Volume-10 Issue-3, September 2021 – International Journal of Recent Technology and Engineering (IJRTE)

C4077098319

IJRTE Journal — Tue, 05 Jul 2022 10:41:52 +0000

A Swarm Intelligence Based Weighted Feature Extraction and Classification using SVM for Sentimental Exploration
P. V. Naga Srinivas¹, M. V. P. Chandra Sekhara Rao²
¹P. V. Naga Srinivas, Research scholar- CSE Department, University College of Engineering & Technology, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India.
²M. V. P. Chandra Sekhara Rao, Professor, CSE Department, RVR & JC College of Engineering, Guntur, Andhra Pradesh, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 883-890 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4077098319/19©BEIESP | DOI: 10.35940/ijrte.C4077.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The goal of Sentiment Exploration (SE) is used for mining the accurate sentiments which are very beneficial for businesses, governments, and individuals, the opinions, recommendations, ratings, and feedbacks are becoming an important aspect in present scenarios. The proposed methodology likewise attempts to introduce a swarm intelligence based sentimental supervised methodology. In order to obtain a relevant feature data set from a large number of data samples, this method used particle swarm optimization to attain the utmost optimum feature set. The evaluation of the optimum feature set is obtained by means of using Minimum Redundancy and Maximum Relevancy measure as the fitness function. The categorization of the extracted feature set is accomplished with the Support Vector Machine classification technique. The experimental outcome for the suggested method is evaluated using four performance measure like precision, recall, accuracy, and f-measure and showed that proposed swarm intelligent based classification method has better performance using IMDB, Movie Lens and Trip Advisor Data Samples.
Keywords: Sentimental Exploration, Particle Swarm Optimization, Minimum Redundancy Maximum Relevancy, Support Vector Machine, Classification, Feature Selection.
Scope of the Article: Classification

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C63460910321

IJRTE Journal — Fri, 01 Oct 2021 11:08:42 +0000

The Discrete Rational Cubic Spline Interpolator
Y.P. Dubey¹, Jyoti Tiwari²
¹Dr. Yadvendra Prasad Dubey*, HOD and Professor, Govt Kundam College Jabalpur (M.P) India.
²Smt. Jyoti Tiwari, Assistant Professor, Department of Mathematics, Shri Ram Institute of Science and Technology, Jabalpur (M.P) India.
Manuscript received on August 31, 2021. | Revised Manuscript received on September 23, 2021. | Manuscript published on September 30, 2021. | PP: 236-239 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C63460910321 | DOI: 10.35940/ijrte.C6346.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The problem of Discrete C2 Rational Cubic Spline has been proposed and Error bound obtained. The Discrete Rational Method have unique representation.
Keywords: Rational, Discrete, Cubic, Shape Preserving, Error bounds, Interpolation Parameter.

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C64820910321

IJRTE Journal — Tue, 28 Sep 2021 10:06:33 +0000

A Relevant SNC Application for Data Computation using Python Programming
K Pooja¹, Shailaja. S²
¹K Pooja*, Department of Computer Science and Engineering, Poojya Dodappa Appa College of Engineering, Kalaburagi (Karnataka), India.
²Dr Shailaja S, Assistant Professor, Department of Computer Science and Engineering Poojya Dodappa Appa College, Kalaburagi (Karnataka), India.
Manuscript received on September 19, 2021. | Revised Manuscript received on September 25, 2021. | Manuscript published on September 30, 2021. | PP: 231-235 | Volume-10, Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64820910321 | DOI: 10.35940/ijrte.C6482.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Multiple applications of cloud servicing can be seen in the field of logical programming as well as IT industries. Complex computations over local machines may demand for plenty of system resources thereby delaying the data processing operations. In order to achieve speed in processing one must opt for cloud computing techniques. Extensive maneuver of cloud services is desirable for scientific computation of user data and application. This will require a platform designed in a way to meet the specific requirements of individual users, providing an ease for moving their data and applications over different devices. Symbolic-Numeric Computation using cloud service platform is presented in the paper. In this approach user tasks are presented in the form of symbolic expressions using languages like Java, C/C++, APIs etc. Proposed work employs Python programming for carrying out compilation process.
Keywords: Python, Cloud computing, SNC, Tensor Flow, SVM.

