422
20
11858
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Dublin Core
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Title
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Book Chapter
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Faculty Publications- Book Chapter
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Performance Analysis of Logistic Regression, KNN, SVM, Nae Bayes Classifier for Healthcare Application During COVID-19
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Coronavirus; COVID-19; Heart disease; Nae Bayes; Pandemic; SVM
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Heart disease is one of the main causes of mortality in India and the USA. According to statistics, a person dies out of a heart-related disease every 36s. COVID-19 has introduced several problems that have intensified the issue, resulting in increased deaths associated to heart disease and diabetes. The entire world is searching for new technology to address thesechallenges. Artificial intelligence [AI] and machine learning [ML] are considered as the technologies, which are capable of implementing a remarkable change in the lives of common people. Health care is the domain, which is expected to get the desirable benefit to implement a positive change in the lives of common people and the society at large. Previous pandemics have given enough evidence for the utilization of AI-ML algorithm as an effective tool to fight against and control the pandemic. The present epidemic, which is caused by Sars-Cov-2, has created several challenges that necessitate the rapid use of cutting-edge technology and healthcare domain expertise in order to save lives. AI-ML is used for various tasks during pandemic like tracing contacts, managing healthcare-related emergencies, automatic bed allocation, recommending nearby hospitals, recommending vaccine centers nearby, drug-related information sharing, recommending locations by utilizing their mobile location. Prediction techniques are used to save lives as early detections help to save lives. One of the problems that might make a person suffering from COVID-19 extremely sick is heart disease. In this research, four distinct machine learning algorithms are used to try to detect heart disease earlier. Many lives can be saved if heart disease can be predicted earlier. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Goswami M.; Sebastian N.J.
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Lecture Notes on Data Engineering and Communications Technologies, Vol-96, pp. 645-658.
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Springer Science and Business Media Deutschland GmbH
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2022-01-01
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<a href="https://doi.org/10.1007/978-981-16-7167-8_47" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-7167-8_47</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126237166&doi=10.1007%2F978-981-16-7167-8_47&partnerID=40&md5=8fc8c4e7b8775d13d05a5e137574ccef" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126237166&doi=10.1007%2f978-981-16-7167-8_47&partnerID=40&md5=8fc8c4e7b8775d13d05a5e137574ccef</a>
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ISSN: 23674512
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Online
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English
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Book chapter
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Goswami M., CHRIST (Deemed to be University), Bangalore, India; Sebastian N.J., CHRIST (Deemed to be University), Bangalore, India
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Articles
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Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals
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Brain signal; Brain-computer interface; Convolutional neural networks; Hand motion recognition; Machine learning
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Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, Dense-Net201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%. 2022 CRL Publishing. All rights reserved.
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Altameem A.; Sachdev J.S.; Singh V.; Poonia R.C.; Kumar S.; Saudagar A.K.J.
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Computer Systems Science and Engineering, Vol-42, No. 3, pp. 1095-1107.
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Tech Science Press
Date
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2022-01-01
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<a href="https://doi.org/10.32604/csse.2022.023256" target="_blank" rel="noreferrer noopener">https://doi.org/10.32604/csse.2022.023256</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124945126&doi=10.32604%2Fcsse.2022.023256&partnerID=40&md5=3e9a4ac7e2155cc4705f1e8f9b8b6b63" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124945126&doi=10.32604%2fcsse.2022.023256&partnerID=40&md5=3e9a4ac7e2155cc4705f1e8f9b8b6b63</a>
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All Open Access; Hybrid Gold Open Access
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ISSN: 2676192; CODEN: CSSEE
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Online
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English
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Article
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Altameem A., Department of Computer Science, College of Applied Studies, King Saud University, Riyadh, 11495, Saudi Arabia; Sachdev J.S., Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, 303007, India; Singh V., Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, 303007, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
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Conference Papers
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Performance Analysis of Nonlinear Companding Techniques for PAPR Mitigation in 5G GFDM Systems
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Exponential companding; GFDM; High-power amplifier; Mu law companding; PAPR reduction; Root companding
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Generalized Frequency Division Multiplexing (GFDM) is a 5G waveform contender that offers asynchronous and non-orthogonal data transmission, featuring several advantages, some of them being low latency, reduced out-of-band (OOB) radiation and low adjacent channel leakage ratio. GFDM is a non-orthogonal multicarrier waveform which enables data transmission on a time frequency grid. However, like orthogonal frequency division multiplexing and many other multicarrier systems, high peak-to-average power ratio (PAPR) is one of the main problems in GFDM, which degrades the high-power amplifier (HPA) efficiency and distorts the transmitted signal, thereby affecting the bit error rate (BER) performance of the system. Hence, PAPR reduction is essential for improved system performance and enhanced efficiency. Nonlinear companding techniques are known to be one of the effective low complexity PAPR reduction techniques for multicarrier systems. In this paper, a GFDM system is evaluated using mu law companding, root companding and exponential companding techniques for efficient PAPR reduction. The PAPR and BER graphs are used to evaluate the proposed methods in the presence of an HPA. Simulations show that, out of these three techniques, exponential companding was found to provide a trade-off between the PAPR reduction and BER performance. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Gopan N.R.; Sujatha S.
