704
20
16481
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Dublin Core
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Title
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Stress Management among Employees in Information Technology Sector Using Deep Learning
Subject
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Deep Learning; Employees; Information Technology; Management; Stress
Description
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Information technology is one of the areas in India that is developing the quickest India's information technology (IT) administrations industry has become more merciless. The information technology area has been managing additional difficult issues like specialized development, administration enhancement, and worldwide overhauling starting from the beginning of this long period. Along these lines, it is unimaginable for everybody to adjust to the moving difficulties they experience in the field of information technology, which causes stress. Stress is something that individuals battle with for most of their lives. Albeit the information technology (IT) industry is notable for its hazardous turn of events and development, it is likewise portrayed by high worker burnout and stress levels. This theoretical proposes an original strategy for overseeing stress in the IT business that utilizes deep learning methods. This study utilizes deep learning calculations to expect, distinguish, and decrease stress makes all together location the earnest issue of stress among IT experts. The principal objective is to make a shrewd framework that can help organizations proactively recognize stress-related issues in their labor force and proposition specific cures. 2024 IEEE.
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Soundarraj P.L.; Pelleti S.; John E.P.; Gadgil A.A.; Sehrawat A.; Yadav L.K.
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Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568788" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568788</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198860620&doi=10.1109%2FICONSTEM60960.2024.10568788&partnerID=40&md5=1f024d4331605f99fdaf670e6555b6f1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198860620&doi=10.1109%2fICONSTEM60960.2024.10568788&partnerID=40&md5=1f024d4331605f99fdaf670e6555b6f1</a>
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ISBN: 979-835036509-2
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Online
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English
Type
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Conference paper
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Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Pelleti S., Klh Global Business School, KoneruLakshmaiah Education Foundation (Deemed To Be University, Estd. U/S. 3 of Ugc Act 1956), Catefgory - I, NIRF University Ranking: 28, Hyderabad, India; John E.P., Srm Institute of Science & Technology, Faculty of Management, Chennai, 600026, India; Gadgil A.A., E&c, KLSGogte Institute of Technology, Karnataka, Belagavi, 590008, India; Sehrawat A., Amity Institute of Corporate Communication, Amity University, Uttar Pradesh, Noida, India; Yadav L.K., Amity Institute of Corporate Communication, Amity University, UP, Noida, India
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
Subject
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Accuracy; COVID-19; DarkNet19; Data augmentation; Deep learning; Discrete wavelet transform; ResNet50; VGG19; WavStaCovNet-19; X-ray images; Xception
Description
An account of the resource
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd
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Soundrapandiyan R.; Naidu H.; Karuppiah M.; Maheswari M.; Poonia R.C.
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Computers and Electrical Engineering, Vol-108
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Elsevier Ltd
Date
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2023-01-01
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<a href="https://doi.org/10.1016/j.compeleceng.2023.108711" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compeleceng.2023.108711</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153535053&doi=10.1016%2Fj.compeleceng.2023.108711&partnerID=40&md5=7b8a533c19c2f91829ef0aed2551f962" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153535053&doi=10.1016%2fj.compeleceng.2023.108711&partnerID=40&md5=7b8a533c19c2f91829ef0aed2551f962</a>
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All Open Access; Green Open Access
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A related resource
ISSN: 457906; CODEN: CPEEB
Format
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Online
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English
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Article
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Soundrapandiyan R., School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India; Naidu H., ServiceNow, Telangana, Hyderabad, 500081, India; Karuppiah M., School of Computer Science and Engineering & Information Science, Presidency University, Karnataka, Bengaluru, 560064, India; Maheswari M., Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India
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Title
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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Recent developments in bandwidth improvement of dielectric resonator antennas /
Subject
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Engineering
Description
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International Journal of RF And Microwave Computer-Aided Engineering, Vol.29, Issue 6, pp.1-17
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Sounik Kiran Kumar Dash and Taimoor Khan
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<a href="https://onlinelibrary.wiley.com/doi/10.1002/mmce.21701" target="_blank" title="Recent developments in bandwidth improvement of dielectric resonator antennas" rel="noreferrer noopener">https://onlinelibrary.wiley.com/doi/10.1002/mmce.21701</a>
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Wiley
Date
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2018-12-29
Language
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English
Type
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Journal Article
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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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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Sourav, Mayur Kumar; Gupta, Varuna
Title
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Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes
Date
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01-01-2025
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AIP Conference Proceedings;Volume;3297;Issue;1;Article No.;286502;
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<a href="https://doi.org/10.1063/5.0286502" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0286502</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022837333?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022837333?origin=resultslist</a>
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Sourav M.K., Christ University, Bangalore, India; Gupta V., Christ University, Bangalore, India
Description
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The primary objective of this study is to predict the performance of students and evaluate the efficacy of various machine learning algorithms in predicting student success based on their marks and grades (academic factors). Through a comprehensive review of literature and experimentation, this research compares the performance of different machine learning models, including but not limited to decision trees, random forests, support vector machines, logistic regression, and neural networks. The evaluation metrics considered in this comparative analysis include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Fourteen experiments have been performed and preliminary results suggest that performances of students on the basis of academic factors might be predictable and by understanding the strengths and weaknesses of student's educational outcomes and foster student achievement can be improved. Through extensive experimentation and comparative analysis, XGBoost(ExtremeGradient Boosting) and AdaBoost demonstrated as the most effective predictive models to analyze the students' performance. 2025 Author(s).
