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Volume-5 Issue-4

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S. No

Volume-5 Issue-4, September 2016, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Th. Rupachandra Singh, Irengbam Tilokchan Singh, Tejmani Sinam

Paper Title:

Analysis of Skype and its Detection

Abstract:  This paper gives a complete analysis of Skype Traffic. Based on the analysis of Skype Traffic, we proposed a heuristic based detection method which classified the Skype Signaling and Skype Media Traffic. We properly categorized the Skype Media traffic as audio or video conversation. In this paper, we also propose a novel approach to identify VoIP Network Traffic in the first few seconds of initial state of communication. The proposed classifier works with Machine Learning Techniques based on the statistical features. The experimental results show that the proposed method can achieve over 99% accuracy for all testing dataset.

 Skype; Network Traffic Analysis; Traffic Classification; Machine Learning


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2.       “l7-filter application layer packet classifier for linux,” 2009, http:  //l7filter.sourceforge.net.

3.       T. Sinam, I. T. Singh, P. Lamabam, and N. N. Devi, “An efficient technique for detecting skype flows in udp media streams,” in Advanced Networks and Telecommuncations Systems (ANTS), 2013 IEEE International Conference, Dec 2013, pp. 1–6.

4.       T. Sinam, I. T. Singh, P. Lamabam, N. N. Devi, and S. Nandi, “A technique for classification of voip flows in udp media streams using voip signalling traffic,” in Advance Computing Conference (IACC), 2014 IEEE International, Feb 2014, pp. 354–359.

5.       T. Sinam, N. N. Devi, P. Lamabam, I. T. Singh and S. Nandi, “Early Detection of VoIP Network Flows based on Sub-Flow Statistical Characteristics of Flows using Machine Learning Techniques,” in Advanced Networks and Telecommuncations Systems (ANTS), 2014 IEEE International Conference, Dec 2014.

6.       L. Grimaudo, M. Mellia, E. Baralis, and R. Keralapura, “Select: Self- learning classifier for internet traffic,” IEEE Transactions on Network and Service Management, vol. 11, no. 2, pp. 144–157, 2014.

7.       T. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” Commun. Surveys Tuts., vol. 10, no. 4, pp. 56–76, Oct. 2008.

8.       J. Chandrakant and D. Lokhande Shashikant, “Analysis of early traffic processing and comparison of machine learning algorithms for real time internet traffic identification using statistical approach,” in Advanced Computing, Networking and Informatics- Volume 2, ser. Smart Innovation, Systems and Technologies, M. Kumar
Kundu, D. P. Mohapatra, A. Konar, and A. Chakraborty, Eds. Springer International Publishing, 2014, vol. 28, pp. 577–587.

9.       R. Yan and R. Liu, “Principal component analysis based network traffic classification,” JCP, vol. 9, no. 5, pp. 1234–1240, 2014.

10.    J. M. Reddy and C. Hota, “P2p traffic classification using ensemble learning,” in Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop, ser. I
CARE ’13. New York, NY, USA: ACM, 2013, pp. 14:1–14:4.

11.    M. Korczynski and A. Duda, “Markov chain fingerprinting to classify encrypted traffic,” in IEEE Conference on Computer Communikations, INFOCOM , Toronto, Canada, April 27 - May 2, 2014. IEEE, 2014, pp. 781–789.

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17.    P. Haffner, S. Sen, O. Spatscheck, and D. Wang, “Acas: Automated construction of application signatures,” in Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, ser. MineNet ’05. New York, NY, USA: ACM, 2005, pp. 197–202.

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K.A.M. Sajad Hyder, M. Vanitha

Paper Title:

Segmentation of Liver and Retrieval Procedure by Feature Extraction for CT-Scan Abdominal Image Processing

Abstract:  Liver is an important vital organ. In these days medical professionals utilize CT scan abdominal images for the diagnosis of liver disorders. There arise the problem of liver segmentation and image processing for recapturing the matching image of predetermined image of liver ill state. In this present work a new way of methodology has been introduced for the pulling out liver image in a large dataset of abdominal scanned images. Further the segmented liver images are preprocessed for the feature extractions of Shape, Intensity and Texture. An automatic system of Least Distance Method (LDM) is used for the recalling of image is run into the system. There is a significant speed and accuracy have been notified by this LDM. The above results are discussed with earlier related works and concluded with the application in clinical practice.