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Lecture Notes in Networks and Systems, Vol-689 LNNS, pp. 39-52.
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Springer Science and Business Media Deutschland GmbH
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-99-2322-9_4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-99-2322-9_4</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169041333&doi=10.1007%2F978-981-99-2322-9_4&partnerID=40&md5=36e9099876460680579ae2943767f159" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169041333&doi=10.1007%2f978-981-99-2322-9_4&partnerID=40&md5=36e9099876460680579ae2943767f159</a>
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ISSN: 23673370; ISBN: 978-981992321-2
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Online
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English
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Conference paper
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Gopan N.R., Department of ECE, Rajagiri School of Engineering and Technology, Kochi, India; Sujatha S., Department of ECE, Christ (Deemed to be University), Bengaluru, India
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Articles
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Performance Analysis of Novel Compact Octagonal Shaped Fractal Antenna for Broadband Wireless Applications
Subject
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Fractal antennas; HFSS; Octagon; Return loss; UWB technology; VSWR; Wireless applications
Description
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Antenna plays an important role in any part of the communication system. It has to be designed very cautiously to provide improved system performance to meet the developments in wireless technologies with various design constraints such as small size, low cost, high data, low power consumption and wideband capabilities. Several efforts have been made by various investigators around the globe to amalgamate benefits of fractal structures with electromagnetic concepts and applications to reduce the size of the antenna without obstructing the performance of the antennas. This paper proposes a novel compact octagonal shaped broadband fractal antenna. The proposed antenna was designed on an inexpensive FR4-epoxy substrate and simulated using the High Frequency Structure Simulator. The antenna resonates in dual bands in 3.8 and 1GHz with lowest return loss of ?32.80dB and gain of 10.22dB while maintaining the VSWR in the 2:1 level. Attempts have been made to reduce the size and improve the bandwidth using fractal concept and truncation of ground plane. The fabricated antenna was verified experimentally and the results are agreeing with the simulations. The point of attraction of this antenna is the use of single patch for broadband coverage with easy fabrication. 2018, Springer Science+Business Media, LLC, part of Springer Nature.
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Vivek R.; Yamuna G.; Suganthi S.; Kosta Y.P.; Jivani M.N.
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Wireless Personal Communications, Vol-103, No. 2, pp. 1325-1340.
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Springer New York LLC
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2018-01-01
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<a href="https://doi.org/10.1007/s11277-018-5511-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11277-018-5511-1</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045149863&doi=10.1007%2Fs11277-018-5511-1&partnerID=40&md5=e1a142fab6c9014aeb1c70d88bba72be" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045149863&doi=10.1007%2fs11277-018-5511-1&partnerID=40&md5=e1a142fab6c9014aeb1c70d88bba72be</a>
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ISSN: 9296212; CODEN: WPCOF
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Online
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English
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Article
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Vivek R., Annamalai University, Chidambaram, India; Yamuna G., Annamalai University, Chidambaram, India; Suganthi S., Christ University, Bengaluru, India; Kosta Y.P., Marwadi University, Rajkot, India; Jivani M.N., Saurastra University, Rajkot, India
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Title
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Conference Papers
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Title
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Performance analysis of OFF-GRID solar photo voltaic system
Subject
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MATLAB/SIMULINK; Modeling; ON-grid systems; Performance analysis
Description
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Day by day the demand for electrical energy is increasing. We can't rely on conventional energy sources for meeting this increasing demand as they are depleting. So it is necessary to find an alternative method to harness the energy that we are lacking. Solar energy generation seems to be a promising technology for this dilemma. It is environmental friendly and infinite source of energy. Photovoltaic systems can be broadly classified into two-an on-grid system or an off-grid system. The energy generated from a solar PV system is based on several factors like irradiance, types of solar PV used and temperature. Analyzing the existing system efficiency is of prime importance for the characterization of the problems and for the improvements. This study deals with the performance analysis of an on-grid and off-grid system. The analysis is carried out by modeling an existing system in MATLAB/SIMULINK which is already in operation. It can be extended to analyze the grid stability. This study aims the quantification of various performance parameters like power output, losses in the system, system efficiency and the total energy transfer. 2015 IEEE.