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American Institute of Physics
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ISSN: 0094243X;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Sowbarnika, Archana; Patra, Abhinanda; Kaur, Lavleen; Suresh, Sruthi; Srivastava, Srishti; Chawla, Drishti
Title
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Chatbots as tools for psychoeducation and self-help in mental health
Date
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01-01-2025
Source
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Chatbots and Mental Healthcare in Psychology and Psychiatry;pp.55-85
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<a href="https://doi.org/10.4018/979-8-3693-3112-5.ch003" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-3112-5.ch003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000239485?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000239485?origin=resultslist</a>
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Sowbarnika A., Infiheal Healthtech Private Limited, India; Patra A., Infiheal Healthtech Private Limited, India; Kaur L., Infiheal Healthtech Private Limited, India; Suresh S., Christ University, India; Srivastava S., Infiheal Healthtech Private Limited, India; Chawla D., Infiheal Healthtech Private Limited, India
Description
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The chapter highlights the role of mental health chatbots in solving problems in global mental health. It aims to explore how AI-powered conversational agents bridge the gap between growing demand and limited availability of mental health services. The authors explain advantages of chatbots, including accessibility, affordability, and user anonymity. They investigate the effectiveness of chatbots in improving treatment outcomes, meeting user needs, and improving operational efficiency. In addition, the chapter highlights the ability of chatbot as a knowledge sharer that seamlessly integrates information from various sources and keeps professionals up to date with current events. It deals with psychoeducation in clinical and non- clinical populations, covering biological, cognitive, emotional, and behavioural aspects. The authors also discuss the potential of chatbots in screening and self- help. Finally, they emphasise the importance of collaboration between psychologists, physicians, and engineers to optimise the development of chatbots to respond dynamically to user needs. 2025, IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-836933113-2; 979-836933112-5;
Language
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English
Type
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Book chapter
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Restricted Access; Hardcopy may be available in the library
-
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Sowhnthariya, M.; Thomas, Tinu
Title
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Melatonin priming confers chromium tolerance in spinach (Spinacia oleracea) seedlings and field-grown plants
Date
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01-01-2026
Source
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International Journal of Vegetable Science;
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<a href="https://doi.org/10.1080/19315260.2026.2673029" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/19315260.2026.2673029</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039016727?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039016727?origin=resultslist</a>
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Sowhnthariya M., Department of Life Sciences, Christ University, Bangalore, India; Thomas T., Department of Life Sciences, Christ University, Bangalore, India, Centre for Landscapes, Wildlife and Marine Ecology (CLIME), Department of Life Sciences, Christ University, Bangalore, India
Description
An account of the resource
Heavy metal contamination of soils poses a significant risk to food safety by accumulating toxic metals in edible crops, such as leafy vegetables. This study tested whether melatonin seed priming reduces chromium accumulation while maintaining plant performance in Spinacia oleracea cultivar Arka Anupama under controlled and polyhouse conditions. Melatonin priming improved seedling growth under chromium stress, with 50-micromolar melatonin treated plants showing better performance. Under polyhouse conditions, 50 micromolar melatonin increased leaf number to 5.25 0.55 compared with 3.75 0.29 in chromium-stressed plants (40% increase), and improved leaf area to 3.70 0.35 cm2 in 100 micromolar melatonin-treated plants compared with 2.27 0.03 cm2 in chromium-stressed plants (63% increase). The chlorophyll normalized difference vegetation index increased from 0.15 0.04 in chromium-stressed plants to 0.26 0.01 in 50 micromolar melatonin-primed plants, representing approximately 78% improvement (significant p <.05). Atomic absorption spectroscopy analysis showed a 42.9% reduction in leaf chromium content following 50-micromolar treatment. Spectral analysis revealed stress-associated variations, including a 512515 cm?1 band under chromium stress. These findings indicate that melatonin priming improves plant growth and reduces chromium accumulation in spinach, with 50 micromolar showing the most consistent response. 2026 Taylor & Francis Group, LLC.
