, No 1.
  12. Yi-Bing Lin, Yun-Wei Lin, Chung-Yun Hsiao, Shie-Yuan Wang, 2017, “Location–based IoT Applications on Campus”, Journal of Pervasive and Mobile Computing, Vol 40, No. C, Pg 660-673.
  13. Bin Guo,DaqingZhang, ZhuWang, ZhiwenYu, XingsheZhou, 2013 “Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things”, Journal of Network and Computer Applications Vol. 36, No 6, Pg 1531–1539
  14. Louis Louw&Mark Walker, 2018, “Design and implementation of a low cost RFID track and trace system in a learning factory”, Conference on Learning Factories - Advanced Engineering Education & Training forManufacturing Innovation, Science direct
  15. Muhammad Tariq, HammadMajeed, Mirza Omer Beg, FarrukhAslam Khan,AbdelouahidDerhab, 2018, “Accurate detection of sitting posture activities in a secure IoT basedassisted living environment”, Future Generation Computer Systems.
  16. Devaki P, Illakiya J, Indumathi R, Arul Priya M 2017,“Geospatially and Literally Analysing Tweets”, Journal of Advanced Research in Dynamical and control Systems, ISSN 1943-023X, Issue 14-Special Issue, Pg- 1002 -1009

335-338

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78.

Authors:

M. Alamelu T. S. Pradeep Kumar

Paper Title:

Unique Identical Weightage (UIW) Model for Real Time Customer Search Queries

Abstract: E-commerce systems plays the vital role in Information Technology and communication applications. Compared to the technology of olden days, people now days buys their required products and services from the online service transactions. Buying and selling products are frequently increasing factor for both the buyer and seller. From the customer point of view searching of quality products with the consumable cost is a big challenge. The proposed system Unique Identical weightage (UIW) system reveals about the customer searching analysis using the weightage system approach. The UIS analysis system will track the identity of the customer queries with respect to the searching categories. The proposed system will make the classification based on the customer searching criteria. Ranking weightage is allotted based on the searching method and finally produce the judgemental range of searching choices to the customer. The advantage of the system will provide quick searching solutions to the customer. This method outperforms the existing system in reducing the time wasted by a customer for searching a product.

Keywords: E Commerce, Weightage based system, Search Query, Identical Weightage system

References:

  1. Zhengbao Jiang, Zhicheng Dou, Member, Ji-Rong Wen, “ Generating query facts using knowledge Bases”, IEEE transactions on knowledge and data engineering, vol. 29, no. 2, february 2017.
  2. Jiaping Zhao, Laurent Itti, “Classifying time series using local descriptors with hybrid sampling” IEEE transactions on knowledge and data engineering, vol. 28, no. 3, march 2016.
  3. Alamelu, AMJ ZubairRahman ,‘Validation and classification of web services using Equalization Validation Classification’ Journal of Internet banking and commerce,Vol.17, Issue 3, pp.2-21,2012.
  4. Steven O. Kimbrough, Thomas Y. Lee, UlkuOktem, Modeling for Decision Support in Network-Based Services, vol. 42, pp. 196, 2012.
  5. David Kreyenhagen, T. I. Aleshin, J. E. Bouchard, A. M. I. Wise and R. K. Zalegowski, "Using supervised learning to classify clothing brand styles,"2014 Systems and Information Engineering Design Symposium (SIEDS), Charlottesville, VA, 2014, pp. 239-243.
  6. Alamelu, RamalathaMarimuthu, “A survey on healthcare and social network collaborative service utilization using internet of things”, Vol.9,pp.1010-1030, Journal of Advanced Research in Dynamical and Control Systems,2017
  7. Alamelu, A.M.J. Mohamed ZubairRahman, “Evaluation of service transactions and selection of quality offered services in a business environment”, American Journal of Applied Sciences Vol.11, Issue No.2,pp. 207-215, 2014.
  8. JayakumarSadhasivam, Alamelu M, Radhika R, Ramya S, Dharani K and SenthilJayavel, “Enhanced way of securing automated teller machine to track the misusers using secure monitor tracking analysis”,IOP Conf. Series: Materials Science and Engineering, 263, 2017.
  9. Alamelu, S. Karthikeshwar, V. Deelipan, C. T. Gowtham, “Mismatch cancer colour prediction analysis on Big Data”, Research Script, pp.4-7, 2016.

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79.

Authors:

Shobana G, Vigneshwara B, Maniraj Sai A.

Paper Title:

Twitter Sentimental Analysis

Abstract: In this current era, social media plays a important role in data exchange, sharing their thoughts. Emotional Effect of a person maintains an important role on their day to day life. Sentiment Analysis is a procedureof analyzing the opinions and polarity of thoughts of the person. Twitter is a main platform on sharing the thought's, opinion and sentiments on different occasions. Twitter Sentimental Analysis is method of analyzing the emotions from tweets (message posted by user in twitter). Tweets are helpful in extracting the Sentimental values from the user. The data provide the Polarity indication like positive, negative or unbiassed values. It is focused on the person’s tweets and the hash tags for understanding the situations in each aspect of the criteria. The paper is to analyse the famous person’s id’s (@realdonaldtrump) or hash tags (#IPL2018) for understanding the mindset of people in each situation when the person has tweeted or has acted upon some incidents. The proposed system is to analyze the sentiment of the people using python, twitter API, Text Blob (Library for processing text). As the results it helps to analysis the post with a better accuracy.

Keywords: (@realdonaldtrump), (#IPL2018), Text Blob (Library for Processing Text).

References:

  1. Jansen,B.J.; Zhang,M.; Sobel,K.; and Chowdury,A. (2009), “Twitterpower: Tweets as electronic word of mouth”, Journal of the American Society for Information Science and Technology 60(11):2169–2188.
  2. Pak, A., and Paroubek, P (2010), “Twitter as a corpus for sentiment analysis and opinion mining”. In Proc. of LREC.
  3. Pang, B., and Lee, L. (2008), ”Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval” 2(1-2):1–135.
  4. Wilson, T. Wiebe, J.; and Hoffmann, (P. 2009),”Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Li nguistics”, 35(3):399–433.
  5. M Hu and B Liu. (2004),”Mining and summarizing customer reviews. KDD”.
  6. Barbosa, J. Feng. “Robust Sentiment Detection on Twitterfrom Biased and Noisy Data”. COLING 2010: Poster Volume,pp. 36-44.
  7. Kamps, M. Marx, R. J. Mokken, and M. De Rijke, “Using wordnet to measure semantic orientations of adjectives,” 2004.
  8. Alec Go, Richa Bhayani, and Lei Huang. 2009. Twitter sentiment classification using distant supervision. Technical report, Stanford.
  9. David Zimbra, M. Ghiassi and Sean Lee, “Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks”, IEEE 1530-1605, 2016.
  10. Varsha Sahayak, Vijaya Shete and Apashabi Pathan, “Sentiment Analysis on Twitter Data”, (IJIRAE) ISSN: 2349-2163, January 2015.
  11. Peiman Barnaghi, John G. Breslin and Parsa Ghaffari, “Opinion Mining and Sentiment Polarity on Twitter and Correlation between Events and Sentiment”, 2016 IEEE Second International Conference on Big Data Computing Service and Applications.
  12. Mondher Bouazizi and Tomoaki Ohtsuki, “Sentiment Analysis: from Binary to Multi-Class Classification”, IEEE ICC 2016 SAC Social Networking, ISBN 978-1-4799-6664-6.
  13. Nehal Mamgain, Ekta Mehta, Ankush Mittal and Gaurav Bhatt, “Sentiment Analysis of Top Colleges in India Using Twitter Data”, (IEEE) ISBN -978-1-5090-0082-1, 2016.
  14. https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python.” Twitter Sentimental analysis for Realdonaldtrump” Devaki P, Ilakiya, J, Indumathi, R and Arul Priya, M,
  15. “Geospatially and literally analysing tweets”, Journal of Advanced Research in Dynamical and Control Systems Volume 9, Issue Special Issue 14, 2017, Pages 1002-1009

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80.

Authors:

Syed Ali Fathima S J, Jenice Aroma R

Paper Title:

Simulation of Fire Safety Training Environment using Immersive Virtual Reality

Abstract: The Real Time Environments that are very difficult and dangerous to practice can be simulated using Virtual Reality (VR) and canbe used as a learning tool. The use of immersive VR supports to demonstrate the three dimensional virtual environment in detail and helps the user to learn about the concepts of fire hazards and practice escape mechanisms in fire surrounded situations. A game like interface techniques is used for VR fire-safety training interaction in order to improve motivation for learning and were encouraged to explore the virtual world. People interact with objects and navigate through environment that are virtually present with full control using a VR controller which gives a feel of an active user. The identification of few fire hazards circumstances at home and office environments are performed based on which a similar virtual environment is designed to practice fire safety and escape techniques.A PASS training for fire extinguishers was included as a core concept of the project. PASS stands for Pull, Aim, Squeeze and Sweep while using a fire extinguisher. Knowing these techniques in times of trouble could be really helpful and mandatory.

Keywords: Fire Safety, Fire extinguishers, Immersive, PASS Training, Virtual Reality (VR), Game interface

References:

  1. Smith, S. & Ericson, E. Virtual Reality (2009) 13: 87. https:// doi.org/10.1007/s10055-009-0113-6
  2. Cha, M., Han, S., Lee, J., Choi, B., 2012. A virtual reality based fire training simulator integrated with fire dynamics data. Fire Saf. J. 50, 12–24.
  3. Rüppel, K. Schatz, Designing a BIM-based serious game for fire safety evacuation simulations, Adv. Eng. Inform. 25 (4) (2011) 600– 611, http://dx.doi.org/10.1016/j.aei.2011.08.001.
  4. Li, Z. Ma, Q. Shen, S. Kong, The virtual experiment of innovative construction operations, Autom. Constr. 12 (5) (2003) 561–575, http:// dx.doi.org/10.1016/S0926-5805(03)00019-0.
  5. https://www.ted.com/topics/virtual+reality , last accessed date 9.04.18
  6. https://www.youtube.com/user/UnrealDevelopmentKit , last accessed date 9.04.18
  7. https://www.osha.gov/SLTC/etools/evacuation/portable_use.html , last accessed date 9.04.18

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81.