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Ajan A.; Kumar K.P.
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IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2015
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Institute of Electrical and Electronics Engineers Inc.
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2015-01-01
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<a href="https://doi.org/10.1109/ICCPCT.2015.7159350" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCPCT.2015.7159350</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945917014&doi=10.1109%2FICCPCT.2015.7159350&partnerID=40&md5=dc157bc3b028db2015ad939ae78bcbbc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945917014&doi=10.1109%2fICCPCT.2015.7159350&partnerID=40&md5=dc157bc3b028db2015ad939ae78bcbbc</a>
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ISBN: 978-147997075-9
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Online
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English
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Conference paper
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Ajan A., Electrical and Electronics Engineering, Christ University Faculty of Engineering, Bangalore, India; Kumar K.P., Research and Development Wing, ANERT, Trivandrum, India
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Conference Papers
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Performance analysis of optimized corporate-fed microstrip array for ISM band applications
Subject
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Corporate feed; ISM Band; Microstrip Array; Rectangular patch; Return loss; VSWR
Description
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This paper presents a low cost high gain corporate feed rectangular microstrip patch antenna array of two elements having cuttings at the corners, with detailed steps of design process, operates in Industrial Scientific Medical (ISM) band (2.4 GHz). The proposed antenna structures are designed using FR4 dielectric substrate having permittivity ?r= 44 and substrate thickness of 1.6 mm. The gain of these simulated antennas are obtained as 2.4819 dB with return loss of -17.779 dB for a single element patch and 6.3128 dB with return loss of -15.8320 dB for an array of two elements. The simulations have been carried out by using Antenna simulator HFSS version 15.0.0 to obtain the VSWR, return loss and radiation pattern. 2017 IEEE.
Creator
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Kumar D.S.; Suganthi S.
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Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, Vol-2018-January, pp. 1013-1016.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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2017-01-01
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<a href="https://doi.org/10.1109/WiSPNET.2017.8299915" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WiSPNET.2017.8299915</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046356456&doi=10.1109%2FWiSPNET.2017.8299915&partnerID=40&md5=77f775874a1cda79b62ffac5a4422764" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046356456&doi=10.1109%2fWiSPNET.2017.8299915&partnerID=40&md5=77f775874a1cda79b62ffac5a4422764</a>
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ISBN: 978-150904441-2
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Online
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English
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Conference paper
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Kumar D.S., Dept. of ECE, Christ University, Bangalore, India; Suganthi S., Dept. of ECE, Christ University, Bangalore, India
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Articles
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Performance analysis of semantic veracity enhance (SVE) classifier for fake news detection and demystifying the online user behaviour in social media using sentiment analysis
Subject
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Fake news detection; Online user behaviour; Semantic veracity enhancement (SVE); Sentiment analysis; Twitter
Description
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The increased propagation of fake news is the significant concern in the digital era. Identification of fake news from social media platforms is critical to strengthen public trust and ensure social stability. This research presents an effective and accurate framework for identifying fake news that combines different steps of natural language processing (NLP) technique along with a neural network architecture. A novel semantic veracity enhancement (SVE) classifier is designed and implemented in this work for detecting fake news. The proposed approach leverages the effectiveness of sentiment analysis for identifying misleading or deceptive content and its subsequent implications on the sentiment and behaviour of social media users. A BERT model is used in this research for analysing the sentiments and classifying the texts from the social media platform. By examining the sentiments, the SVE classifier differentiates between real news and fabricated content. To achieve this, three different datasets comprising both actual content and fabricated (tweaked) tweets are employed for training the SVE classifier. The potentiality of the SVE classifier is evaluated and compared with different optimization techniques. The outcome of the experimental analysis shows that the proposed approach exhibits an excellent performance in terms of classifying misinformation from the original information with an outstanding accuracy of 99% compared to other state of art methods. 2024, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
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Sethurajan M.R.; Natarajan K.