Subject
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Crop resilience; heavy metal toxicity; leafy vegetable; stress tolerance
Publisher
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Taylor and Francis Ltd.
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ISSN: 19315260;
Language
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English
Type
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Article
Rights
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Restricted Access; Hardcopy may be available in the library
Format
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online
-
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Title
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Faculty Publications
Book Chapter
Faculty Publications- Book Chapter
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Creator
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Sowjanya, K. Krishna; Madavi, K. P. Bindu
Title
A name given to the resource
IOT and UAV Integrated System for Proactive Crop Disease Prediction
Date
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01-01-2026
Source
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Studies in Computational Intelligence;Volume;1236;pp.287-303
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<a href="https://doi.org/10.1007/978-3-032-05603-0_13" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-05603-0_13</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105024559425?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105024559425?origin=resultslist</a>
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Sowjanya K.K., CMR Institute of Technology, Bengaluru, India; Madavi K.P.B., Christ University, Bengaluru, India
Description
An account of the resource
The productivity of agriculture is often affected by the crop diseases incurring economic loss and reduced food production. Detecting these crop diseases in the early stages is the need of the hour to mitigate the spread of the disease and manage the crop yield effectively. This paper presents a proactive crop disease prediction system by employing the technologies such as Unmanned Aerial Vehicles (UAVs) and Internet of Things (IoT) to detect and mitigate the crop disease at early stages. The employed UAVs have a high resolution camera to capture the aerial images of crops and leaves. The images captured via UAVs are transferred to a computing environment dynamically for analysis purposes. The analysis is performed on the images to predict the disease of the leaf along with its intensity. After analyzing and predicting the leaf disease and its intensity, the pesticide information is passed to the IoT sensors which are fixed in the fields to spray the recommended pesticide. The system analyzes the captured images by leveraging the machine learning algorithms in the realtime to identify different diseases and predict the spread of the disease. The proposed proactive approach helps the farmers to take preventive actions to identify the crop disease at the early stage and mitigate them in a timely manner to produce a better crop yield. Moreover, the proposed method provides a cost-effective solution for disease prediction and is easily accessible to farmers to enhance crop productivity. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Subject
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Disease prediction; Internet of things; Machine learning; Unmanned aerial vehicles
Publisher
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Springer Science and Business Media Deutschland GmbH
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A related resource
ISSN: 1860949X;
Language
A language of the resource
English
Type
The nature or genre of the resource
Book chapter
Rights
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Restricted Access; Hardcopy may be available in the library
-
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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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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The Creation of Intelligent Surfaces for the Purpose of Next-Gen Wireless Networks
Subject
The topic of the resource
5G; field programmable gate arrays; line-of-sight; liquid crystal; meta-surfaces; reconfigurable intelligent surfaces; smart reflect-arrays; wireless communications
Description
An account of the resource
In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. This paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. The study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks. 2024 IEEE.
Creator
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Sowmiya S.; Senthilkumar P.; Kuppuraj T.; Mishra A.; Muralidharan P.; Rosaline S.