Authors:

Nishant Kaushik, Parveen Sultana H, Senthil Jayavel

Paper Title:

Remote Authentication using Face Recognition with Steganography

Abstract: In today’s world securing data from the hackers and other unauthorized attackers is a critical task. Almost all the system has some kind of authentication which allows the user to access their data. Most of these system are limited to one layer of security like textual passwords. The authentication using textual password is famous as it is straightforward. But the simplicity comes at the cost of vulnerability. These authentication methods are prone to spyware and dictionary attacks. As the systems are becoming more powerful than ever, it is easy to launch a dictionary attack. Another form of attack is to monitor the request and response between the client and server. It is possible when the attacker has gained physical access to the communication medium. Intruder just has to analyze the packets to figure out the delicate information such as password. There are many networks that cannot afford any kind of breach. Steganography, the art of hiding the existence of message by embedding the secret message into another medium, can be exploited in authentication system. Steganography has emerged as technology with various applicationwhich introduced steganalysis, the process to detect the hidden information. The user has to undergo face recognition as well as textual authentication. Since any of the request and response between server and client will not have password in plain text form, it is not possible to breach the password. The system is combination of face recognition and steganography.

Keywords: Remote Authentication, Steganography, Cryptography

References:

  1. Vishnu S babu and Prof. Helen K J. “A Study on Combined Cryptography and Steganography:” International Journal of Research and Studies in Computer Science and Engineering Volume 2, Issue 5, May 2015, PP 45 - 49 ISSN 2349 - 4840 (Print) & ISSN 2349 - 4859(online).
  2. SnehaBansod and GunjanBhure, "Data Encryption by Image Steganography", International Journal of Information and Computation Technology, ISSN 0974-2239, Volume 4, Number 5, 2014.
  3. Sutaone, M.S., Khandare, M.V, “Image based steganography using LSB insertion technique”, IEEE WMMN, pp. 146-151, January 2008.
  4. DushyantGoyal and Shiuh - Jeng Wang, “Steganographic Authentications in conjunction with Face and Voice Recognition for Mobile Systems”.
  5. JasleenKour ,DeepankarVerma , “ Steganography Techniques – A Review Paper” International Journal of merging Research in Management &Technology ISSN: 2278 - 9359 (Volume - 3, Issue - 5) May 2014.
  6. SumeetKaur, SavinaBansal, and R. K. Bansal., “Steganography and Classification of Image Steganography Techniques”. International Conference on Computing for Sustainable Global Development.
  7. Amr A. Hanafy, Gouda I. Salama and Yahya Z. Mohasseb “A secure covert communication model based on video steganography” 11331. 978 - 1 - 4244 - 2677 - 5 IEEE 2008.
  8. NishantKaushik and Dr. Parveen Sultana. “Remote Authentication Using Face Recognition with Steganography”. Vol (02) _Issue (04) April 2018.

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82.

Authors:

K. R. Baskaran, S. Sabari Rangan, S. Ajithkumar, B. Krishna Prasath

Paper Title:

Likeminded – A Recommender System Based Knowledge Sharing Application for Students

Abstract: Students in college/university face many issues in doing a project, organizing an event, finding a mentor to guide them. The main reason for this problem is lack of proper networking among students and professors. In order to solve this problem, within the college/university, there must be a proper networking channel among students and professors so that they come to know about each other. Consider a scenario in which a mechanical student wants to do an “Object Following Rover” project. In order to do the project, he/she needs skills like mechanical design, image processing, electronic controller, programming and many more. It is not possible for a single student to be expert in all these fields. He/she may be an expert in mechanical design, for the rest he/she needs to find students from other departments or from their seniors, and for mentoring he/she needs to find a professor who has worked in that area. This team formation is possible only if the student knows about what others are doing in the college/university, what other students skill-set are, and in what field they are expert in. This information cannot be obtained easily because a college/university contains 5000+ students and professors. So, it is very difficult for a single student to know about most of his/her fellow students in their college/university. This application provides solution to this problem, by providing a platform for a student to share his/her works, skills and reaching them out to target audience by using suitable recommendation algorithms and helping out students to know what their peers are doing and what are their skill-sets. This paper focuses on the various recommendation approaches that are used for this application in delivering the contents to the target audience.

Keywords: Machine learning, Graph theory, Recommender system, Clustering, Social networking

References:

  1. https://en.wikipedia.org/wiki/List_of_social_networking_websites
  2. Haolin Zhang, FeiyueYe, “A collaborative filtering recommendation based on users interest and correlation of items”, Shanghai, China, 12 July 2016.
  3. Danah M.Boyd , Nicole B. Ellison, “Social Network Sites: Definition, History, and Scholarship” - in Human Communication Research, 17 December 2007
  4. https://docs.docker.com/get-started/
  5. https://link.springer.com/chapter/10.1007/978-981-10-0983-9_9
  6. R. Baskaran, C. Kalaiarasan, “Pre-eminence of combined web pre-fetching and web caching based on machine learning technique”, Arabian Journal for Science and Engineering, ISSN 1319-8025, Vol. 39, No.11, November 2014, pp. 7895-7906.
  7. R.Baskaran, C. Kalaiarasan, “Improved performance by combining web pre-fetching using clustering with web caching based on SVM machine learning method”, International Journal of Computers Communications & Control, ISSN 1841-9836,Vol.11, No.2, April 2016, pp. 166-177

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83.

Authors:

P. Saravana Kumar, T. V. P. Sundararajan, J. Poornimasre

Paper Title:

Fuzzy Based Estimation of Enhanced Colour Illumination for Digital Images

Abstract: Communication between living beings is more essential with the fundamentals of digital forgeries to make an effort to develop a step by step procedure for image detection in a powerful way with the use of various media elementary pictorial representation of any information can be easily manipulated using editing software. Communication between users is carried by image transmission, in which major issue is security that is without any alteration. Image forgery detection is technique for detecting any unauthorized process in image. In compared with existing, use fuzzy classifier to accurate results for comparison instead of SVM classifier. Weintroduced detection method against image splicing, that is joining of two different image fragments. This detection is brought by using conflicting of illuminating colours in whole image. Using illuminate estimation, extracting features such as shape and colour of images and finally classified in Fuzzy logic classifier. Performance of forgery detection is evolved as accuracy using testing process. From our experimental results, conclude that high accuracy provided by extract combining shape and colour features of image, which compared with other.

Keywords: Fuzzy classifier; Feature extraction; Segmentation; Illuminant map; SVM Classifier; Image forgery;

References:

  1. Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." 3D Vision (3DV), 2016 Fourth International Conference on. IEEE, (2016).
  2. Chen, Liang-Chieh, et al. "Attention to scale: Scale-aware semantic image segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. (2016).
  3. Carvalho, Tiago, et al. "Illuminant-based transformed spaces for image forensics." IEEE transactions on information forensics and security4 (2016): 720-733.
  4. Matsushita, Yasuyuki, et al. "Illumination normalization with time-dependent intrinsic images for video surveillance." IEEE Transactions on Pattern Analysis and Machine Intelligence10 (2004): 1336-1347.
  5. Maini, Raman, and Himanshu Aggarwal. "Study and comparison of various image edge detection techniques." International journal of image processing (IJIP) 3.1 (2009): 1-11.
  6. Melin, Patricia, et al. "Edge-detection method for image processing based on generalized type-2 fuzzy logic." IEEE Transactions on Fuzzy Systems6 (2014): 1515-1525.
  7. Guyon, Isabelle, and André Elisseeff. "An introduction to feature extraction." Feature extraction. Springer, Berlin, Heidelberg, (2006): 1-25.
  8. Abe, Shigeo. "Feature selection and extraction." Support Vector Machines for Pattern Classification. Springer, London, (2010): 331-341.
  9. Hordley, Steven D. "Scene illuminant estimation: past, present, and future." Color Research & Application: Endorsed by Inter‐Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur4 (2006): 303-314.
  10. Finlayson, Graham D. "Corrected-moment illuminantestimation." Proceedings of the IEEE International Conference on Computer Vision. (2013).
  11. Zhao, Xudong, et al. "Passive image-splicing detection by a 2-D noncausal Markov model." IEEE Transactions on Circuits and Systems for Video Technology2 (2015): 185-199.
  12. Tian, Shangxuan, et al. "Multilingual scene character recognition with co-occurrence of histogram of oriented gradients." Pattern Recognition51 (2016): 125-134.
  13. Rahmani, Hossein, et al. "HOPC: Histogram of oriented principal components of 3D pointclouds for action recognition." European conference on computer vision. Springer, Cham, 2014.
  14. Lee, Woo-Young, Kwang-EunKo, and Kwee-Bo Sim. "Robust lip detection based on histogram of oriented gradient features and convolutional neural network under effects of light and background." Optik-International Journal for Light and Electron Optics136 (2017): 462-469.
  15. Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, (2005).
  16. Oreifej, Omar, and Zicheng Liu. "Hon4d: Histogram of oriented 4d normals for activity recognition from depth sequences." Proceedings of the IEEE conference on computer vision and pattern recognition. (2013).
  17. Barron, Jonathan T., and Jitendra Malik. "Shape, illumination, and reflectance from shading." IEEE transactions on pattern analysis and machine intelligence8 (2015): 1670-1687.
  18. Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint arXiv: 1502.00873(2015).
  19. Deng, Zhenyun, et al. "Efficient kNN classification algorithm for big data." Neurocomputing195 (2016): 143-148.
  20. Nascimento, Sérgio MC, Kinjiro Amano, and David H. Foster. "Spatial distributions of local illumination color in natural scenes." Vision Research120 (2016): 39-44.
  21. Joze, Hamid Reza Vaezi, and Mark S. Drew. "Exemplar-based color constancy and multiple illumination." IEEE transactions on pattern analysis and machine intelligence5 (2014): 860-873.
  22. Carvalho, Tiago, et al. "Illuminant-based transformed spaces for image forensics." IEEE transactions on information forensics and security4 (2016): 720-733.
  23. Chen, Yushi, et al. "Deep feature extraction and classification of hyperspectral images based on convolutional neural networks." IEEE Transactions on Geoscience and Remote Sensing 54.10 (2016): 6232-6251.
  24. Azar, Ahmad Taher, and Aboul Ella Hassanien. "Dimensionality reduction of medical big data using neural-fuzzy classifier." Soft computing4 (2015): 1115-1127.
  25. Camastra, Francesco, et al. "A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference." Expert Systems with Applications3 (2015): 1710-1716.
  26. Bagis, Aytekin, and Mehmet Konar. "Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling." Transactions of the Institute of Measurement and Control5 (2016): 579-592.