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Social Network Analysis and Mining, Vol-14, No. 1
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Springer
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2024-01-01
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<a href="https://doi.org/10.1007/s13278-024-01199-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s13278-024-01199-9</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183650929&doi=10.1007%2Fs13278-024-01199-9&partnerID=40&md5=5e35976b71bdee3e38fa009d165f4131" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183650929&doi=10.1007%2fs13278-024-01199-9&partnerID=40&md5=5e35976b71bdee3e38fa009d165f4131</a>
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ISSN: 18695450
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Online
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English
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Article
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Sethurajan M.R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India; Natarajan K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, 560074, India
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Conference Papers
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Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
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brain tumor; classification; Deep learning; MRI
Description
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Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper. 2024 IEEE.
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Wahlang I.; Pohrmen F.H.
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2024 IEEE 8th International Conference on Signal and Image Processing Applications, ICSIPA 2024
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICSIPA62061.2024.10685883" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSIPA62061.2024.10685883</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206480084&doi=10.1109%2FICSIPA62061.2024.10685883&partnerID=40&md5=bea5a484da2dd58d7f6a394b7b97b344" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206480084&doi=10.1109%2fICSIPA62061.2024.10685883&partnerID=40&md5=bea5a484da2dd58d7f6a394b7b97b344</a>
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ISBN: 979-835035236-8
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English
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Wahlang I., Ghani Khan Choudhury Institute of Engineering and Technology, Department of Computer Science and Engineering, Malda, India; Pohrmen F.H., Christ University, Department of Computer Science, Bangalore, India
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Conference Papers
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Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction
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Artificial Neural Network; Diabetes Mellitus; Levenberg-Marquardt; Multi Layer Perceptrons
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Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training algorithms of Multilayer Perceptrons in Diabetes Prediction. In this study, we have used Pima Indian Diabetes data set from UCI Machine Learning Repository as input dataset. The system is implemented in MatlabR2013. The Pima Indian Diabetes dataset consists of about 768 instances. The input data is the patient history and the target output is the prediction result as tested positive or tested negative. From the performance analysis, it was observed that out of all the training algorithms, Levenberg-Marquardt Algorithm has given optimal training results. 2015 IEEE.
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Saji S.A.; Balachandran K.
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Conference Proceeding - 2015 International Conference on Advances in Computer Engineering and Applications, ICACEA 2015, pp. 201-206.
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Institute of Electrical and Electronics Engineers Inc.
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2015-01-01
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<a href="https://doi.org/10.1109/ICACEA.2015.7164695" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACEA.2015.7164695</a>
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ISBN: 978-146736911-4
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Online
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English
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Saji S.A., Department of Computer Science and Engineering, Christ University, Bangalore, India; Balachandran K., Department of Computer Science and Engineering, Christ University, Bangalore, India
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Conference Papers
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Performance Analysis of User Behavior Pattern Mining Using Web Log Database for User Identification
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Frequent pattern identification; Mutual pre-processing; User access pattern; Web access mining
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User behavior analytics is a progressive research domain. Understanding the users behavior patterns and identifying their behavior patterns will provide solutions to many issues like identity theft and user authentication. So many research works are done in analyzing the frequent access patterns of the users by pre-processing access logs and applying various algorithms to understand the frequent access behavior of the user. From the literature, it founds that the frequent user access pattern identification needs improvement on prediction accuracy and the minimal false positives. To accomplish these, three different approaches were proposed to overcome the existing issues and intended to reduce false positives and improve the frequent pattern mining accuracy based on web access logs. Proposed methods were found to be good while compared with the existing works. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Gokulapriya R.; Ganesh Kumar R.
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Lecture Notes in Networks and Systems, Vol-627 LNNS, pp. 347-356.
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Springer Science and Business Media Deutschland GmbH
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2023-01-01
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<a href="https://doi.org/10.1007/978-981-19-9858-4_29" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-9858-4_29</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161435101&doi=10.1007%2F978-981-19-9858-4_29&partnerID=40&md5=4efd0e618b153977ba60a65fc76a7b6c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161435101&doi=10.1007%2f978-981-19-9858-4_29&partnerID=40&md5=4efd0e618b153977ba60a65fc76a7b6c</a>
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ISSN: 23673370; ISBN: 978-981199857-7
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Online
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English
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Gokulapriya R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST(Deemed to be University), Karnataka, Bangalore, India; Ganesh Kumar R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST(Deemed to be University), Karnataka, Bangalore, India
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Conference Papers
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Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy
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classifier; Decision Tree; Gaussian Naive Bayes; Logistic Regression; Machine Learning; Natural Language Processing; Random Forest; Sentiment Analysis; Support Vector Machines; Twitter; XGBoost
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With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms. 2023 IEEE.