Automatic retrieval technique, Digital image processing, Liver Segmentation, Least Distance method for recapturing matching image.


1.    R. Punia and S. Singh, “Review on Machine Learning Techniques for Automatic Segmentation of Liver Images,” International Journal of Advanced Research in
Computer Science and Software Engineering, Vol. 3, No. 4, 2013, pp. 666-670

2.    M. Erdt, et al., “Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation,” Computer-Based Medical Systems, 2010, pp. 249-254

3.    M. Sammouda, et al., “Tissue Color Images Segmenta-tion Using Artificial Neural Networks,” Biomedical Im-aging: Nano to Macro, 2004

4.    G.G. Rajput and Anand M.Chavan (2016) “Atomic Detection of Abnormalities Associated with Abdomen and Liver Images : A survey on Segmentation methods”, International Journal of Computer Applications, Volume 140-No.4, pp. 1 to 8

5.    Lav R.Varshney (2002), “Abdominal Organ Segmentation in CT-Scan Images : A Survey”, International Journal of Information Technology, Volume 100. pp. 200 to 215

6.    Luo et. al., (2014), “Review on the methods of Automatic Liver Segmentation from Abdominal Images” Journal of Computer and Communications, 2, pp. 1-7

7.    X. Zhang, et al., “Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection,” IEEE Transaction on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2611-2626

8.    M. Erdt, et al., “Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation,” Computer-Based Medical Systems, 2010, pp.

9.    H. Badakhshannoory and P. Saeedi, “A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes,” IEEE Transaction on Biomedical Engineering, 2009, pp. 2681-2693






Sourav Sarkar, J. Shah, R. K. Kotnala, M. C. Bhatnagar

Paper Title:

Effect of Zn and Mn Substitution on Structural, Dielectric, Magnetic and Optical Properties of Multiferroic CoFe2O4-BaTiO3 Core-Shell Type Composites

Abstract: In this paper, we have reported the synthesis of Zn and Mn substituted cobalt ferrite by chemical co-precipitation method and used it as core material in barium titanate sol to finally prepare core-shell type composite material. Amount of ferrite was varied in the final composite samples from 30% to 50%. X-ray diffraction show prominent spinel and perovskite peaks corresponding to ferrite and titanate phases respectively. HRTEM micrographs reveal core-shell type nature with presence of a well-defined interface. Our proposed substitutions increase the resistivity of pure cobalt ferrite by one order which has been verified through I-V measurement. SEM micrographs show dense microstructure and particle formation of both phases in the composites. Substitution of Zn at the site of Co is supported by the peak shift in Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy. Maxwell Wagner relaxation phenomena at the interface and hopping conduction in ferrites explain both frequency and temperature variation of dielectric parameters. Substitution of Zn and Mn result in super-paramagnetic type behavior with coercively  few Oe and very negligible remnant magnetization (MR). Photoluminescence (PL) spectra show slight decrease in energy band gap of ferrite as a result of these substitutions.

 Sol-gel process (A); Composites (B); Dielectric properties (C); Optical properties (C)


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Naina Lohana, M. Mani Roja

Paper Title:

A Review of the Internet of Things

Abstract:  In this paper an effort is taken to review the concept of the Internet of Things (IoT). It has gained popularity in the recent years due to its wire-ranging applications. As the world moves towards a future with more and more devices linked to the Internet, this paper looks at the elements of IoT, its communication models, the challenges it faces and its applications.



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3.    K. L. Lueth, IoT Basics: Getting Started with the Internet of Things, IoT Analytics(2015). Retrieved from https://iot-analytics.com/product/whitepaper-iot-basics-getting-started-with-the-internet-of-things/

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5.    D. Hamilton The Four Internet of Things Connectivity Models Explained. Retrieved from http://www.thewhir.com/web-hosting-news/the-four-internet-of-things-connectivity-models-explained

6.    T.T. Mulani, S.V. Pingle  Internet of Things, IRJMS Vol 2  Special Issue 1, March 2016.

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Ahmed H.Almutairi

Paper Title:

Laser Diode and Applications

Abstract: This paper shows how to use the P-N Junction to generate the Laser (Laser Diode) and how we use this laser Diode in many applications.

 Introduction, P-N Junction, Biased p-n Junction, Laser diodes, Turning semiconductor amplifiers into laser diodes, Applications of Laser Diodes, Conclusion and References.


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