133
20
16481
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Faculty Publications
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Jain, Rituraj; Singh, Sitesh Kumar; Upreti, Kamal; Bekuma, Yohannes
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Artificial Intelligence and Machine Learning in Advanced Materials Science: A New Era of Innovation
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01-01-2026
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Engineering Solutions for Modern Challenges in Advanced Materials Science;pp.1-34
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<a href="https://doi.org/10.4018/979-8-3373-3556-8.ch001" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-3556-8.ch001</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033082624?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033082624?origin=resultslist</a>
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Jain R., Department of Information Technology, Marwadi University, Rajkot, Gujarat, India; Singh S.K., National University of Science and Technology, Oman; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Bekuma Y., Wollega University, Ethiopia
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The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smartproduction ofpolymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials. 2026 by IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-833733558-2; 979-833733556-8;
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English
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Nagashruthi, M.K.; Hemanth, K.S.
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Artificial Intelligence and Machine Learning in Clinical Care: Revolutionizing Decision Support
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01-01-2025
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Innovation in HealthTech: a Roadmap for Empowering Healthcare;pp.95-116
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<a href="https://doi.org/10.1201/9781003516163-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003516163-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002517479?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002517479?origin=resultslist</a>
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Nagashruthi M.K., School of Computer Science and Application, REVA University, Karnataka, Bangalore, India; Hemanth K.S., Department of Computer Science, Christ University, Karnataka, Bangalore, India
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The potential of artificial intelligence (AI) and machine learning to significantly modify clinical decision support is examined in this chapter. AI algorithms can use extensive databases of imaging outcomes, clinical trials, and medical records to identify complex patterns that lead to precise diagnoses, treatment plans, and progressively affected patient outcomes. A diagnosis includes evaluating the patients condition leveraging information gathered from multiple kinds of tests and their past medical history. AI-driven systems in the healthcare industry are constrained by the difficulty of handling tiny volumes and poor-quality medical data. A better prediction system for low-quality data and the analysis of unusual and sensitive medical cases can be analyzed by more powerful AI technologies. The chapter shows how AI-powered equipment is presently affecting healthcare. With excellent accuracy, device getting-to-know algorithms can examine medical images and identify potential abnormalities in X-rays, mammograms, or other imaging modes that a human might overlook. Furthermore, AI may review an affected persons records and show fitness facts to estimate a patients vulnerability to specific diseases, allowing for active intervention and preventative measures. The chapter concludes with critical tips for optimizing AIs complete range of applications in scientific care. To ensure the ideal and ethical application of these effective technologies, the responsibilities consist of defensive record safety and privacy, tackling algorithmic bias, and inspiring cooperation among clinical experts and AI developers. The healthcare zone can enter a modern section of using statistics to make educated selections through the implementation of AI and machine-gaining knowledge. 2025 selection and editorial matter, Rakesh Kumar and Meenu Gupta individual chapters, the contributors.
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CRC Press
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ISBN: 978-104033782-0; 978-103285027-6;
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English
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Faculty Publications
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Akshaya, A.V.R.; Vijayalakshmi, B.; Nisha, P.; Ramachandran, Anupriya; Sathiyaseelan, Vasanthaseelan
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Artificial Intelligence and Machine Learning in Detecting Autism: Transforming Diagnosis and Care
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01-01-2026
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Artificial Intelligence in Detecting Autism;pp.124-142
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<a href="https://doi.org/10.1201/9781003541318-14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003541318-14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037471163?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037471163?origin=resultslist</a>
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Akshaya A.V.R., School of Business and Management, CHRIST University, Bengaluru, India; Vijayalakshmi B., Department of ECE, Sri Krishna College of Engineering and Technology, Coimbatore, India; Nisha P., Department of CSE, Dr. NGP Institute of Technology, Coimbatore, India; Ramachandran A., Department of Software Systems, Sri Krishna Arts and Science, Kuniyamuthur, Coimbatore, India; Sathiyaseelan V., Department of Autotronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
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Autism Spectrum Disorder (ASD) is a condition that involves many aspects and falls into the category of neurodevelopmental disorders. This is shown by problems in socializing, talking and repetitive actions. Despite the fact that early intervention is beneficial such initiatives may be postponed due to the lengthy process of assessing the disease by qualified doctors. More people are interested in AI and ML technologies which may help detect ASD earlier and more accurately. The goal of this paper is to describe the machine learning techniques used to spot ASD in individuals of any age, using information from behaviour, genes and brain images. It applies supervised learning, unsupervised learning and deep learning, using Support Vector Machines, Random Forests and Convolutional Neural Networks to find autistic patterns in complex data. We also discuss the use of facial recognition, speech recognition, motion analysis and wearable devices in helping with early detection and creating personal intervention programs. At the same time, these technologies are concerned with data accuracy, biased algorithms, lack of openness and ethical and social issues such as data safety and consent. It explains the benefits and the issues that come with using AI and ML in healthcare to find cases of autism spectrum disorder (ASD). By working together, policymakers, researchers and clinicians can help these technologies advance the diagnosis and treatment of ASD which will improve the lives of those with ASD and their families. 2026 Ram Kumar Chenthur Pandian, Shanmuga Raju Sekar, Subrata Chowdhury, Muhammad Rukunuddin Ghalib, and Kassian T.T. Amesho.