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Agarwal K.; Deepa S.; Sivabalan R.V.; Balakrishnan C.
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Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023, pp. 862-870.
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/IITCEE57236.2023.10091034" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IITCEE57236.2023.10091034</a>
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ISBN: 978-166546263-1
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Online
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English
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Agarwal K., Christ University, Department Computer Science, Bangalore, India; Deepa S., Christ University, Department Computer Science, Bangalore, India; Sivabalan R.V., Christ University, Department Computer Science, Bangalore, India; Balakrishnan C., Christ University, Department Computer Science, Bangalore, India
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Conference Papers
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Performance Analysis of YOLOv7 and YOLOv8 Models for Drone Detection
Subject
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CNN (convoluted neural network); Computer Vision; Drone Detection; UAS (Unmanned Aerial System); unmanned aerial vehicles (UAVs); Yolo (You Only Look Once); Yolov7; Yolov8
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Drone detection techniques are used to detect unmanned aerial systems (UAS) also commonly known as drones. A rapid increase in these drones has limited the airspace safety and so the research for drone detection has emerged. This study compares between the two widely used deep-learning models, previously used YOLOv7 and the latest YOLOv8. The overall finding of this study suggests that the YOLOv8 deep-learning model appears to be more promising and may make valuable contributions on their own. We got the result that for 10 epochs YOLOv8 gave 50.16% accuracy while YOLOv7 gave 48.16% accuracy making YOLOv8 more promising for the task. As a practical application for future work, we intend to deploy YOLOv8 on edge devices to achieve real-time drone detection in critical security applications. 2023 IEEE.
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Agarwal K.; Ashik Sanyo M.S.; Bakshi S.; Vinay M.; Jayapriya J.; Deepa S.
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2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/NMITCON58196.2023.10276343" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NMITCON58196.2023.10276343</a>
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ISBN: 979-835030082-6
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Online
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English
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Conference paper
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Agarwal K., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Ashik Sanyo M.S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Bakshi S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Vinay M., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India; Deepa S., Christ Universtiy, Department Computer Science, Karnataka, Bangalore, India
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Conference Papers
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Performance and Steady State Heat Transfer Analysis of Functionally Graded Thermal Barrier Coatings Systems
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FEM Analysis; Funtionally Graded Materials; High Temperature Performance; SEM analysis; Thermal Barrier Coatings
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Thermal barrier coatings (TBCfs), typically 8 wt.% Yttria Stabilized Zirconia (8YSZ), in single layered configuration have been traditionally used in aerospace components to protect them from degradation at high temperatures and to improve the thermal efficiency of the system. This paper compares the performance of two types of TBC configurations: Single layered and multilayered functionally graded materials (FGM). Aerospace alloy, Inconel 718 substrates, NiCrAlY bond coat (BC) and 8YPSZ top coat (TC) were the materials used. FGM configuration was used to improve the durability and life of the conventional TBC system by reducing the coefficient of thermal expansion (CTE) mismatch. The TBCs were subjected to thermal fatigue (thermal shock and thermal barrier test) in laboratory scale burner rig test and oxidation stability test in high temperature furnace upto 1000. The as-sprayed and thermal fatigue tested specimen were characterized by X-ray diffraction (XRD) analysis and Scanning Electron Microscope (micro-structure). Results are discussed in the light of suitability of coating configuration, thermal fatigue and spalling characteristics with reference to aerospace applications at temperatures in the 9000C to 15000C range. Computational work was carried out comprising a simulation model involving the developed TBCs. 2018 Elsevier Ltd.
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Pasupuleti K.T.; Dsouza S.; Thejaraju R.; Venkataraman S.; Ramaswamy P.; Murty N.
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Materials Today: Proceedings, Vol-5, No. 14, pp. 27936-27945.