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84.

Authors:

P. Uva Dharini, S. Monisha, K. Narrmadha, K. Saranya

Paper Title:

IOT Based Decision Support System for Agriculture Yield Enhancements

Abstract: In agriculture Expert systems are used in a wide range of operation. Farmers mostly depend on agricultural specialists for decision making. These systems are used by farmers and others without the knowledge of computerusage. In this paper we present the part of expert system in agriculture and its approaches in crop production. It is a knowledge build system for information generation with existing knowledge. This supports farmers in identifying economically strong decision for crop management. On considering the success of expert system, various such systems were developed. IOT plays a key role in agriculture. The abstraction of IOT and its architecture is discussed in this paper. Expert system builds on Internet of Things (IOT) uses the input data gathered in real time is proposed in this paper. In this paper, an expert system in cloud based infrastructure is used. IOT components such as &Cube (IOT Gateway) and Mobius (IOT service platform) are integrated in proposed system. In the proposed system, Kalman filter (KF) is used in sensor node to minimize the noise in sensor fusion. This paper illustrates the need of expert system in agriculture and the advantages of IOT based farming.

Keywords: IOT components such as &Cube (IOT Gateway) and Mobius (IOT service platform)

References:

  1. “Expert System Applications: Agriculture”,AhmedRafea Central Laboratory for Agricultural Expert Systems P.O.Box 100 Dokki Giza Egypt rafea@esic.claes.sci.eg.
  2. AvneetPannu, (2015) “Survey on Expert System and its Research Areas”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 10, April 2015.
  3. “The Applications Of WiFi-based Wireless Sensor Network In Internet Of Things And Smart Grid”, Li Li , Hu Xiaoguang, Chen Ke.
  4. Design and Implementation of a Connected Farm for Smart Farming System,MinwooRyu, Jaeseok Yun, Ting Miao, Il-YeupAhn, Sung-Chan Choi, Jaeho Kim.
  5. 2008 “An Example of Agricultural Expert SystemsBeing Used in India” 1 Pinaki CHAKRABORTY, 2 Dr.Dilip Kumar CHAKRABARTI.
  6. N.R. Prasad, Dr. A. Vinaya Babu,(2006) ” A Study on Various Expert Systems in Agriculture”
  7. RaheelaShahzadi ,JavedFerzund ,Muhammad Tausif , Muhammad AsifSuryani,(2016) ” Internet of Things based Expert System for Smart Agriculture”,(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 7, No. 9, 2016.
  8. Wireless Sensor Network for Smart Irrigation and Environmental Monitoring: A Position Article Raul Moraisa , A. Valente b , and C. Serôdio c IJSTE–International Journal of Science Technology &Engineering|Vol.1,Issue 5, November 2014| ISSN(online):2349-784X,”
  9. Expert Systems In Agriculture: OVERVIEW” SaketMishra , B.Tech. Student Department of Information Technology, ASET, Amity University, Noida.
  10. RICEsmart: An Expert System to Enhance RiceYield”, Shailendra Kumar Yadav1 , Niraj Singhal2 andVivek Yadav3
  11. Suhas M patil, Sakkaravarthi R, (2017) “INTERNET OF THINGS BASED SMART AGRICULTURE SYSTEM USING PREDICTIVE ANALYTICS”.
  12. Expert System for Agriculture Extension “ ,Sujai Das *, LaxmikantaNayak
  13. Farm Operation Monitoring System with Wearable Sensor Devices Including RFID”, 1 Tokihiro Fukatsuand 2 Teruaki Nanseki.
  14. Xian-Yi Chen, Zhi-Gang Jin ,(2012) “Research on Key Technology and Applications for Internet of Things”,2012 International Conference on Medical Physics and Biomedical Engineering.
  15. J. Yelapure , Dr. R. V. Kulkarni,(2012)“Literature Review on Expert System in Agriculture”, International Journal of Computer Science and Information Technologies, Vol. 3 (5).

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85.

Authors:

R. Sathish Kumar, R. Karthikamani, S. Vinodhini

Paper Title:

Mathematical Morphology for Recognition of Hard Exudates from Diabetic Retinopathy Images

Abstract: Diabetic retinopathy is the most frequent form of diabetic eye disease. It will typically affects people who have diabetes for a significant number of years. Retinopathy becomes particularly dangerous because it will affect all diabetics and, increases the risk of blindness, when it is left untreated.To avoid total loss of sight the ophthalmologist will treat the patients by sophisticated laser treatment,if detected effectively at an initial period. One of the main symptoms of initial stage of diabetic retinopathy is analysis of Hard exudates. At the early stage, using mathematical morphology the exudates are identified and removed.

Keywords: Diabetic retinopathy, Mathematical morphology, Fuzzy logic.

References:

  1. Basha, S.S., Prasad, K.S.(2008)”Automatic detection of hard exudates in diabetic retinopathy using morphological segmentation and fuzzy logic”, Int. J. Comput. Sci. Netw. Secur., 8, (12), 211–218
  2. C Gillaes,. and T.Y.Wong. (2007). “Management of diabetic retinopathy: A systematic review”, “The Journal of the American Medical Association, JAWA, 298(8), 902–916”.
  3. Gonzalez R.C., Eddins S.L.: “Morphological reconstruction from digital image processing using MATLAB”, “MATLAB Digest – Academic Edition”.
  4. F. Jelinek, C.Depardieu, ., Huang, W.,Cree, M.J.(2005) “Towards vessel characterisation in the vicinity of the optic disc in digital retinal images”. “Image and Vision Computing Conf., 41–47”.
  5. V. Kumari, N.S.Narayanan.(2010) “Diabetic retinopathy – early detection using image processing techniques”, Int. J. Comput. Sci. Eng., 357–361.
  6. NayomiGeethanjaliRanamuka, RavindaGayan N. Meegama, (2013) “Detection of hard exudates from diabetic retinopathy images using fuzzy logic”, “IET Image Processing , ISSN 1751-9659”.
  7. G. Ranamuka, R.G.N. Meegama (2013). “Detection of hard exudates from diabetic retinopathy images using fuzzy logic, Image Processing, IET 7(2), 121-130”.
  8. Xu, and S. Luo (2009). “Support vector machine based method for identifying hard exudates in retinal images, IEEE Youth Conference on Information Computing and Telecommunication”.
  9. Parivallal,R, Nagarajan,B, SathishKumar,R,(2012) “Zernike Feature Based Pattern Retrieval using Artificial Neural Network’ Indian Journal of Engineering and Technology, 6 No 1 & 2”.
  10. Sathish Kumar, R, Nagarajan, B &Karthikamani, R (2016), “Security Enhancement for Automated Captcha Recognition System in Web Pages’, Transylvanian Review, vol. XXIV, no. 10, 1640-1645.”
  11. Sathish Kumar, R, Nagarajan, B &Karthikamani, Dr. M. Gunasekaran, “Region-Based Object Extraction using Adaptive Neuro-Fuzzy Inference System Combined with Support Vector Machine, Asian Journal of Research in Social Sciences and Humanities, Vol. 7, No. 2, February 2017, pp. 412-427”.

367-370

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86.

Authors:

R. Rajanandkumar, S. Ashokan

Paper Title:

Mn2+-Doped with ZnS Nanoparticle Synthesized by Chemical Co-Precipitation Technique

Abstract: Manganese (Mn2+) ions doped with Zinc sulfide (ZnS) nanoparticles are synthesized by chemical co-precipitation technique. The samples characterized by physical, optical and magnetic properties. XRD results show the 2θ peaks that appear due to reflections from the (100), (002) and (101) cubic crystal planes of ZnS and distinguished to be hexagonal wurtzite structure. At 700oC, ZnS is changed over into ZnO stage due to oxidation reaction. The photoluminescence range of nanoparticles appears as wide glow in green visible region that shows the possibility of creating photonic gadgets. The calculated bandgap (Eg) values is 2.3951 eV. Magnetic hysteresis study of magnetic field (B) indicate the material has good magnetic property similar to intensity magnetization permeability.

Keywords: Hysteresis Curve; XRD; Magnetic properties

References:

  1. Neeleshwar, C.L. Chen, C.B. Tsai, Y.Y. Chen, Phys. Rev. B 71 (2005) 201307.
  2. Karar, F. Singh, B.R. Mehta, J. Appl. Phys. 95 (2004) 656.
  3. Sapara, A. Prakash, A. Ghangrekar, N. Periasamy, D.D. Sharma, J. Phys. Chem. B 109 (2005) 1663.
  4. Chen, V.F. Aguekian, N. Vassiliev, A. Yu. Serov, N.G. Filosofov, J. Chem. Phys. 123 (2005) 124707.
  5. Quan, Z. Wang, P. Yang, J. Fang, Inorg. Chem. 46 (2007) 1351.
  6. Beaulac, P. I. Archer, D. R. Gamelin, J. Solid State Chem, 181 (2008) 1582.
  7. Ge, J. Wang, H. Zhang, X. Wang, Q. Peng, Ya Li, Adv. Funct. Mater. 15 (2005) 303.
  8. Z. Liu, P.X. Yan, G.H. Yue, J.B. Chang, D.M. Qu, R.F. Zhuo, J. Phys. D: Appl. Phys. 39 (2006) 2352.
  9. Q. Li, J.A. Zapien, Y.Y. Shan, Y.K. Liu, S.T. Lee, Appl. Phys. Lett. 88 (2006) 013115.
  10. K. Mandal, A.R. Mandal, S. Das, J. Appl. Phys. 101 (2007) 114315.
  11. Kar, S. Biswas, S. Chaudhuri, P.M.G. Nambissan, Nanotechnology 18 (2007) 225606.
  12. Nogues, J. Sort, V. Langlais, V. Skumryev, S. Surinach, J.S. Muñoz, M.D. Baro, Phys. Rep. 422 (2005) 65.
  13. Iglesias, A. Labarta, X. Batle, J. Nanosci. Nanotechnol. 8 (2008) 2761.
  14. W G Peng, G W Cong, S C Qu and Z G Wang, nanotechnology 16, 1469 (2005).
  15. Chen Jianfeng, Li Yaling, Wang Yuhong, Yun jmmy, and Cao Dapeng, Res. Bull. 39, 185 (2004).