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CRC Press
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ISBN: 978-104053668-1; 978-103286653-6;
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English
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Book chapter
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Faculty Publications
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George, Alwin T.; Gupta, Varuna; Mehndiratta, Vandana
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Artificial Intelligence and Machine Learning in Educational Apps
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01-01-2026
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Cutting-Edge Mobile Applications for Higher Education;pp.39-90
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<a href="https://doi.org/10.4018/979-8-3693-8287-5.ch002" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8287-5.ch002</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031914524?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031914524?origin=resultslist</a>
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George A.T., Christ University, Bangalore, India; Gupta V., Christ University, Bangalore, India; Mehndiratta V., Christ University, Bangalore, India
Description
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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in educational applications is transforming higher education by enhancing personalized learning, intelligent tutoring, and predictive analytics. This chapter explores AI-driven functionalities, including adaptive learning, NLP, and automated assessments, while addressing challenges such as data privacy, algorithmic bias, and accessibility. Through case studies, it highlights AIs transformative potential in shaping future education, offering insights for educators, researchers, and developers interested in AI-driven learning innovations. 2026 by IGI Global Scientific Publishing. All rights reserved.
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IGI Global
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ISBN: 979-836938289-9; 979-836938287-5;
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English
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Mehndiratta, Vandana; Gupta, Varuna; Srivastava, Shilpa; Singh, Meenu
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Artificial Intelligence and Machine Learning in Financial Fraud Research: A Bibliometric Analysis of Trends and Collaborations
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01-01-2025
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2025 International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2025;pp.1100-1104
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<a href="https://doi.org/10.1109/EmergIN67762.2025.11450892" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/EmergIN67762.2025.11450892</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036390358?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036390358?origin=resultslist</a>
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Mehndiratta V., Christ University, School of Sciences, Bengaluru, India; Gupta V., Christ University, School of Sciences, Bengaluru, India; Srivastava S., Christ University, School of Sciences, Bengaluru, India; Singh M., VB-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Ostrava, Czech Republic
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This study conducts a bibliometric analysis of the financial fraud research domain, synthesizing publication metadata to uncover collaboration structures, thematic hotspots, and temporal trends. The research utilizes the bibliometric tool Vos viewer to retrieve the insights on the various aspects of financial fraud detection using the emerging technologies. The study analyses the integration of AI and machine learning techniques on the detection of financial fraud. Using co-authorship networks, we identify influential authors and collaboration clusters, while keyword frequency and co-occurrence patterns reveal core topics and emerging fronts such as anomaly detection, machine learning, insider trading, and regulatory analytics. Temporal profiling of publications highlights growth phases and shifts in emphasis across years and venues. The analysis provides an integrated view of the field's intellectual structure, enabling scholars and practitioners to locate key contributors, benchmark thematic coverage, and identify gaps for future inquiry. Results offer actionable insights for forming research collaborations, prioritizing topics, and designing evidence- based strategies against financial fraud. 2025 IEEE.
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bibliometrics; co-authorship; co-occurrence; financial fraud; scient metrics; topic modelling
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833155603-7;
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English
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Conference paper
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Conference Papers
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Artificial Intelligence and Machine Learning-Based Systems for Controlling Medical Robot Beds for Preventing Bedsores
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actuators; AI; centralized control; integrated data; IoT; microcontroller; pressure ulcer; robotics
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Artificial Intelligence is one of the most important technologies of the modern world which is continuously changing the dimensions of almost every sector. AI and IoT have together resulted in multiple outstanding technological innovations which have also impacted the healthcare sector massively. This study has critically focused on the role of AI and robotics in the treatment outcomes for patients. This study has done deep research regarding the role of automated beds in reducing pressure ulcers or bed sores among patients who are recovering from any chronic disease. This entire study has secondary qualitative data collection for analyzing the design and microcontroller systems in automated beds. This has provided a detailed data analysis with relevant equations and tables for reaching its proposed outcomes. 2022 IEEE.