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Elsevier Ltd
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2018-01-01
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<a href="https://doi.org/10.1016/j.matpr.2018.10.033" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.matpr.2018.10.033</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061208446&doi=10.1016%2Fj.matpr.2018.10.033&partnerID=40&md5=d9787817fdc97ee57c7da35f9c0e08b3" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061208446&doi=10.1016%2fj.matpr.2018.10.033&partnerID=40&md5=d9787817fdc97ee57c7da35f9c0e08b3</a>
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ISSN: 22147853
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Online
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English
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Conference paper
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Pasupuleti K.T., Department of Mechanical and Automobile Engineering, Faculty of Engineering, Christ, Bangalore, 566074, India; Dsouza S., Department of Mechanical and Automobile Engineering, Faculty of Engineering, Christ, Bangalore, 566074, India; Thejaraju R., Department of Mechanical and Automobile Engineering, Faculty of Engineering, Christ, Bangalore, 566074, India; Venkataraman S., Department of Mechanical and Automobile Engineering, Faculty of Engineering, Christ, Bangalore, 566074, India; Ramaswamy P., Department of Mechanical and Automobile Engineering, Faculty of Engineering, Christ, Bangalore, 566074, India; Murty N., Materials Characterization Division, Vikram Sarabhai Space Center, Trivandrum, 695022, India
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Articles
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Performance comparison of artificial neural network techniques for foreign exchange rate forecasting
Subject
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Feed forward back propagation networks; Feed forward networks; Financial time series; Pattern recognition networks radial basis function; Technical indicators
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Artificial Neural Networks is one of the promising techniques for forecasting financial time series markets and business. In this paper, Radial Basis Function is used to forecast the daily foreign exchange rate of USD in terms of Indian rupees in India during the period 2009-2014. Here, seven technical indicators like simple moving average of one week, Two week, Momentum, Price rate of change, Disparity 7, Disparity 14, Price oscillator are proposed as inputs for forecasting the time series. In addition, this study compares the four models namely Pattern Recognition Networks, Feed Forward Back Propagation Networks, Feed Forward Networks with no feedback, and Radial Basis Function Network to forecast the daily currency exchange rate during the period. The performance of all these models are analysed from accuracy measures namely Mean Square Error, Mean Absolute Error, Sum Square Error and Root Mean Square Error. From the simulation results, the average performance of Radial Basis Function network was found considerably better than the other networks. Research India Publications.
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Kumar Chandar S.; Sumathi M.; Sivanandam S.N.
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International Journal of Applied Engineering Research, Vol-9, No. 24, pp. 27433-27455.
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Research India Publications
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2014-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941313195&partnerID=40&md5=0151fdcce4c5ecc17014b993f711085d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941313195&partnerID=40&md5=0151fdcce4c5ecc17014b993f711085d</a>
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ISSN: 9734562
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Online
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English
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Article
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Kumar Chandar S., Madurai Kamaraj University, Madurai/CHRIST University, Bangalore, India; Sumathi M., Sri Meenakshi Government College for Arts for Women (Autonomous), Madurai, India; Sivanandam S.N., Karpagam College of Engineering, Coimbatore, India
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Conference Papers
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Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images
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Dropbox; Google Drive; Healthcare image; Performance analysis; Personal cloud storage
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In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Roy M.; Singh M.
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Lecture Notes in Networks and Systems, Vol-291, pp. 435-444.
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Springer Science and Business Media Deutschland GmbH
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2022-01-01
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<a href="https://doi.org/10.1007/978-981-16-4284-5_38" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-4284-5_38</a>
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ISSN: 23673370; ISBN: 978-981164283-8
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Online
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English
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Conference paper
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Roy M., Department of Computer Science, Christ Deemed To Be University, Bengaluru, India, Department of Computer Applications, CMR Institute of Technology, Bengaluru, India; Singh M., Department of Computer Applications, CMR Institute of Technology, Bengaluru, India
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Performance evaluation and sustainability analysis of geopolymer concrete developed with ground granulated blast furnace slag and sugarcane bagasse ash
Subject
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Carbon di-oxide emissions; Compressive strength; Final setting time; Nonlinear regression analysis; Sugarcane bagasse ash; Workability
Description
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This experimental work aims to determine the workability, strength and sustainability aspects of geopolymer concrete developed with GGBS and SCBA in five different proportions of 100-0%, 95 ? 5%, 90 ? 10%, 85 ? 15%and 80 ? 20%. 8M NaOH concentration and Na2SiO3 solutions are used as an alkaline activator in mixes developed. Na2SiO3 to NaOH ratio of 2.5 and 0.5 alkaline liquid to binder ratio is employed in this study to develop ambient cured geopolymer concrete. The results show that the standard consistency and FST of geopolymer paste increases with an increase in the SCBA content of mixes developed. Cs, Sts and Fs decreased with an increase in the content of SCBA in geopolymer concrete mixes. The 28 days Cs of geopolymer concrete developed under ambient cured condition varied from 63.56 to 39.59MPa. Regression analysis was performed to find the correlation between Sts and Fs to Cs. This study aims to outline a unique technique of utilizing an agro industrial waste by product i.e., sugarcane bagasse ash which in turn reduces disposal problem to some extent. According to the test findings, Sugarcane bagasse ash up to 20% can be used as precursor to develop sustainable geopolymer concrete. Due to the high cost of chemicals and river sand the cost of geopolymer concrete developed is slightly higher than normal concrete. Also, as the percentage of SCBA increase in the geopolymer concrete the demand for energy is reduced. Additionally, incorporation of sugarcane bagasse ash will also reduce disposal problems and reduces CO2 emissions into the atmosphere. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
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Tanu H M.; Unnikrishnan S.