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87.

Authors:

J. Cynthia, C. Bharathi Priya, P. A. Gopinath

Paper Title:

IOT based Smart Parking Management System

Abstract: In this fast-growing economy, the number of vehicle users increases exponentially demanding more parking space. Pervasive presence of smart phone encourages users to prefer mobile application based solutions. Growth of IoT has paved way for integration of mobile devices, wireless communication technologies and mobile Applications. This paper proposes an IoT based Smart parking system that integrates with mobile Application. It provides a comprehensive parking solution both for the user and owner of the parking space. Features are provided for reserving a parking space, authenticating a reserved user, identifying nearest free space depending on the size of the vehicle, navigating to the parking slot and computes accounts information on daily, weekly and monthly basis. IR sensors are used to identify if a parking spot is free. Availability of a free slot with its location information is transmitted using WIFI module technology, microcontroller and wireless communication technology to the server and is retrieved though a mobile application. RFID tag attached to a vehicle is used to authenticate a user who reserves the parking slot on a hourly, daily, weekly or monthly basis. A scheduling algorithm is used to identify the nearest free slot based on the size of a vehicle. The owner of the parking space can get the analytics of the number of free and available slots for a given period, the occupancy rate on week days and weekend and the amount collected for a given period and can use it for fixing variable parking fees. The mobile application is designed to provide rich customer experience.

Keywords: Smart Parking, IoT, Mobile Application, RFID, Analytics

References:

  1. Supriya Shinde1, AnkitaM Patial2, pSusmedha Chavan3,Sayali Deshmukh4, and Subodh Ingleshwar5 “IOT Based Parking System Using Google”, I-SMAC,2017,pp.634-636.
  2. HemantChaudhary, PrateekBansal., B.Valarmathi,” Advanced CAR Parking System using Arduino”, ICACCSS, 2017.
  3. Nastaran Reza NazarZadeh, Jennifer C. Dela,”Smart urban parking deducting system” ICSCE, 2016, pp-370-373.
  4. PavanKumarJogada and VinayakWarad, “Effective Car Parking Reservation System Based on Internet of things Technologies “.BIJSESC, 2016, Vol. 6, pp.140-142.
  5. Yashomati R. Dhumal1, Harshala A. Waghmare2, Aishwarya S. Tole2, Swati R. Shilimkar2,”Android Based Smart Car Parking System”-IJREEIE, Vol. 5, Issue 3, pp-1371-74,mar-2016.
  6. Faiz Ibrahim Shaikh, Pratik NirnayJadhav, Saideep Pradeep Bandarakar” Smart Parking System based on embedded system and sensor Network” IJCA, vol.140.pp.45-51.Apr-2016.
  7. RicardGarra, Santi Martinez, and Francesc Seb’e” A Privacy-Preserving Pay-by-phone Parking system”IEEE-TVT, pp.1-10, Dec-2016.
  8. Amir O. Kotb, Yao-chunShen, and Yi Huang “Smart parking Guidance, Monitoring and Reservation: A Review,” IEEE-ITSM, pp.6-16.Apr-2017.
  9. Ching-FeiYang, You-HueiJu, Chung-Ying Hsieh “Iparking -a real-time parking space monitoring and guiding system”, Elsevier, pp.301-305. Apr-2017.
  10. Fei-Yue Wang, Liu-Qing Yang, Fellow, Jian Yang,” Urban Intelligent Parking system based on Parallel Theory”, IEEE-ICNC, 2016.
  11. Fei-Yue Wang, Liu-Qing Yang, Fellow, Jian Yang, [2016],” Urban Intelligent Parking system based on Parallel Theory”, IEEE-Computing, Networking and Communications, Mobile Computing and Vehicle Communications.
  12. TarekAlmahdi and chittrurivenkatratnum, [2016]”Intelligent automated parking System hacking intimation Features,”IEEE-computing and engineering.
  13. Huey-Der Chu, Yong-QuanYeh, Yi-Cheng Lin, Meng-hung Lai, Yi-Jie Lin, [2017],” The Study Intelligent Roadside Park Charging Systems”, IEEE- International Conference on Applied System Innovation, pp.1064-67.
  14. J.Bonde,”Automated car parking systemCommanded by Android application”, IEEE Conf., 05-03, Jan 2014.
  15. YangengGeng, Christos G. Cassandras,” A new ‘Smart parking’ system Infrastructure and Implementation “, 1278- 1287 Science Direct, Social and Science behavioural sciences, 2012.
  16. AtaurRehman, M.M.Rashid, A. Farhana and N. Farhana, “Automatic parking management And parking fee collection based on number Plate recognition”, International journal of Machine learning and Computing.
  17. Norazwinawati Bashar Uddin, R. Yusnita, FarizaNorbaya,”intelligent parking space Detection system based on image processing”, International Journal of Innovation, Management and Technology, 2012.
  18. A. R. Sarkar, A. A. Rokoni, M. O. Reza, M. F. Ismail, “Smart parking system with image Processing facility”, I. J. Intelligent System and Application, 2012.
  19. Losilla, A.J Garcia-Sanchez, F. Garcia-Sanchez and J. Garcia- Haro, “On the Role of Wireless Sensor Networks in intelligent Transportation Systems, ICTON, Pp. 2161- 2056, 2012.
  20. Chinrungrueng, S. Dumnin and Pongthornseri, “I Parking: A Parking Management Framework”, 11th International Conference on ITS Telecommunications, Pp.63-68, 2011.
  21. Hirakata, A. Nakamura, K. Ohno and M. Itami, “Navigations System using ZigBee Wireless Sensor Network for Parking”, 12th International Conference on ITSTelecommunications, Pp. 605-609, 2012.
  22. [http://www.laweekly.com/news/five-los-angeles-parking-secrets-and-111-places-to-park- google-map-4171416].
  23. [https://socialcops.com/case-studies/data-collection-for-location-mapping-parking-lots-india/].
  24. Senthil , M. Suguna , J. Cynthia, “Mapping The Vegetation Soil And Water Region Analysis Of Tuticorin District Using Landsat Images”, IJIEST ISSN (2455-8494), Vol.03, No. 01, Jan 2018
  25. BharathiPriya,,Dr.S.Siva Kumar, “ A survey on localization techniques in wireless sensor networks”, International Journal of Engineering & Technology, 7 (1.3) (2018) 125-129

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88.

Authors:

V. Vanitha, V. P. Sumathi, V. Soundariya

Paper Title:

An Exploratory Data Analysis of Movie Review Dataset

Abstract: The film industry plays a major role in the planetary or world-wide economy. It is the symbolic contributor to the global economy. Every year more than hundreds to thousands of movies are released to the public audience with the hope that the movies getting released will be the next block buster. According to the movie industry statistics, six to seven movies out of ten movies gets unprofitable, only one third of the movie gets success. The producers, studios, investors, sponsors in the movie industry are alike interested in predicting the box office success of the movie. This paper work is on analysing the film genre, the release date around holidays, the release month of movies, the languages and country with more movies from the movie review dataset. There are attributes (country, languages, genre, movie release date, budget and revenue) taken from the dataset and the derived attributes (release month of the movie derived from release date of movie and profit from budget and revenue) is analysed to determine the movie performance. The analysed data is plotted in graphs for statistical observation of the movie success.

Keywords: predicting box office success, block buster, film genre, genre count, release month, movie profit, and movie review dataset.

References:

  1. Quader, N., Gani, M. O., & Chaki, D. (2017, December). Performance evaluation of seven machine learning classification techniques for movie box office success prediction. In Electrical Information and Communication Technology (EICT), 2017 3rd International Conference on (pp. 1-6). IEEE.
  2. Jain, V. (2013). Prediction of movie success using sentiment analysis of tweets. The International Journal of Soft Computing and Software Engineering, 3(3), 308-313.
  3. Vinodhini, G., & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6), 282-292.
  4. Basari, A. S. H., Hussin, B., Ananta, I. G. P., & Zeniarja, J. (2013). Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization. Procedia Engineering, 53, 453-462.
  5. Sumathi, VP, Kousalya, K, Vanitha, V, Cynthia, J, (2018), ‘ Crowd estimation at a social event using call data records’, Int. J. Business Information Systems, Vol 28, No. 2, pp 446-461.

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89.

Authors:

K. Saranya, S. Jayanthy

Paper Title:

BCI based EEG Signals for Emotion Classification

Abstract: The human brain which is the central processing unit of the human machine is responsible for multiple tasks such as perception, cognition, attention,emotion, memory and action. In human life emotions significantly affect one’s wellbeing. Providing methodologies to access to human emotions would be a key for successful human machine interaction. Understanding Brain Computer Interface (BCI) techniques to identify the emotions also help in aiding people to interact with the world like a common man. Many techniques were devised to identify the human emotions of which usage of EEG signals to classify the emotions as happiness, fear, anger and sadness were found promising. These emotions are evoked by many means such as showing subjects pictures of smile and cry facial expressions, by hearing to emotionally mixed audios or by watching videos and at time combination of these.This paper is a survey of all the optimized methods to filter the EEG signal and comparative study of the various classification methods used to classify the emotions is carried out and a multimodal classification technique which makes use of EEG signals and at the same time efficiency is measured with Natural Language Processing(NLP) is proposed for improving the accuracy.