Source
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2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 229-234.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/ICACITE60783.2024.10616689" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE60783.2024.10616689</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201821543&doi=10.1109%2FICACITE60783.2024.10616689&partnerID=40&md5=a623389f6a4af5ee8ab9bbcb7c113288" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201821543&doi=10.1109%2fICACITE60783.2024.10616689&partnerID=40&md5=a623389f6a4af5ee8ab9bbcb7c113288</a>
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Restricted Access
Relation
A related resource
ISBN: 979-835036016-5
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Conference paper
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Sowmiya S., Prince Shri Venkateshwara Padmavathy Engineering College, TN, India; Senthilkumar P., Nehru Institute of Engineering and Technology, Tamil Nadu, T.M.Palayam, India; Kuppuraj T., Karpagam Academy of Higher Education, TN, India; Mishra A., Ies Institute of Technology and Management, Ies University, M.P, Bhopal, India; Muralidharan P., Christ University, Bangalore, India; Rosaline S., R.M.K.Engineering College, TamilNadu, Kavaraipettai, India
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Dublin Core
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Title
A name given to the resource
Articles
Article
Faculty Publications -Articles
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
A name given to the resource
Cultural Expression of Anxiety Symptoms in Kannada Language: A Qualitative Study
Subject
The topic of the resource
Anxiety; Culture; Culture-specific tool; Qualitative; Tool development
Description
An account of the resource
Background: In anxiety disorders, culture is important in symptom presentation and help-seeking. Most tools for anxiety disorders are not validated in India and thus might not capture culture-specific aspects of anxiety. This study aims to identify and generate culturally specific terms to describe symptoms of anxiety as part of the development of the Kannada version of the Panic and Anxiety National Indian Questionnaire (PANIQ). The PANIQ is a tool under development to identify anxiety and panic in Indian settings. Methods: This study used qualitative methods like focus group discussions (FGDs) and in-depth interviews (IDIs) to identify and generate items related to anxiety and panic in Kannada from stakeholders like individuals with anxiety disorders, their caregivers, healthcare workers, and mental health professionals who treat individuals with anxiety and panic disorders. Five FGDs (n = 28), one triad (n = 3), and 34 IDIs (n = 34) were conducted. Results: The mean age of the participants was 38.9 (standard deviation: 12.28) years; 57.1% were from rural areas. We generated 615 Kannada items. These were classified into 21 domains and facets. Items in domains like Somatic symptoms, Fear, and Impairment in day-to-day life were higher than those noted in existing tools for anxiety that focus more on cognitive symptoms of anxiety. Conclusions: This study generated culturally specific items of anxiety through a qualitative process of tool development incorporating subjective experiences of persons with anxiety disorders and other stakeholders. This is among the first steps toward the development of PANIQ. 2022 The Author(s).
Creator
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Sowmya H.R.; Ghani S.; Sreedaran P.; Sahu M.K.; Mysore A.; Sharan P.
Source
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Indian Journal of Psychological Medicine, Vol-45, No. 5, pp. 496-502.
Publisher
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SAGE Publications Ltd
Date
A point or period of time associated with an event in the lifecycle of the resource
2023-01-01
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<a href="https://doi.org/10.1177/02537176221140742" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/02537176221140742</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145209847&doi=10.1177%2F02537176221140742&partnerID=40&md5=55b017545f2646c5e790ce090c1f47fa" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145209847&doi=10.1177%2f02537176221140742&partnerID=40&md5=55b017545f2646c5e790ce090c1f47fa</a>
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ISSN: 2537176
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Online
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English
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Article
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Sowmya H.R., St. Johns Research Institute, Karnataka, Bangalore, India; Ghani S., Christ University, Karnataka, Bangalore, India; Sreedaran P., Dept. of Psychiatry, St. Johns Medical Hospital, Karnataka, Bangalore, India; Sahu M.K., Dept. of Psychiatry, Pt. J.N.M. Medical College, Karnataka, Raipur, India; Mysore A., Dept. of Psychiatry, St. Johns Medical Hospital, Karnataka, Bangalore, India; Sharan P., Dept. of Psychiatry, All India Institute of Medical Sciences, New Delhi, India
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Conference Papers
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Faculty Publications- Conference Papers
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Framework for Controlling Interference and Power Consumption on Femto-Cells In-Wireless System
Subject
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Cellular network; Coverage; Energy efficiency; Femto-cells; Power consumption
Description
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Utilization of femto-cells is one of the cost effective solution to increase the internal network connectivity and coverage. However, there are various impediment in achieving so which has caused a consistent research work evolving out with solution. Review of existing literature shows that maximum focus was given for energy problems in cellular network and not much on problems that roots out from interference. Therefore, the proposed system has presented a very simple and novel approach where the problems associated with interference and energy in using large groups of femto-cells are addressed. Adopting analytical research methodology, the proposed model offers on-demand utilization of the selective femto-cells on the basis of the traffic demands. The study outcome shows that proposed system offers better performance in contrast to existing approach. Springer Nature Switzerland AG 2019.