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Journal of Building Pathology and Rehabilitation, Vol-9, No. 2
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Springer Nature
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2024-01-01
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<a href="https://doi.org/10.1007/s41024-024-00489-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41024-024-00489-1</a>
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ISSN: 23653159
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Online
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English
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Article
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Tanu H M., Department of Civil Engineering, Christ University, Bangalore, India, Department of Civil Engineering, Ballari Institute of Technology and Management, Ballari, India; Unnikrishnan S., Department of Civil Engineering, Christ University, Bangalore, India
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Articles
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Faculty Publications -Articles
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Performance Evaluation Frameworks in the Context of Indian Microfinance Institutions
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cluster analysis; data envelopment analysis; efficiency; India; microfinance; performance evaluation
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The paper conducts a detailed examination of the existing evaluative frameworks for microfinance institutions to gauge the differences and similarities. Efficiency evaluates how MFIs are meeting the performance standards considering time and budget constraints. Outreach evaluates the effectiveness of MFIs in reaching the beneficiaries. Relative efficiency scores were calculated using data envelopment analysis and outreach was measured in five different dimensions (pentagon model). Further, cluster analysis assisted in categorizing the MFIs into five value clusters. The study compares both outreach performance and relative efficiency scores employing ANOVA and correlation analysis. The study was conducted among the Indian context when the sector was hit by crisis during 2010. Paper brought out important insights about the sample. Indian MFIs were found to be more socially efficient, since the social dimension taken into consideration was number of female clients and majority of Indian MFIs has exclusive female focus. The correlation tests found that relative efficiency scores are positively related to depth (poor focus) and length (sustainability) outreach. The results showed that cluster analysis model basing outreach scores was more comprehensive and captured more information compared to the data envelopment model relative efficiency scores. The study is original in its approach in using cluster analysis for outreach performance and in the objective of comparing the two different models. 2019 Aruna Balammal et al., published by Sciendo 2019.
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Balammal A.; Madhumathi R.; Ganesh M.P.
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Foundations of Management, Vol-11, No. 1, pp. 209-228.
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Sciendo
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2019-01-01
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<a href="https://doi.org/10.2478/fman-2019-0018" target="_blank" rel="noreferrer noopener">https://doi.org/10.2478/fman-2019-0018</a>
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All Open Access; Gold Open Access; Green Open Access
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ISSN: 20807279
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Online
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English
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Article
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Balammal A., Christ University, Institute of Management, Kumbalgodu Bangalore, India; Madhumathi R., Christ University, Institute of Management, Kumbalgodu Bangalore, India; Ganesh M.P., Christ University, Institute of Management, Kumbalgodu Bangalore, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Performance Evaluation of Area-Based Segmentation Technique on Ambient Sensor Data for Smart Home Assisted Living
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Activity Recognition; Elder Care; Machine Learning; Segmentation; Smart Home
Description
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Activity recognition(AR) is a popular subject of research in the recent past. Recognition of activities performed by human beings, enables the addressing of challenges posed by many real-world applications such as health monitoring, providing security etc. Segmentation plays a vital role in AR. This paper evaluates the efficiency of Area-Based Segmentation using different performance measures. Area-Based segmentation was proposed in our earlier research work. The evaluation of the Area-Based segmentation technique is conducted on four real world datasets viz. Aruba17, Shib010, HH102, and HH113 comprising of data pertaining to an individual, living in the test bed home. Machine learning classifiers, SVM-R, SVM-P, NB and KNN are adopted to validate the performance of Area-Based segmentation. Amongst the four chosen classification algorithms SVM-R exhibits better in all the four datasets. Area-Based segmentation recognise the four test bed activities with accuracies of 0.74, 0.98, 0.66, and 0.99 respectively. The results reveal that Area based segmentation can efficiently segment sensor data stream which aids in accurate recognition of smart home activities. 2019 Procedia Computer Science. All rights reserved.