Keywords: EEG signal, Emotion Classification, BCI, multimodal, NLP.

References:

  1. Christiane Goulart, Javier Castillo, Carlos Valadão, TeodianoBastos, ElieteCaldeira,”EEG analysis and mobile robot as tools for emotion characterization in autism,”from 5th Congress of the Brazilian Biotechnology Society (SBBIOTEC) Florianópolis, Brazil. 10-14 November 2013.
  2. HimaanshuGaubaa, Pradeep Kumara, ParthaPratimRoya, Priyanka Singh a,DebiProsadDograb, BalasubramanianRamana,”Prediction of advertisement preference by fusing EEG response and sentiment analysis,”Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, Neural Networks 92 (2017) 77–88.
  3. NoppadonJatupaiboon, Setha Pan-ngum and PasinIsrasena,”Real-Time EEG-Based Happiness Detection System,”Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330,Thailand, Hindawi Publishing Corporation,TheScientificWorld Journal Volume 2013, Article ID 618649, 12 pages
  4. Khalili, M. H. Moradi,”Emotion Recognition System Using Brain and Peripheral Signals:Using Correlation Dimension to Improve the Results of EEG” ,Proceedings of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009.
  5. Robert Jenke, Angelika Peer, and Martin Buss,”Feature Extraction and Selection for Emotion Recognition from EEG”,IEEE Transactions on affective computing, vol. 5, no. 3, july-september 2014.
  6. Steven G. Masonand Gary E. Birch,” A General Framework for Brain–Computer Interface Design”, IEEE Transactions on neural systems and rehabilitation engineering, vol. 11, no. 1, march 2003.
  7. MandeepKaur , P. Ahmed, M. QasimRafiq"Technology Development for Unblessed People using BCI: A Survey" International Journal of Computer Applications (0975 – 8887)
  8. Gary Garcia Molina, TsvetomiraTsoneva, Anton Nijholt," Emotional Brain-Computer Interfaces"
  9. TeodianoFreireBastos-Filho,AndreFerreira,AnibalCotrinaAtencio, Sridhar Arjunan,DineshKumar"Evaluation of Feature Extraction Techniques in Emotional State Recognition", International Conference on Intelligent Human Computer Interaction, Kharagpur, India, December 27-29, 2012
  10. Mina Mikhail,Khaled El-Ayat, James A. Coan, John J.B. Allen,"Using minimal number of electrodes for emotion detection using brain signals produced from a new elicitation technique" Int. J. Autonomous and Adaptive Communications Systems, Vol. 6, No. 1, 2013
  11. Xiao-Wei Wang, Dan Nie, Bao-Liang Lu,"Emotional state classification from EEG data using machine learning approach " Neurocomputing,Elsevier,2013
  12. RohanHundia,"Brain Computer Interface-Controlling Devices Utilizing The Alpha Brain Waves" International journal of scientific & technology research volume 4, issue 01, January 2015.
  13. Saranya, K., Hema, M.S., Chandramathi, S.”Data fusion in ontology based data integration,” International Conference on Information Communication and Embedded Systems, ICICES 2014
  14. K., Jayanthy.S, Machine Learning Techniques for Onto-based Emotional classification of text, International Journal of Pure and Applied Mathematics,2018

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90.

Authors:

K.Saranya, S. J. Syed Ali Fathima, Mohd N Azri Ismail

Paper Title:

Enhancing Customer Engagement using Beacons

Abstract: Most of the people these days use mobile phones for almost everything. Many technologies have been used in a smartphone which provides a variety of services like Social networking, payment, marketing etc. A new way of marketing which uses BLE beacon technology can be incorporated with a mobile application which is used to send personalized notifications to customers using the Indoor positioning system using Beacons. Existing GPS can’t be used for Indoor Positioning System, as there are some shortcomings like power consumption and also the accuracy of the system indoors due to obstructions to the satellite. RFID technology is accurate indoors but the range is too small which is only up to 15 meters. To overcome this Beacon is used which is a low-cost, low-energy transmitter equipped with Bluetooth Low Energy or BLE also known as Bluetooth 4.0 or Bluetooth Smart that can end proximity-based, context-aware messages over distances ranging from 15 cm to 70m. The Hardware used here is a beacon which transmits a signal which contains a Unique ID at regular intervals which can be detected within a certain range. A custom Mobile App is developed which receives the signal while in a certain proximity of the beacon and is programmed to calculate the distance. The Custom Mobile App connects to a Web server which makes use of the Unique Id received along with the signal to retrieve information corresponding to that Unique ID. The Web server contains a Database which keeps track of all the Unique Id’s and the appropriate information corresponding to each Unique ID.

Keywords: Bluetooth Low Energy, Accuracy Positioning, Indoor Positioning, Radio map- based positioning, Practical Path Loss Model

References:

  1. MyunginJi, Jooyoung Kim, JuilJeon, YoungsuCho.,”Analysis of Positioning Accuracy corresponding to the number of BLE beacons in Indoor Positioning System”ISBN 978-89-968650-5-6
  2. DalalZaim and MostafaBellafkih.” BLE based Geomarketing system” ISBN-978-1-5090-5781-8
  3. The potential of Beacon technology (techcrunch.com/2014/11/01/unlocking-the-potential-of-beacon-technology/)
  4. iBeacon Architecture(www.slideshare.net/johngifford/create-rich-mobile-apps-using-salesforce1-and-i-beacon
  5. Beacon V. IOS 8.” Chain Store Age 90.5 (2014): 11A-13A. Business Source Complete.Web. 24 Sept. 2014.
  6. Brousell, Lauren. “5 Things You Need To Know.” Cio (13284045) (2014): 18. Business Source Complete.Web. 24 Sept. 2014
  7. Evans, Michelle. “The Rise of Beacon Technology and Prospects of it Displacing NFC (Part 2).” 1 November. 2013. Euromonitor Interview Series. Elon University Belk Library,
  8. O’Donnell, Fiona. “Marketing to Sports Fans – US.” July 2014. Mintel.Elon University Belk Library, Elon, NC. 24 Oct. 2014. <http://academic.mintel.com

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91.

Authors:

S. Kirubakaran, S. Karthick, S. P. Prakash

Paper Title:

Increasing Privacy for Private Database in Cloud Environment

Abstract: After Personal computing, main frame and client server Cloud computing is a fifth generation computing. It focuses on resource sharing and computations and is a network based environment. The data will be stored enduringly information in servers on the internet and cached momentarily on clients. It has the advantage of sinking cost by allocation of computing and resource storage. Azure, EC2 Application og Google, Aneka are the processor that utilize for its operation for effective computations. The necessary resources are utilized by user through the internet. The will be raise in confidentiality issue when provider depends on other provider for resource utilizing. As there is no limits in cloud computing the data can be physically positioned anywhere in the globe. Hence the problems regarding data authentication and privacy are in raise. The privacy can be obtained by imposing access policy or by encrypting using cryptographic tools. Without leaking the important information of the owner the safety can be made ensured by safeguarding from hackers. In this proposed work a implementation authentication and authentication based on data access module and anonymity. Programming is carry out using JAVA platform and back up management using MySQL and Advanced Encryption Standard security algorithm is apply for ensuring security framework.

Keywords: Data Authentication, Anonymity, Encryption standard, Security.

References:

  1. Garfinkel SL, “A less personal computer Technology Review”’, May 2010.
  2. Stone, B. & Vance, A, “Companies slowly join cloud computing”’. New York Times, 2010.
  3. Chow, R., Golle, P., Jakobsson, M., Shi, E., Staddon, J., Masuoka, R. & Molina, J, “Controlling data in the cloud: outsourcing computation without outsourcing control”, In Proceedings of the 2009 ACM workshop on Cloud computing security.
  4. Shi, E., Bethencourt, J., Chan, T.H., Song, D. & Perrig, A., “Multi-dimensional range query over encrypted data”’ In IEEE Symposium on Security and Privacy, 2007.
  5. Sion, R. & Carbunar, B’ “On the computational practicality of private information retrieval”, In Proceedings of the Networkand Distributed Systems Security Symposium 2007.
  6. Rivest, R.L., Adleman, L. & Dertouzos, M.L., “On data banks and privacy homomorphisms”, Foundations of secure computation 1978..
  7. Yao, A.C, “Protocols for secure computations”’ In IEEE 23rd Annual Symposium on Foundations of Computer Science.
  8. Micciancio, D, “A first glimpse of cryptography's Holy Grail”’ Communications of the ACM, 2010.
  9. Shen, E., Shi, E. & Waters, B, “Predicate privacy in encryption systems”’ In Theory of Cryptography Conference 2009.
  10. Gentry, C. & Boneh, D, “A fully homomorphic encryption scheme”’ Stanford University 2009.
  11. Goldreich, O, “Foundations of cryptography–A prime”’. Foundations and Trends in Theoretical Computer Science, 2005.
  12. Sudha, M., Rao, D.B.R.K. & Monica, M, “A comprehensive approach to ensure secure data communication in cloud environment”’ International Journal of Computer Applications 2010.
  13. Pearson, S, “Taking account of privacy when designing cloud computing services”, In Software Engineering Challenges of Cloud Computing, 2009.
  14. Wang, J. & Le, J, “Based on private matching and min-attribute generalization for privacy preserving in cloud computing”, Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010.

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92.

Authors:

Yamunathangam, K. Pritheka, P. Varuna

Paper Title:

IOT Enabled Air Pollution Monitoring and Awareness Creation System

Abstract: The air pollution rates now a days are drastically increasing in all the developed and the developing countries which requires a more portable and cost effective solution. The proposed system includes the design for monitoring air pollution and creating awareness among the public. This paper aims at using IOT along with cloud to make the services real time and faster. The proposed system is installed in a particular locality where there is acute air pollution. The level of each hazardous pollutant is monitored at periodic intervals. The Air Quality Index (AQI) for the observed pollutants is determined and awareness is created among the public through an android app which displays the level of each observed pollutant and also the air quality index in that particular location. Thus the quality of air in that area can be understood by the public by viewing the concentration of the gases in both numerical and graphical format. Further this system is to be extended in future by allowing the public to register themselves in an app which pushes weekly or monthly air quality report through message which reaches the user as a notification that is more comfortable in access.