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Sowmya Naik P.T.; Narasimha Murthy K.N.
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Advances in Intelligent Systems and Computing, Vol-1046, pp. 199-208.
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Springer
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2019-01-01
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<a href="https://doi.org/10.1007/978-3-030-30329-7_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-30329-7_19</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075606968&doi=10.1007%2F978-3-030-30329-7_19&partnerID=40&md5=9f80bba8cada0e90e5ae29d542bcfb02" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075606968&doi=10.1007%2f978-3-030-30329-7_19&partnerID=40&md5=9f80bba8cada0e90e5ae29d542bcfb02</a>
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ISSN: 21945357; ISBN: 978-303030328-0
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Online
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English
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Conference paper
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Sowmya Naik P.T., Department of Computer Science and Engineering, City Engineering College, Bengaluru, India; Narasimha Murthy K.N., Faculty of Engineering, Christ (Deemed to be University), Bengaluru, India
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Conference Papers
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Title
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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of Machines in Manufacturing Industry
Subject
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Artificial Intelligence; CBM; Industry 4.0; PHM; Predictive maintenance
Description
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Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. 2023 IEEE.
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Sowmya P.; Ravichandran S.K.; Rakshitha
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Proceedings of the 3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023, pp. 194-199.
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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/ICAIS56108.2023.10073753" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAIS56108.2023.10073753</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152452157&doi=10.1109%2FICAIS56108.2023.10073753&partnerID=40&md5=95e003f0406753caa9a21f7d7086d49e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152452157&doi=10.1109%2fICAIS56108.2023.10073753&partnerID=40&md5=95e003f0406753caa9a21f7d7086d49e</a>
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ISBN: 978-166546216-7
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Online
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English
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Conference paper
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Sowmya P., Nmam Institute of Technology-Affiliated to NITTE(Deemed to Be University), Department of Robotics and Artificial Intelligence, Karnataka, Nitte, India; Ravichandran S.K., Christ University, School of Engineering and Technology, Department of Computer Science, Karnataka, Bangalore, India; Rakshitha, Nmam Institute of Technology-Affiliated to NITTE(Deemed to Be University), Department of Artificial Intelligence and Machine Learning, Karnataka, Nitte, India
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Articles
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Title
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A comprehensive review of AI based intrusion detection system
Subject
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Artificial intelligence; Deep learning; Ensemble learning; Intrusion detection system; Machine learning
Description
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In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023
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Sowmya T.; Mary Anita E.A.
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Measurement: Sensors, Vol-28
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Elsevier Ltd
Date
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2023-01-01
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<a href="https://doi.org/10.1016/j.measen.2023.100827" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.measen.2023.100827</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163471347&doi=10.1016%2Fj.measen.2023.100827&partnerID=40&md5=38c5ff8dc0c55fc5ce68b9a51dca1a02" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163471347&doi=10.1016%2fj.measen.2023.100827&partnerID=40&md5=38c5ff8dc0c55fc5ce68b9a51dca1a02</a>
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ISSN: 26659174
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Online
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English
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Article
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Sowmya T., Department of CSE, Christ University Bangalore, CMR Institute of Technology, Bengaluru, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore, India
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Articles
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Intelligent machine learning approach for cidscloud intrusion detection system
Subject
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Cloud intrusion detection system; Feature selection; KLS framework; Machine learning; UNSW-NB15 dataset
Description
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In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
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Sowmya T.; Muneeswari G.
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Lecture Notes on Data Engineering and Communications Technologies, Vol-66, pp. 873-885.