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C64770910321

IJRTE Journal — Tue, 28 Sep 2021 07:43:08 +0000

Geospatial Location Based Study for Establishment of New Public Health Centers: A Case of Adama City, Oromia, Ethiopia
Temesgen Abraham Gebreselassie¹, P. Jagadeeswara Rao²
¹Temesgen Abraham Gebreselassie*, Research Scholar, Department of Geo-Engineering, College of Engineering (A), Andhra University, Visakhapatnam (Andhra Pradesh), India.
²Prof. P. Jagadeeswara Rao, Professor, Department of Geo-Engineering, College of Engineering (A), Andhra University, Visakhapatnam (Andhra Pradesh), India.
Manuscript received on September 17, 2021. | Revised Manuscript received on September 25, 2021. | Manuscript published on September 30, 2021. | PP: 221-230 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64770910321 | DOI: 10.35940/ijrte.C6477.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This study focuses on establishing adequate public healthcare centers to reduce disease spread and untimely death of human lives in Adama City, Ethiopia. There is considerable evidence, owing to poor geographical accessibility, the primary healthcare facility is not reaching the majority of the population in developing nations. This case study has been carried out to identify and prioritize the suitable areas to develop healthcare centers using a Geographic Information System (GIS). Thematic maps and ancillary data are used to analyze location-allocation analysis in ArcGIS-10.4. Required field data was collected using GPS in Adama, Oromia, Ethiopia, and required supporting data from the Municipality of the City. The density of population is the significant input in calculating the standards for services-based location-allocation. Therefore, road networks, existing healthcare facilities, and population density are the critical parameters considered to identify the new healthcare centers to be established to cater to the people’s needs. In addition, land use/cover classes and settlement location, proximity to the existing healthcare centers are also considered in GIS analysis for better results. The study identified eight suitable sites to develop healthcare centers in the city. Therefore, the government can utilize the recommendations for infrastructure development planning to improve healthcare facilities based on the accessibility.
Keywords: Accessibility, Spatial Distribution, GIS, Health Centers

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C64840910321

IJRTE Journal — Tue, 28 Sep 2021 07:05:54 +0000

Estimation and Analysis of Rainfall Runoff for Urban Hydrology using TR 55 SCS CN and GIS Approach in Hebbal Valley of Bengaluru, South India
Ganesh V¹, Ajey Kumar V G², Aravindan S³, Sudha Ravindranath⁴, Vidya A⁵
¹Ganesh V*, Department of Civil Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
²Ajey Kumar V G, Department of Civil Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
³Aravindan S, Department of Civil Engineering, Sri Venkateshwara College of Engineering, Bengaluru (Karnataka), India.
⁴Sudha Ravindranath, Senior Scientist, Regional Remote Sensing Centre-South, Bengaluru National Remote Sensing Centre, Hyderabad (Telangana), India.
⁵Vidya A, Scientist Engineer ‘SE’, Regional Remote Sensing Centre-South, Bengaluru National Remote Sensing Centre, Hyderabad (Telangana), India.
Manuscript received on September 20, 2021. | Revised Manuscript received on September 25, 2021. | Manuscript published on September 30, 2021. | PP: 215-220 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64840910321 | DOI: 10.35940/ijrte.C6484.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Urban floods are increasing frequently and severely. Climate change is usually attributed to urban floods with insufficient evidence. While in certain cases this appears to be true, the influence of landscape change in urban growth is more important. This study analyses development of an urban landscape with the complexity of established cities and combines physiographic data for the assessment of peak surface runoff in the study area, Hebbal valley. A portion of the Cauvery river basin draining into the Pinakini river in the district of Bangalore. It encompasses a 305.21 sq.km region in East Bengaluru and North Bengaluru. The land use and land cover classification was classified as 14 different categories: dark, light, roads and vegetation. The region of study has undergone unpredictable expansion and changes in the Land Use Land Cover in the last two decades. Several flood occurrences have occurred in different regions of Hebbal Valley throughout recent years. Rainfall analysis conducted between 1970 and 2018 with 1596mm of greatest precipitation. For the study, several space and non-space data were collected and thematic maps were produced. Runoff estimates for 2018 were made for 24 micro water sheds in the Hebbal Valley using SCS-CN TR55 technique for urban hydrology. The objective of this study is to determine the quantity of peak runoff produced, to develop better urban management techniques. The finding shows that rush volume has increased in recent years as land use patterns have changed and precipitation intensity has increased substantially over shorter periods. The study suggests spatial intervention efforts to provide suitable buildings and measures for flood flow.
Keywords: Curve Number, GIS, Hydrologic Soil Group (HSG), SCS-CN, Runoff Estimation.