Keywords: Arduino, hazardous pollutants, AQI, Thingspeak, Android

References:

  1. Kgoputjo Simon Elvis Phala, Anuj Kumar, and Gerhard P.Hancke, “Air Quality Monitoring System Based on ISO/IEC/IEEE 21451 Standards” ,IEEE Sensors Journal, Vol. 16, No. 12, June 15, 2016.
  2. Khaled Bashir Shaban, Senior Member, IEEE, Abdullah Kadri, Member, IEEE, and Eman Rezk,“Urban Air Pollution Monitoring System”,With Forecasting Models, IEEE Sensors Journal, Vol. 16, No. 8, April 15, 2016.
  3. Ramagiri Rushikesh and Chandra Mohan Reddy Sivappagari, “ Development of IoT based Vehicular Pollution Monitoring System”, International Conference on Green Computing and Internet of Things (ICGCIoT),2015.
  4. Dongyun Wang, Chenglong jiang, Yongping Dian, “Design of air quality monitoring system based on internet of things”,10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA),2016.
  5. Akshata Tapashetti and Divya Vegiraju, “IoT-Enabled Air Quality Monitoring Device - A Low Cost Smart Health Solution”,IEEE Global Humanitarian Technology Conference,2016.
  6. Navreetinder Kaur,Rita Mahajan ,Deepak Bagai, ” Air Quality Monitoring System based on Arduino Microcontroller”,International Journal of Innovative Research in Science,Engineering and Technology Vol. 5, Issue 6, June 2016.
  7. Marin B. Marinov, Ivan Topalov, Elitsa Gieva and Georgi Nikolov, “Air Quality Monitoring in Urban Environments”, 39th International Spring Seminar on Electronics Technology (ISSE),2016.
  8. Santosh G Bhandarakawathe, Prof.S. B. Somani, “A Survey on WiFi Based Air Pollution Monitoring System”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2017.
  9. V.Saikumar, M.Reji P.C.Kishoreraja , “IOT based Air Quality Monitoring system”,International Journal of Pure and Applied Mathematics Volume 117 No. 9 2017, 53-57.
  10. Neha R. Rewatkar,Prof. Deepali M. Khatri, “A Review: Cost Effective IOT Based Air Pollution Monitoring and Air Quality Analysis”, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169,Volume: 5 Issue: 1,2017
  11. Siva Shankar Chandrasekaran, Sudharshan Muthukumar and Sabeshkumar Rajendran, “Automated Control System for Air Pollution Detection in Vehicles”, 4th International Conference on Intelligent Systems, Modelling and Simulation,2013.

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93.

Authors:

V. Sudha, N. Jeba, R. Akiladevi

Paper Title:

A Survey on the Modern Technologies used in Public Toilets

Abstract: In a country like India, where 60% of the world population do open defecation public toilets play a vital role. Though now-a-days open defecation is reducing by the open toilets constructed by the government, the maintenance of these toilets in hygienic manner is still an issue. The usage of the public toilets is reduced due to the improper maintenance of the toilets and foul smell from it. Moreover, the peoples started using the open places which leads to many health problems. One of the health issue caused is the diarrhoea. In India, this disease kills one child per minute. Hence, the issue of maintenance of the public toilet has to be dealt seriously. In this paper, we survey on the technologies proposed for modern public toilet facilities.

Keywords: Open Defecation, Open Toilets, Water, And Maintenance.

References:

  1. Bilgi, O.Ozturk, A.G. Gulnerman, “Navigation system for blind, hearing and visually impaired people in ITU Ayazaga campus”, ICCNI, IEEE Conference December 2017.
  2. Cynthia J. and Sumathi Vembu, “Adaptive Service Discovery Protocol for Mobile Ad hoc Networks”, Eurpean Journal of Scientific Research, Vol. 49, 6-17, 2011.
  3. Dr Donata Dubber and Dr Laurence Gill, “Water saving technologies to reduce water consumption and waste water production in Irish households”” STRIVE report.
  4. Huang, S.Yu and H.Syu, “Development of the Smart Toilet Equipment with Measurements of Physiological Parameters”, UIC/ATC, page 9-16, IEEE Computer Society, 2012.
  5. Lan, G.Zhai, W.Lin,”Lightweight smart glass system with audio aid for visually impaired people”, TENCON 2015, January.
  6. Murati, “Factors of Host and Environmental that Affecting Behaviour of Open Defecation”, Thesis, Semarang Master Program of Epidemiology School of Public Health Diponegoro University.
  7. Kin Wai Michael Siu, “Design Quality of Public Toilet Facilities”, Int. J. Rel. Qual. Saf. Eng., 13, 341, 2006.
  8. Kitisak Osathanunkul, K.Hantrakul, P.Pramokchon, P.Khoenkaw, N.Tantitharanukul, “Configurable automatic smart urinal flusher based on MQTT protocol”, ICDAMT, 2017.
  9. S.Nandakumar, V.Sudha and D.Aswini, “Fault detection in overhead power transmission”, IJPAM, volume 118, 377-381, 2018.
  10. Panek, G.Edelmayer, Christian Daye and W.L.Zagler, “Concept and Evaluation Mehtodology of Adjustable
  11. Toilets for Old Persons and People with Disabilities”, The 3rd European Medical and Biological Engineering Conference, EMBEC’05, Pg. 20-25, November 2005
  12. Parth M. Sarode, “Design and Implementation of Automatic Flush System for Sanitation in Public Toilets”,JRBAT, Vol. II, Issue (7), Nov 2015: 56-58.
  13. http://www.indiawaterportal.org/articles/factors-affecting-toilet-adoption-rural-india
  14. http://www.wrap.org.uk/sites/files/wrap/EN667R_v2_Feb_9_2009.pdf
  15. https://www.digitaltrends.com/home/google-smart-bathroom-patent/

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94.

Authors:

Aswini D, Guruprasath. J, Raghuselvapraveen. S

Paper Title:

An Exploratory Data Analysis of Bowler’s Performance in IPL

Abstract: Indian premier league is one of the most popular cricket league. So, it attracts more cricket admirers. Bowlers are the one of key players for the every team especially in twenty – twenty cricket. Their performance change the match result. In this study, the bowler’s performance is analysed in every season of the Indian Premier League (IPL). Performance parameters chosen are number of wickets, number of maiden overs bowled, economy rate and number of boundaries. Among the various parameters, economy rate and wickets are the significant for the match. We also analysed which bowler performed consistently and also whom performed well in good batting pitch.

Keywords: wickets taken, maiden overs bowled, economy rate, consistent, bowler’s performance.

References:

  1. Prakash, C. D., Patvardhan, C., & Lakshmi, C. V. (2016). MAYO Index for Deep Analytics of Price and Performance of IPL Players. International Journal of Computer Applications, 150(2).
  2. Saikia, H., Bhattacharjee, D., & Lemmer, H. H. (2012). Predicting the performance of bowlers in IPL: an application of artificial neural network. International Journal of Performance Analysis in Sport, 12(1), 75-89.
  3. Kumar, A., & Sindhu, R. (2014). Reflecting Against Perception: Data Analysis of IPL Batsman.
  4. Dey, P. K., Ghosh, D. N., & Mondal, A. C. (2011). A MCDM approach for evaluating bowlers performance in IPL. Journal of emerging trends in Computing and Information Sciences, 2(11), 563-73.
  5. Sankaran, S. (2014). Comparing Pay versus Performance of IPL Bowlers: An application of Cluster Analysis. International Journal of Performance Analysis in Sport, 14(1), 174-187.
  6. Khan, U. M., Kabir, Z., & Malik, A. W. (2015). Perception Model to Analyze Football Players' Performances. In PACIS (p. 146).
  7. howstat.com/cricket/Statistics/Players/PlayerHomeAway.asp?
  8. https://en.wikipedia.org/wiki/Indian_Premier_League
  9. https://en.wikipedia.org/wiki/List_of_Indian_Premier_League_records_and_statistics
  10. https://en.wikipedia.org/wiki/Icon_player
  11. https://www.iplt20.com/
  12. https://timesofindia.indiatimes.com › News › Sports News
  13. cricbuzz.com/cricket-series/2430/indian-premier-league-2016/matches

403-406

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95.

Authors:

Thiruvaazhi.U, Arthi.R

Paper Title:

Threats to Mobile Security and Privacy

Abstract: Market research reports from Forrester estimates the global mobile penetration to be around 50% in 2017 and is forecast to reach 66% by 2022. In India, Mobile Internet Penetration using Smart Phone has reached 36% as of 2016 from 0.1% in 1998. With the grand new push towards Digital India and low cash transactions, mobile transactions including mobile payments have seen significant thrust in the recent times. Many startups as well as major enterprises and government has been continually providing and promoting many mobile apps for variety of transactions from multimedia messaging to digital payments. In this paper, we present a survey of the threats and a clear demonstration of the risks of usage of mobile on security and privacy of the person using it and his/her communications.

Keywords: Smart Phone has reached 36% as of 2016 from 0.1% in 1998.