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Springer Science and Business Media Deutschland GmbH
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2021-01-01
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<a href="https://doi.org/10.1007/978-981-16-0965-7_67" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-16-0965-7_67</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114611150&doi=10.1007%2F978-981-16-0965-7_67&partnerID=40&md5=882b13c7e0696c7d005a4ae4d175e86a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114611150&doi=10.1007%2f978-981-16-0965-7_67&partnerID=40&md5=882b13c7e0696c7d005a4ae4d175e86a</a>
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ISSN: 23674512
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Online
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English
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Article
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Sowmya T., School of Engineering and Technology CHRIST (Deemed to be University), Bangalore, India; Muneeswari G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, India
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Faculty Publications
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Sowmya, Amancherla; Balasundaram, Sathiyaseelan; Basavaraj, Santosh; Yadav, Radha
Title
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From Exclusion to Inclusion: Empowering LGBT Integration with Allies, Workplace Strategies and Family Role Models
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01-01-2026
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Journal of Human Values;Volume;32;Issue;2;pp.139-156
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<a href="https://doi.org/10.1177/09716858241312281" target="_blank" rel="noreferrer noopener">https://doi.org/10.1177/09716858241312281</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218675033?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218675033?origin=resultslist</a>
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Sowmya A., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India; Balasundaram S., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India; Basavaraj S., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India; Yadav R., School of Business and Management, Christ (Deemed to be University), Karnataka, Bengaluru, India
Description
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The workplace encounters challenge due to the absence of inclusive environments, resulting in potential loss of top-tier talent, diminished productivity and diminished business performance. This research endeavours to construct a comprehensive framework for the integration of Lesbian, Gay, Bisexual and Transgender (LGBT) individuals, focusing on embedding gender and sexual minorities. This study examines the relationship between diversity-friendly workplace management and LGBT integration through advocacy by allies (ABA) and family role modelling. The research framework is constructed based on the principles of sociometric, signalling and family systems theory and is undertaken within the Indian IT/ITES sector, involving 546 employees across 9 technology parks through a survey methodology. The analysis was carried out utilizing Smart PLS software, employing structural equation modelling and making predictions using partial least squares. Mediation and moderation analyses were performed. Findings demonstrate that effective management of diversity-friendly workplaces has a favourable impact on the integration of LGBT individuals in work environments. Results also suggest that ABA plays a supporting role in this relationship through complementary mediation, while the influence of family role modelling is moderated. This study contributes substantially to both theoretical understanding and managerial practices. A cross-cultural, longitudinal and a qualitative perspective could have added more insights to the study. 2025 Management Centre for Human Values
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advocacy; allies; diversity-friendly workplace; family role modelling; inclusion; integration; LGBT; policies
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Sage Publications India Pvt. Ltd
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ISSN: 9716858;
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English
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
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Faculty Publications
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Sowmya, K.; Richard, Mariyan A.; Kavitha, S.
Title
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The Adverse Impact of Yellow Disease of Leaves in Different Plant Species
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01-01-2025
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16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.9207-9214
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<a href="https://www.scopus.com/pages/publications/105034877303?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034877303?origin=resultslist</a>
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Sowmya K., Nitte Meenakshi Institute of Technology, Bengaluru, India; Richard M.A., Atria Institute of Technology, Bengaluru, India, Christ University, Bengaluru, India; Kavitha S., Atria Institute of Technology, Bengaluru, India, Christ University, Bengaluru, India
Description
An account of the resource
Yellow Leaf Disease (YLD), or chlorosis, reduces crop health and productivity, affecting plants like citrus, wheat, and bananas. This study reviews the causes of YLD, including bacterial, viral, and fungal infections, along with poor nutrition and environmental stress. It highlights the importance of early detection through novel methods like molecular diagnostics and remote sensing. The review also stresses the need to understand the interaction between disease, nutrition, and environment for effective management. Breeding YLD-resistant crops is proposed as a potential solution. This work serves as a foundation for future research to mitigate YLD's impact on agriculture. Grenze Scientific Society, 2025.