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Kavitha R.; Binu S.
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Procedia Computer Science, Vol-165, pp. 314-321.
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Elsevier B.V.
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2019-01-01
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<a href="https://doi.org/10.1016/j.procs.2020.01.044" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2020.01.044</a>
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All Open Access; Gold Open Access
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ISSN: 18770509
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Online
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English
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Conference paper
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Kavitha R., CHRIST (Deemed to Be University), Bangalore, Karnataka, India; Binu S., Bangalore, Karnataka, India
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Articles
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Title
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Performance evaluation of artificial neural networks in sustainable modelling biodiesel synthesis
Subject
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Absolute average deviation (AAD); Biodiesel; Catalyst concentration; Fatty acid methyl ester (FAME); Genetic algorithm; Machine learning (ML); Response surface methodology (RSM); Transesterification
Description
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Biodiesel is a characteristic and inexhaustible homegrown fuel removed from creature fats or vegetable oil and liquor through a transesterification response. The exploration work means to assess the exhibition of biodiesel blend. In this paper, biodiesel was displayed and improved by utilizing a hereditary calculation and Artificial Neural Network (ANN). In AI, hereditary calculations and counterfeit neural organizations assume a significant part in displaying biodiesel blend. To upgrade an excellent arrangement hereditary calculation was created. The mix of ANN and Genetic Algorithm gives the ideal condition as the temperature of methanol molar proportion, impetus fixation. It tentatively decides the exhibition trademark like the Coefficient of determination and Absolute Average deviation (AAD). It predicts the Fatty Acid Methyl Ester (FAME) model productively than Response Surface Methodology (RSM). The exhibition examination is reenacted and hypothetical outcomes are recorded then it is contrasted with constant information to decide the exactness of ANN. 2022 Elsevier Ltd
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Treve M.; Patra I.; Prabu P.; Rama Sree S.; Keerthi Kumar N.; Methkal Abd Algani Y.; Kiran Bala B.; Balaji S.
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Sustainable Energy Technologies and Assessments, Vol-52
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Elsevier Ltd
Date
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2022-01-01
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<a href="https://doi.org/10.1016/j.seta.2022.102098" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.seta.2022.102098</a>
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ISSN: 22131388
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Online
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English
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Article
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Treve M., School of Languages and General Education, Walailak University, Thailand; Patra I., An Independent Researcher and Ex-research Scholar at NIT Durgapur, West Bengal, India; Prabu P., Department of Computer Science, CHRIST(Deemed to be University), Bangalore, India; Rama Sree S., Professor in CSE Dept., Aditya Engineering College, Andhra Pradesh, Surampalem, India; Keerthi Kumar N., Department of Mechanical Engineering, BMS Institute of Technology and Management, Avalahalli, Doddaballapur Main Road, Yelahanka, Bangalore, 560064, India; Methkal Abd Algani Y., Department of Mathematics, Sakhnin College, Israel, Department of Mathematics, The Arab Academic College for Education in Israel-Haifa, Israel; Kiran Bala B., Department of Artificial Intelligence and Data Science, K. Ramakrishnan College of Engineering, Tamil Nadu, Trichy, India; Balaji S., Department of CSE, Panimalar Engineering College, Tamil Nadu, Chennai, India
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The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
Subject
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CNN; Galaxy; Parallel CNN; ResNet; Star; VGG16
Description
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This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE.
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Premanand N.; Tarun V.G.; Pawar S.; Jawakar D.N.; Utsav; Deepa S.; Jayapriya J.; Vinay M.
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2023 International Conference on Data Science and Network Security, ICDSNS 2023
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Institute of Electrical and Electronics Engineers Inc.
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2023-01-01
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<a href="https://doi.org/10.1109/ICDSNS58469.2023.10244794" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSNS58469.2023.10244794</a>
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ISBN: 979-835030159-5
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Online
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English
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Conference paper
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Premanand N., Christ University, Department of Computer Science, Bengaluru, India; Tarun V.G., Christ University, Department of Computer Science, Bengaluru, India; Pawar S., Christ University, Department of Computer Science, Bengaluru, India; Jawakar D.N., Christ University, Department of Computer Science, Bengaluru, India; Utsav, Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India