References:

  1. [Forrester, 2017] Forrester Report of SatishMeena and Sanjay Kumar, “Forrester Data: Mobile, Smartphone, And Tablet Forecast, 2017 To 2022 (Global)” accessed from
  2. https://www.forrester.com/report/Forrester+Data+Mobile+Smartphone+And+Tablet+Forecast+2017+To+2022+Global/-/E-RES138971 , July 2017, Forrester
  3. [Statista 2015] Statista Data on “Share of mobile phone users that use a smartphone in India from 2014 to2019 “accessed from
  4. https://www.statista.com/statistics/257048/smartphone-user-penetration-in-india/ , 2015, Statista.com
  5. [Symantec MTIR 2018]Symantec “Mobile Threat Intelligence Report 2017” accessed from https://www.symantec.com/content/dam/symantec/docs/reports/mobile-threat-intelligence-report-2017-en.pdf, April 2018
  6. [Symantec Q1 MTIR 2018] Symantec “Ten Years of (Hacking) iOS”, Q1 2017 Mobile Threat Intelligence Report from
  7. https://www.symantec.com/content/dam/symantec/docs/reports/skycure-mobile-threat-intelligence-report-q1-2017-en.pdf , 2017
  8. [Symantec ISTR 2018] Symantec “Internet Security Threat Report 2018”, from
  9. http://resource.symantec.com/LP=5538?cid=70138000000rm1eAAA , March 2018
  10. [Verizon MSI 2018] Verizon “Mobile Security Index 2018”, from
  11. http://www.verizonenterprise.com/verizon-insights-lab/mobile-security-index/2018/ , 2018
  12. [Zimperium 2017], Zimperium Global Threat Report 2017 from
  13. https://go.zimperium.com/threat_report_q2_2017”,
  14. 2017
  15. [McAfee MTR Q1 2018], “McAfee Mobile Threat Report Q1, 2018”, from
  16. https://www.mcafee.com/enterprise/en-us/assets/reports/rp-mobile-threat-report-2018.pdf , 2018
  17. G. Delac, M. Silic and J. Krolo, "Emerging security threats for mobile platforms," 2011 Proceedings of the 34th International Convention MIPRO, Opatija, 2011, pp. 1468-1473.
  18. L.Latha, S.Thangasamy, “A robust person authentication system based on score level fusion of left and right irises and retinal features”, Procedia Computer Science, Volume 2, 2010, Pages 111-120, ISSN 1877-0509, from
  19. http://www.sciencedirect.com/science/article/pii/S1877050910003443.
  20. L Latha and S Thangasamy. “Providing multimodal biometric authentication using five competent traits”, The Imaging Science Journal, Volume 61, Pages 212 – 218, Taylor & Francis, from
  21. https://doi.org/10.1179/1743131X11Y.0000000033

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96.

Authors:

Anandha Moorthy Appusamy, Prakash Eswaran, Madheswaran Subramaniyan

Paper Title:

Investigation on Dry Sliding Wear Behaviour of Self-Lubricating Metal Matrix Composites Reinforced with Fly ash and Solid Lubricant

Abstract: The present study is focussed to explore the dominant factors on metal removal of self-lubricating composite materials through rotating disc. The proposed composite material is reinforced with fly ash and Boron Nitride. As referred with ASM standard AA2218 alloy is suitable for tribological applications. Composites samples are processed by liquid metal stir casting route. AA2218 matrix alloy was reinforced with Five, Ten and Fifteen weight percentage of fly ash as hard reinforcement particles and five percentages. Levels of Boron Nitride as the second stage fortification particles in the composite material. Ninety percentage of hardness value has been correlated with tensile strength of the composite material. The hardness of the amalgamated specimens are experimented, test results shows that BHN values are significantly higher than the structural material. Dry sliding wear behavior of test samples are experiment with pin-on-disc apparatus. Affecting parameters of load on pin, weight level of support content and sliding speed on amount of wear was tested. Formation of trials through DOE approach utilizing Taguchi technique was embraced to break down the exploratory outcomes. Because of Taguchi investigation, mix of most appropriate qualities is accounted for. Motion to-commotion proportion and examination of change (ANOVA) is been utilized to explore the impact of process parameters on wear rate.

Keywords: Wear, Hardness, ASM, Design of Experiment & Analysis of Variance.

References:

  1. Rohatgi PK, Nikhil Gupta and Weiss D, “Applications of Fly-ash in Synthesizing Low Cost Metal Matrix Composites for Automotive and other Engineering Applications”, Journal of Minerals, Metals and Materials society , (2006), Volume 58, Issue No.11, pp.71-76.
  2. Radhakrishna K and Mahendra KV, “Fabrication of Al-4.5% Cu Alloy with Fly-ash Metal Matrix Composites and its Characterization”, Material Science, (2007), Volume 25, Issue No.1, pp. 57-68.
  3. Lefebvre LP, White B and Thomas Y, “Effects of Lubricants and Compacting Pressure on the Processability and Properties of Aluminum P/M Parts”, Journal of Light Metals, (2002), Volume 2, pp. 239-246.
  4. Venkateswaran S and Suresh N, “Influence of Cenosphere Fly ash on the Mechanical properties and Wear of permanent moulded euetic Al-Si alloys”. Material Science, (2010), Volume 28, Issue No.1.
  5. ŞenerKarabulut, “Optimization of surface roughness and cutting force during AA7039/Al2O3metal matrix composites milling using neural networks and Taguchi method”, Measurement (2015), Volume 66, pp 139 - 149.
  6. Kaushik & Singhal, “Wear conduct of aluminum matrix composites: A parametric strategy using Taguchi based GRA integrated with weight method”, Cogent Engineering (2018).
  7. Ulhas K. G.B. and Annigeri Veeresh Kumar, Method of stir casting of Aluminum metal matrix Composites: A review”, Materials Today, (2017), Volume 4, Issue 2, pp. 1140 – 1146.
  8. Derby B and Walker JR, “Metal matrix composites: production by the stir casting method”, Journal of Material Processing Technology, (1999), Volume 92-93, pp. 1-7.
  9. Sijo, “Analysis of Stir Cast Aluminium Silicon Carbide Metal Matrix Composite: A Comprehensive Review”, Procedia Technology, (2016), Volume 24, pp. 379- 385.
  10. Cappleman GR, “The interface region in squeeze-infiltrated composites containing &alumina fibre in an aluminium matrix”, J. Sci., (1985), Vol. 20, pp. 2159-2168.
  11. Inegbenebor AO, “Aluminum Silicon Carbide Particulate Metal Matrix Composite Development Via Stir Casting Processing”, Silicon, (2018), Volume 10, Issue No. 2, pp. 343-347.
  12. Zongyi M, Jing B and Yinxuan G, “Abrasive wear of discontinuous SiC reinforced aluminium alloy composites”, Wear, (1991), Volume 148, Issue 1, pp. 287 - 293.

413-416

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97.

Authors:

Nandakumar G. S, Viswanandhne S.

Paper Title:

A Survey on Item Selection Approaches for Computer Based Adaptive Testing

Abstract: Assessment is an essential part in determining the level of attainment of education. In spite of availability of several formal and informal methods, Computer Based Assessment (CBT) is predominantly used for very large scale assessments. Adaptive Testing has better estimation capabilities if the standard of its items (questions) match the ability of the candidate. Items that are too simple or too difficult give unpredictable reactions and can't give much information about the ability of the student. It is therefore essential to select items from the large pool so that the selected item gives maximum information about the ability of the student. This paper reviews the various methods for the item selection during the computerized adaptive testing.

Keywords: Computer Adaptive Testing, Item selection approaches.

References:

  1. Richard M. Luecht and Stephen G. Sireci,” A Review of Models for Computer-Based Testing”, in College Board, ReSearch Report 2011-12
  2. Mariana Lilley and Trevor Barker, “An Evaluation of a Computer Adaptive Test in a UK University Context”, University of Hertfordshire, United Kingdom
  3. Daniel R. Eignor, Martha L. Stocking, Walter D.Way, Manfred Steffen , “Case Studies In Computer Adaptive test Design through Simulation”, Educational Testing service, NewJersey, November 1993
  4. Mansoor Al-A'ali, “Implementation of an Improved Adaptive Testing Theory”, International Forum of Educational Technology & Society (IFETS), ISSN 1436-4522,2007
  5. V. Natarajan, “Basic Principles of IRT And Application to Practical Testing & Assessment”, in IACAT conferences at Amsterdam, Asilomar (US),2008
  6. Hua-Hua Chang and Zhiliang Ying, “a-Stratified Multistage Computerized Adaptive Testing With b Blocking”, in Sage Publication, Educational Testing Service, 25 No. 4, December 2001, 333–341
  7. Wim J. van der Linden, “Constrained Adaptive Testing with Shadow Tests”, Statisticsfor Social and Behavioral Sciences, DOI 10.1007/978-0-387-85461-8 2
  8. Wei He,” Comparison of Four Item-Selection Methods for Severely Constrained CATs”, Northwest Evaluation Association (NWEA),2013
  9. Chingwei David Shin, YuehmeiChien, Walter Denny Way, Len Swanson,” Weighted Penalty Model for Content Balancing in CATS”, in Pearson, April 2009.
  10. Van der Linden, W. J. (1998). Bayesian item-selection criteria for adaptive testing. Psychometrika, 62, 201–216
  11. Randall D. Penfield , Applying Bayesian Item Selection Approaches to Adaptive Tests Using Polytomous Items, 2006

417-419

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98.

Authors:

K. K. Megavarthini, S. Vignesh, S. Paul Joshua, A. P. Gokulraj, V. Indirakumar

Paper Title:

Design and Implementation of Alert System for Monitoring the Ambient Temperature Using Cloud Computing In Hospital Regions

Abstract: The incubators in most of all the hospitals require a device for monitoring the ambient temperature in order to ensure the prevention of illness or issues related to growth in new babies. The concept of temperature monitoring is said to affect the environmental condition of the incubator based on several external factors. The technique of cloud computing through Ubidots software can be used to analyze the variation in temperature of the incubator. This also shows that the alert system can be incorporated for sensing the rise in temperature and indicating the same to hospital users. The alert unit which has been developed and installed in the hospitals are provided with an option to become beneficial for most of the patients.