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Agriculture; Infection; Sugarcane Yellow Leaf Virus (ScYLD); Yellow Leaf Disease
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Grenze Scientific Society
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Faculty Publications
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Sowmya, T.; Anita, E. A. Mary
Title
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A Novel SHiP Vector Machine for Network Intrusion Detection
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01-01-2025
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IEEE Access;Volume;13;pp.117445-117463
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<a href="https://doi.org/10.1109/ACCESS.2025.3586296" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2025.3586296</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010190732?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010190732?origin=resultslist</a>
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Sowmya T., Manipal Academy of Higher Education, Manipal Institute of Technology, Manipal, Bengaluru, 576104, India, Christ University, School of Engineering and Technology, Department of Computer Science and Engineering, Bengaluru, 560074, India; Anita E.A.M., Christ University, Department of Computer Science and Engineering, Karnataka, Bengaluru, 560074, India
Description
An account of the resource
In this paper, network intrusion detection is proposed using an improved version of the support vector machine model to detect DoS attacks. Here, the SVM model considers the weight parameter along with the kernel to find the best decision boundary that separates the data into DoS and normal. The proposed model provides a novel kernel trick that reduces the overlapping of data. The intrusion detection system aims to construct an ideal system that can detect attacks with very high performance using a ShiP vector machine(Sophisticated High Performance Vector Machine). The framework comprises three major steps: data collection and preprocessing, Recursive Feature Elimination (RFE) based feature selection, and the ShiP Vector Machine classification strategy. The system is evaluated using the DoS dataset from UNSWNB15 and real time PSD-23 sniffer dataset. DoS data is generated by extracting the normal and DoS attacks from the UNSWNB15 dataset. Experimental results show that the proposed ShiP vector machine shows outstanding performance by achieving 96.44 % accuracy on the DoS dataset and 90.12 % accuracy for real time PSD-23 data. 2013 IEEE.
Subject
The topic of the resource
DoS dataset recursive feature elimination (RFE); PSD-23 dataset; SHiP vector machine (sophisticated high performance vector machine)
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Institute of Electrical and Electronics Engineers Inc.
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ISSN: 21693536;
Language
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English
Type
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Article
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All Open Access; Gold Open Access; Green Open Access
Format
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online
-
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Sowmya, T.; Mary Anita, E.A.
Title
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A novel stable feature selection algorithm for machine learning based intrusion detection system
Date
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01-01-2025
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Procedia Computer Science;Volume;252;pp.738-747
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<a href="https://doi.org/10.1016/j.procs.2025.01.034" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2025.01.034</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85219199616?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85219199616?origin=resultslist</a>
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Sowmya T., Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India, Department of Computer Science and Engineering, School of Engineering and Technology, Christ(Deemed to Be University), Bangalore, India; Mary Anita E.A., Department of Computer Science and Engineering, CHRIST (Deemed to Be University), Karnataka, Bengaluru, 560074, India
Description
An account of the resource
The advent of new technologies like artificial intelligence, and big data has influenced many cyber attackers to launch their attacks on the network. Hence researchers have already proposed Intrusion Detection Systems by incorporating machine learning as well. Building an effective IDS is still a challenging task because of low accuracy. Managing high dimensional data is another major problem that occurs in IDS. Hence in this paper, an efficient Machine Learning based Intrusion Detection System is developed by means of a novel stable feature selection strategy called IV-RFE. The proposed methodology aims to select only the relevant features that contribute to the attack, which is purely based on relative variance, and weight factor in combination with RFE. This methodology increases the performance in terms of accuracy and maintains a stable set of features. Previous studies only focussed on the feature selection strategy and their performance. The feature stability also has to be considered which is an equally important metric, especially in the field of Intrusion Detection Systems. Hence in the current study, an efficient ML based IDS is proposed which selects only the relevant and stable features. Experimental results also revealed that the proposed IV-RFE outperformed well for three attacks with respect to accuracy and stability metrics also. The results show that stability is also an important indicator in selecting the features in the field of Intrusion Detection Systems. 2025 The Authors. Published by Elsevier B.V.
Subject
The topic of the resource
Feature Selection(FS); Intrusion Detection System(IDS); Machine Learning; Recursive Feature Elimination(RFE)
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Elsevier B.V.