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C63860910321

IJRTE Journal — Tue, 28 Sep 2021 06:44:21 +0000

Data Augmentation using Auxiliary Classifier for Improved Detection of Covid 19
Lakshmisetty Ruthvik Raj¹, Bitra Harsha Vardhan², Mullapudi Raghu Vamsi³, Keerthikeshwar Reddy Mamilla⁴, Poorna Chandra Vemula⁵
¹Lakshmisetty Ruthvik Raj, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
²Bitra Harsha Vardhan, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
³Mullapudi Raghu Vamsi, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
⁴Keerthikeshwar Reddy Mamilla, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
⁵Poorna Chandra Vemula*, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on August 17, 2021. | Revised Manuscript received on September 26, 2021. | Manuscript published on September 30, 2021. | PP: 209-214 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C63860910321 | DOI: 10.35940/ijrte.C6386.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: COVID-19 is a severe and potentially fatal respiratory infection called coronavirus 2 disease (SARS-Co-2). COVID-19 is easily detectable on an abnormal chest x-ray. Numerous extensive studies have been conducted due to the findings, demonstrating how precise the detection of coronas using X-rays within the chest is. To train a deep learning network, such as a convolutional neural network, a large amount of data is required. Due to the recent end of the pandemic, it is difficult to collect many Covid x-ray images in a short period. The purpose of this study is to demonstrate how X-ray imaging (CXR) is created using the Covid CNN model-based convolutional network. Additionally, we demonstrate that the performance of CNNs and various COVID-19 acquisition algorithms can be used to generate synthetic images from data extensions. Alone, with CNN distribution, an accuracy of 85 percent was achieved. The accuracy has been increased to 95% by adding artificial images generated from data. We anticipate that this approach will expedite the discovery of COVID-19 and result in radiological solid programs. We leverage transfer learning in this paper to reduce time complexity and achieve the highest accuracy.
Keywords: CXR, Convolutional Neural Networks, VGGNET, RESNET.

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C63870910321

IJRTE Journal — Sat, 25 Sep 2021 09:30:54 +0000

Development of Foot Step Power Generation using Piezo Electric Sensors
K. Varalaxmy Nithya¹, V.Iswariya², K.Harini³, K.Hemalatha⁴
¹K.Varalaxmy Nithya*, BE, ECE Department, R.M.K College of Engineering & Technology, Chennai, India.
²V.Iswariya, BE, ECE Department, R.M.K College of Engineering & Technology, Chennai, India.
³K.Harini, BE, ECE Department, R.M.K College of Engineering & Technology, Chennai, India.
⁴K.Hemalatha, BE, ECE Department, R.M.K College of Engineering & Technology, Chennai, India.
Manuscript received on August 18, 2021. | Revised Manuscript received on September 24, 2021. | Manuscript published on September 30, 2021. | PP: 206-208 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C63870910321 | DOI: 10.35940/ijrte.C6387.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This project was developed to need for a system that generates non conventional energy. Energy crisis is one of the major issues worldwide. This paper proposes maximum utilization of human locomotion energy for power generation using Piezo electric sensors. The basic principle was, any vibration or pressure that generate between surface and footsteps while walking, is converted into electric energy using Piezo electric transducers. A series connection of Piezo electric transducers are mounted on the tile and this tile is placed in the crowded area for the generation of electric energy. This paper explores the power generated by footsteps and used for mobile charging.
Keywords: Footstep Power generation, Piezo electric Sensors, Transducer, Voltage regulator, Rechargeable battery.

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C64740910321

IJRTE Journal — Fri, 24 Sep 2021 11:51:22 +0000

SBC-Based Object and Text Recognition Wearable System using Convolutional Neural Network with Deep Learning Algorithm
Melchiezedhieck J. Bongao¹, Arvin F. Almadin², Christian L. Falla³, Juan Carlo F. Greganda⁴, Steven Valentino E. Arellano⁵, Phillip Amir M. Esguerra⁶
¹Melchiezhedhieck J. Bongao*, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna Philippines.
²Arvin F. Almadin, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
³Christian L. Falla, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
⁴Juan Carlo F. Greganda, Computer Engineering Department, College of Engineering, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
⁵Steven Valentino E. Arellano, Computer Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
⁶Phillip Amir M. Esguerra, Electronics Engineering Department, College of Engineering and Graduate School, University of Perpetual Help System Laguna, City of Biñan, Laguna, 4024 Philippines.
Manuscript received on September 17, 2021. | Revised Manuscript received on September 24, 2021. | Manuscript published on September 30, 2021. | PP: 198-205 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64740910321 | DOI: 10.35940/ijrte.C6474.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This Raspberry Single-Board Computer-Based Object and Text Real-time Recognition Wearable Device using Convolutional Neural Network through Tensor Flow Deep Learning, Python and C++ programming languages, and SQLite database application, which detect stationary objects, road signs and Philippine (PHP) money bills, and recognized texts through camera and translate it to audible outputs such as English and Filipino languages. Moreover, the system has a battery notification status using an Arduino microcontroller unit. It also has a switch for object detection mode, text recognition mode, and battery status report mode. This could fulfill the incapability of visually impaired in identifying of objects and the lack of reading ability as well as reducing the assistance that visually impaired needs. Descriptive quantitative research, Waterfall System Development Life Cycle and Evolutionary Prototyping Models were used as the methodologies of this study. Visually impaired persons and the Persons with Disability Affairs Office of the City Government of Biñan, Laguna, Philippines served as the main respondents of the survey conducted. Obtained results stipulated that the object detection, text recognition, and its attributes were accurate and reliable, which gives a significant distinction from the current system to detect objects and recognize printed texts for the visually impaired people.
Keywords: Visually Impaired, Real-time Object and Text Recognition, Convolutional Neural Network, Deep Learning