Keywords: temperature monitoring; alert system; incubators; environmental condition; cloud computing; hospital zones

References:

  1. Radu-Corneliu Marin, Radu-Ioan Ciobanu, Ciprian Dobre, "Improving Opportunistic Networks by Leveraging Device-to-Device Communication", Communications Magazine IEEE, vol. 55, no. 11, pp. 86-91, 2017.
  2. R. C. Trusca, S. Albert and M. L. Soran, "The benefits of data center temperature monitoring," 2015 Conference Grid, Cloud & High Performance Computing in Science (ROLCG), Cluj-Napoca, 2015, pp. 1-3.
  3. Daniel Rosner, Razvan Tataroiu, Laura Gheorghe, Razvan Tilimpea, "UNCHAIN - Ubiquitous Wireless Network Communication Architecture for Ambient Intelligence and Health Scenarios", Secure Internet of Things (SIoT) 2014 International Workshop on, pp. 44-51, 2014.
  4. Evangelos K. Markakis, Asimakis Lykourgiotis, Ilias Politis, Anastasios Dagiuklas, Yacine Rebahi, Evangelos Pallis, "EMYNOS: Next Generation Emergency Communication", Communications Magazine IEEE, vol. 55, no. 1, pp. 139-145, 2017.
  5. Cesar Medeiros Davi, D. Silva Silveira and F. Buarque de Lima Neto, "A Framework Using Computational Intelligence Techniques for Decision Support Systems in Medicine," in IEEE Latin America Transactions, vol. 12, no. 2, pp. 205-211, March 2014.
  6. Ubidots : IoT platform - Internet of Things, http://help.ubidots.com/iot-projects-tutorials
  7. Lisha Yu, Wai Man Chan, Yang Zhao, Kwok-Leung Tsui, "Personalized Health Monitoring System of Elderly Wellness at the Community Level in Hong Kong", Access IEEE, vol. 6, pp. 35558-35567, 2018.

420-422

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99.

Authors:

A. Parvathy Karthika, V. Gayathri

Paper Title:

Experimental Studies on Durability Aspects of High Strength Concrete using Flyash and Alccofine

Abstract: The experimental work is to study the influence of FLY ASH and ALCCOFINE 1203 in achieving High Strength Concrete (HSC). This study analyses the performance of various combinations of concrete in which cement is partially replaced with 30% fly ash with different proportions of alccofine of grade 1203, micro fine silica includes 0%, 4%, 8%, 12% respectively. Super plasticizer Conplast SP430 of 1.5% for every specimen is added in order to improve the workability of the mix. Specimens are casted for M 60 grade as per mix design using manufactured sand (M sand) as fine aggregate. Durability tests conducted includes rapid chloride penetration test, water absorption test, carbonation test and water permeability test. Scanning electron microscopic analysis was carried out to determine the development of micro structural configuration of the concrete.

Keywords: High Strength Concrete, Rapid Chloride Penetration Test, Water Absorption Test, Carbonation Test, Water Permeability Test, and Scanning Electron Microscopy.

References:

  1. Alok Kumar, Oshin Parihar, Rahul Chaudhary, ShivPrakash Singh, (2016) “Use of Alccofine 1206 to achieve high strength durable concrete” SSRG-International Journal of Civil Engineering, vol. 3 Issue 5
  2. IS 3812(PART I):2013 " Specification for Pulverized Fuel Ash, Part 1: For Use as Pozzolana in Cement, Cement Mortar and Concrete"
  3. IS: 516 – 1959" Method of Tests for Strength of Concrete"
  4. IS40311988" Methods of physical tests for hydraulic cement"
  5. IS 2386(PART 3)" Methods of test for aggregates for concrete"
  6. IS 383: 1970 "Specification for coarse and fine aggregate from concrete for natural resources" S. Shetty "concrete technology".
  7. Saurabh Gupta, Sanjay Sharma, Devinder Sharma (2015) “A Review on Alccofine : A supplementary cementitous material” International Journal of ModernTrends in Engineering and Research vol. 2 Number 8.
  8. Swamy, (1999) “Role of Slag in the development of Durable and Sustainable High Strength Concretes" proceedings of International Symposium on concrete technology for sustainable development in the 21s Century, Hyderabad, pp. 186-121.
  9. Yatin Patel, Shah B. K., Patel P. J, (2013) “Effect of Alccofine 1203 and Fly Ash Addition on the Durability of High Performance Concrete” International Journal of Scientific Research and Development, Vol. 1, Issue 3, ISSN (online) 2321-0613.

423-427

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100.

Authors:

C. Maheswari, E.B.Priyanka, S.Thangavel, P. Parameswari

Paper Title:

Development of Unmanned Guided Vehicle for Material Handling Automation for Industry 4.0

Abstract: In the current scenario, the industries are implementing automation in every field works. Industries are trying to reduce the labor cost and processing time which is taken by the human. This proposed work will be helpful for handling the materials efficiently. That is, for picking the raw materials from the storehouse and carrying it on the vehicle and transporting to the workshop where it is being machined. To replace the labor cost and to reduce the processing time, this type of vehicle can be used. The UGV picks the raw materials that had to be machined from the storehouse to the workshop where the machining processes are done. ARDUINO UNO R3 ATMEGA 328P controller is the heart of the system which controls the vehicle movement and arm actuation. The robotic arm consists of 5 servo motors with the gripper at its end. The vehicle with three wheels is helpful for carrying the materials from one place to another place. Three IR sensors are used for detecting the black line on which the vehicle has to move. Sensor 1 and 2 are used for sensing the black line. Whenever it is sensed the vehicle has to move forward or left or right. The third sensor indicates the storehouse or the workshop. When the robot reaches the storehouse, which is indicated by the third IR sensor, the vehicle stops and ARDUINO UNO R3 actuates the arm and simulates it to carry the goods. The vehicle automatically starts and carries it to the workshop without any human interruptions. Hence, the proposed project is useful for carrying and transporting the raw materials and finished goods efficiently with the less consumption of time. This research includes the robotic arm with a gripper, an Autonomous vehicle with three wheels, ARDUINO UNO R3 controller, Motor driver, three InfraRed sensors (IR) and Single-Mode Power Supply (SMPS).

Keywords: Unmanned guided vehicle, Robotic arm, ARDUINO UNO R3 ATMEGA 328P, Material Handling+

References:

  1. F.A. Vis. "Survey of research in the design and control of automated guided vehicle systems," European Journal of Operational Research, 2006, vol. 170, pp. 677–709.
  2. Ganesharajah, N.G. Hall,and C. Sriskandarajah. “Design and analysis of operational issues in AGV-served manufacturing systems,” Annals of Operation Research, 1998,vol.76, pp.109-154.
  3. Kelly, B. Nagy, D. Stager,and R. Unnikrishnan. “An infrastructure-free automated guided vehicle based on computer vision,” IEEE Roboticsand Automation Magazine, 2007, vol. 14, pp. 24-34.
  4. Chen C, Wang B and Ye Q T. “Application of automated guided vehicle (AGV) based on inductive guidance for the newsprint rolls transportation system,” J. of Dong Hua Univ. (Engl. Ed.), 2004, Vol. 21, pp. 88-92.
  5. Tomoya, O. Jun, A. Tamio et al."Semi-guided navigation of AGV system through iterative learning techniques" IEEE international conference on intelligent robotics system.,2001,vol.2, pp.968-973.
  6. G Beccari, S Caselli, F Zanichelli and et al. “Vision-based line tracking and navigation in structured environments,” IEEE Int. Symposium on computational intelligence in robotics and automation, 1997, pp. 406-411.
  7. Maheswari, E.B.Priyanka, B.Meenakshipriya, Fractional order based PID controller tuned by coeffiecient diagram method and PSO algorithms for SO2 emission control process, Journal of system and control engineering,2017, vol.231,pp.587-589.
  8. Priyanka E.B, C.Maheswari, Parameter monitoring and control during petrol transportation using PLC based PID controller, Journal of Applied Research and Technology, 14 (5)(2016) 125-131.
  9. Priyanka E.B, C. Maheswari, S. Thangavel, Remote monitoring and control of an oil pipeline transportation system using a Fuzzy-PID controller, Flow Measurement and Instrumentation, (2018) Article in press. https://doi.org/10.1016/j.flowmeasinst.2018.02.010.
  10. Parameswari, Dr.M.Manikantan 2017,’Geo-Intelligence System: A Frame work for agricultural improvements’, International Journal of Pure and Applied Mathemetics, vol 116,no 12,pp 117-125.
  11. Parameswari, P, Abdul Samath, J &Saranya, S 2015, ‘Efficient birchclustering algorithm for categorical and numerical data using modified co-occurrence method’, International Journal of Applied Engineering Research, vol. 10, no. 11, pp. 27661-27673.
  12. Parameswari, P, Abdul Samath, J &Saranya, S 2015, ‘Scalable Clustering Using Rank Based Preprocessing Technique for Mixed Data Sets Using Enhanced Rock Algorithm’, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 5, pp. 1327-1334.
  13. Wu X, Lou P H and Tang D B. “Integrated motion control of path tracking and servo control for an automated guided vehicle,” Chinese J. of Mech. Eng., 2011, vol. 47, pp. 43-48.
  14. Deyle, N. Hai, M. S. Reynold et al. “ RFID based guided robots for pervasive automation” IEEE Pervasive Computer, 2010, vol.9, pp.37-45.

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101.

Authors:

Kiruthikaa K V, Vijay Franklin J, Yuvaraj S

Paper Title:

Analysis of Prediction Accuracy of Heart Diseases using Supervised Machine Learning Techniques for Developing Clinical Decision Support Systems

Abstract: Heart diseases are taking on hands as the vital mortality deciding factor in the current era. Most of the people around the world are experiencing a time-scheduled and stressful work life, which often leads to increase in the percentage of healthy people affected by heart diseases. It is mandatory to solve this raising issue by predicting the occurrence of the disease as earlier as possible with the help of variety of available solutions. Machine learning techniques can be applied to analyze and predict whether a person is likely to have heart disease or not. In this paper, we made a detailed investigation on prediction accuracy rate of heart diseases using different supervised machine learning techniques, which will pave the way for researchers to choose the efficient technique(s) in order to design and develop clinical decision support systems that predicts the occurrence of heart diseases in people efficiently.

Keywords: Heart Disease, Machine Learning, Prediction Accuracy, Clinical Decision Support Systems

References:

  1. S. Brisimi, T. Xu, T. Wang, W. Dai, W. G. Adams and I. C. Paschalidis,