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ISSN: 18770509;
Language
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English
Type
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Conference paper
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All Open Access; Gold Open Access
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Title
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Sowmya, T.; Mary Anita, E.A.; Lapina, Maria
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UK-IDS-Machine Learning Based Intrusion Detection System for Unknown Attack Detection
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01-01-2026
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Lecture Notes in Networks and Systems;Volume;1456 LNNS;pp.425-433
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<a href="https://doi.org/10.1007/978-3-032-07275-7_38" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-07275-7_38</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023155002?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023155002?origin=resultslist</a>
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Sowmya T., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation
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Computer networks have become the major focus for attackers. Hence intrusion detection system plays a significant role in detecting attacks. Many researchers have already focused on the domain of cyber security by developing an efficient framework. However, developing an efficient IDS is still a challenging task because of its effectiveness in determining novel attacks. Hence in the current study, a machine learning based IDS called UK-IDS is proposed by incorporating OC-SVM and a basic SVM model. The aim of the proposed system is to achieve high accuracy and F1 score by detecting novel attacks. The OC-SVM approach identifies the novel attacks by collaborating the clustering and thresholding mechanism. The basic SVM model is to distinguish the type of attack. The experimental study reveals that UK-IDS framework shows good performance in terms of accuracy and F1 score. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Artificial Intelligence (AI); Intrusion Detection System(IDS); One Class SVM(OC-SVM); Unknown -Intrusion Detection System(UK-IDS)
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Springer Science and Business Media Deutschland GmbH
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ISSN: 23673370; ISBN: 978-303207274-0;
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English
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Articles
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An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems
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Feature selection; Hybrid GA-PI algorithm; Network Intrusion Detection Systems (NIDS); Support vector machines (SVM)
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The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved.
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Sowmya; Mary Anita T.
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International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 7s, pp. 718-731.
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Ismail Saritas
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2023-01-01
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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-s2.0-85165153935&partnerID=40&md5=61ac7ed13d9a5cb1b861cf350fb54da6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165153935&partnerID=40&md5=61ac7ed13d9a5cb1b861cf350fb54da6</a>
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Restricted Access
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ISSN: 21476799
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Online
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English
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Article
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Sowmya, Christ University & Assistant professor, CMRIT, Karnatka, Bangalore, India; Mary Anita T., Christ University Bangalore, Karnatka, Bangalore, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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Innovative Method for Detecting Liver Cancer using Auto Encoder and Single Feed Forward Neural Network
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Auto Encoder (AE); Convolutional Neural Network (CNN); Extreme Learning Machine; Support Vector Machine (SVM)
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Liver cancer ranks sixth among all cancers in frequency of incidence. A CT scan is the gold standard for diagnosis. These days, CT scan images of the liver and its tumor can be segmented using deep learning and Neural Network techniques. In this proposed approach to identifying cancer cells, it's focus on four important areas: To enhance a photo by taking out imperfections and unwanted details. An ostu method is used for this purpose. Specifically, this proposed approach to use the watershed segmentation technique for image segmentation, followed by feature extraction, in an effort to isolate the offending cancer cell. After finishing the model training with AE-ELM. To do this, Extreme Learning Machine incorporates an auto encoder. To achieve effective and supervised recognition, the network's strengths of Extreme Learning Machine (ELM) are thoroughly leveraged, including its few training parameters, quick learning speed, and robust generalization ability. The auto encoder-extreme learning machine (AE-ELM) network has been shown to have a respectable recognition impact when the sigmoid activation function is used and the number of hidden layer neurons is set to 1200. According to the results of this investigation, a method based on AE-ELM can be utilized to detect the liver tumor. As compared to the CNN and ELM models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE.
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Sowparnika B.; Yamini K.; Walid M.A.A.; Prasad J.; Aparna N.; Chauhan A.
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Proceedings of the 2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, pp. 156-161.
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Institute of Electrical and Electronics Engineers Inc.
Date
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2023-01-01
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<a href="https://doi.org/10.1109/ICAAIC56838.2023.10140207" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICAAIC56838.2023.10140207</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163700872&doi=10.1109%2FICAAIC56838.2023.10140207&partnerID=40&md5=997f62c923c1d9e75d0626636f431fe0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163700872&doi=10.1109%2fICAAIC56838.2023.10140207&partnerID=40&md5=997f62c923c1d9e75d0626636f431fe0</a>
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Restricted Access
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ISBN: 978-166545630-2
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Online
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English
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Conference paper
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Sowparnika B., Nandha Engineering College, Department of Biomedical Engineering, Tamilnadu, Erode, India; Yamini K., Sriher University, Cyber Security in Computer Science and Engineering, Sri Ramachandra Faculty of Engineering, Tamilnadu, Chennai, India; Walid M.A.A., Khulna University of Engineering & Technology (KUET), Department of Computer Science and Engineering, Bangladesh; Prasad J., Shri Shankaracharya College of Pharmaceutical Sciences, Chhattisgarh, Junwani, Bhilai, India; Aparna N., Dhaanish Ahmed Institute of Technology, Department of Bio Medical Engineering, Tamilnadu, Coimbatore, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India