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C64780910321

IJRTE Journal — Fri, 24 Sep 2021 11:24:06 +0000

Study of Deep Learning Methods for Fingerprint Recognition
Mamadou Diarra¹, Ayikpa Kacoutchy Jean², Ballo Abou Bakary³, Kouassi Brou Médard⁴
¹Mamadou Diarra*, Assistant professor, Department of mathematics and computer sciences, University of Félix Houphouet
²Ayikpa Kacoutchy Jean, Teacher and Researcher,Department of Computer science and digital sciences, Virtual University
³Ballo Abou Bakary, Teacher and Researcher, Department of agropastoral management.
⁴Kouassi Brou Medard , IT Manager, University of Félix Houphouet
Manuscript received on September 17, 2021. | Revised Manuscript received on September 24, 2021. | Manuscript published on September 30, 2021. | PP: 192-197 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64780910321 | DOI: 10.35940/ijrte.C6478.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Biometric systems aim to reliably identify and authenticate an individual using physiological or behavioral characteristics. Traditional systems such as the use of access cards, passwords have shown limitations such as forgotten passwords, stolen cards, etc. As an alternative, biometric systems present themselves as efficient systems with a high reliability due to the physiological characteristics of each individual. This paper focuses on a deep learning method for fingerprint recognition. The described architecture uses a pre-processing phase in which grayscale images are represented on the RGB bands and then merged to obtain color images. On the obtained color images will be extracted the characteristics of the fingerprints textures. The fingerprint images after preprocessing are used in a deep convolution network system for decision making. The method is robust with an accuracy of over 99.43% and 99.53% with the respective variants densenet-201 and ResNet-50.
Keywords: Deep learning; fingerprint authentification; Biometrics system; CNN; DenseNet-201; ResNet-50.

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C64760910321

IJRTE Journal — Fri, 24 Sep 2021 11:12:48 +0000

Shoreline Change Detection from Cuddalore to Nagapattinam Coast, Tamil Nadu
Pradeep J¹, Aparna S. Bhaskar²
¹Pradeep J*, Student, Masters, Remote Sensing and GIS in SRM Institute of Science and Technology.
²Dr. Aparna S., Bhaskar (Ph.D) Associate Professor, Department of Civil Engineering, Kattankulathur Campus, SRM Institute of Science and Technology.
Manuscript received on September 17, 2021. | Revised Manuscript received on September 22, 2021. | Manuscript published on September 30, 2021. | PP: 188-191 | Volume-10 Issue-3, September 2021. | Retrieval Number: 100.1/ijrte.C64760910321 | DOI: 10.35940/ijrte.C6476.0910321
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Erosion of shoreline areas is one of the major sources of problems on the world, whose impact has had a devastating effect on coastal communities. The coastal region of India is undergoing various natural and Anthro -pogenic process activities which controls the accretion , erosion rates along the coastal area. Existing research suggests that the merged use of satellite imagery and mathematical techniques are the appropriate technique for coastal survey analysis. The study was conducted in the Cuddalore to Nagapattinam coastal area till kodiyakarai (Tamil Nadu), India ,Using satellite -data imagery from 2003 to 2017. This coastal stretch has been report high rate of erosion as well as Accretion. Coastal side erosion and long-term erosion, accretion rates were calculated using DSAS, to measure the rate of change of the shoreline. Analysis results shows erosion is rampant in Mayiladuthurai district, Tharangambadi – Karaikal (Puducherry Union Territory) especially from Tharangambadi beach to nagore beach.
Keywords: DSAS (Digital Shoreline Analysis System), Erosion, Accretion, Shoreline.

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