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Jaichandran R.; Rayavarapu S.; Salve A.R.; Rajagopal M.; Prasad G.N.R.; Muda I.
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Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, pp. 2105-2109.
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Institute of Electrical and Electronics Engineers Inc.
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2022-01-01
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<a href="https://doi.org/10.1109/IC3I56241.2022.10073403" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3I56241.2022.10073403</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151947976&doi=10.1109%2FIC3I56241.2022.10073403&partnerID=40&md5=e16e55f5a437c62d6f2535a18c81a8be" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151947976&doi=10.1109%2fIC3I56241.2022.10073403&partnerID=40&md5=e16e55f5a437c62d6f2535a18c81a8be</a>
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ISBN: 979-835039826-7
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Online
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English
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Conference paper
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Jaichandran R., Aarupadai Veedu Institute of Technoloyg, Vinayaka Mission's Research Foundation, India; Rayavarapu S., Liverpool John Moores University, United Kingdom; Salve A.R., Indira College of Engineering and Management, India; Rajagopal M., School of Business and Management Christ Deemed to Be University, India; Prasad G.N.R., Chaitanya Bharathi Institute of Technology, India; Muda I., Universitas Sumatera Utara, Indonesia
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Book Chapter
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Artificial intelligence and service marketing innovation
Description
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The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved.
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Monica R.; Soju A.V.; Kumar B.S.
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AI Innovation in Services Marketing, pp. 150-172.
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IGI Global
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2024-01-01
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<a href="https://doi.org/10.4018/979-8-3693-2153-9.ch007" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-2153-9.ch007</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196232913&doi=10.4018%2F979-8-3693-2153-9.ch007&partnerID=40&md5=4c2911a2e03dafb278fd8ef41090e2cf" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196232913&doi=10.4018%2f979-8-3693-2153-9.ch007&partnerID=40&md5=4c2911a2e03dafb278fd8ef41090e2cf</a>
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ISBN: 979-836932154-6; 979-836932153-9
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Online
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English
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Book chapter
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Monica R., Christ University, India; Soju A.V., Christ University, India; Kumar B.S., Christ University, India
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Conference Papers
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Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Subject
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Agricultural; Artificial Intelligence; Human Resources; Information Systems
Description
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Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE.
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Ramasamy K.; Rajendran S.; Saurabh S.; Dwivedi S.; Soundarraj P.L.; Jeyalakshmi S.
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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.
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2024-01-01
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<a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568833" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568833</a>
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ISBN: 979-835036509-2
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Online
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English
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Conference paper
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Ramasamy K., Sri Krishna Arts And Science College, Department of Management Science, Coimbatore, Kuniyamuthur, India; Rajendran S., Psg College of Arts And Science, Department of Corporate Secretaryship, Coimbatore, India; Saurabh S., I Business Institute, Knowledge Park-II, UP, Greater Noida, 201310, India; Dwivedi S., I Business Institute, Knowledge Park-II, UP, Greater Noida, 201310, India; Soundarraj P.L., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India; Jeyalakshmi S., Psg College of Arts And Science, Department of Corporate Secretaryship, Coimbatore, India
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Faculty Publications
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Verma, Balraj; Schulze, Mike; Goswami, Divya; Upreti, Kamal
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Artificial intelligence attitudes and resistance to use robo-advisors: exploring investor reluctance toward cognitive financial systems
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01-01-2025
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Frontiers in Artificial Intelligence;Volume;8;Issue;;Article No.;1623534;
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<a href="https://doi.org/10.3389/frai.2025.1623534" target="_blank" rel="noreferrer noopener">https://doi.org/10.3389/frai.2025.1623534</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017896058?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017896058?origin=resultslist</a>
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Verma B., Chitkara Business School, Chitkara University, Punjab, India; Schulze M., CBS International Business School, CBS University of Applied Sciences, Mainz, Germany; Goswami D., Chitkara Business School, Chitkara University, Punjab, India; Upreti K., Department of Computer Science, Christ University, Delhi, India
Description
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Introduction: The study investigates resistance towards Financial Robo-Advisors (FRAs) among retail investors in India, grounded in innovation resistance theory. The study examines the impact of functional barriers and psychological barriers on resistance to FRAs, while considering users attitudes towards Artificial Intelligence (AI) as a moderator. It further evaluate the influence of such resistance on users intentions to use and recommend FRAs. Methods: Utilizing purposive sampling data was collected from 409 investors and further analyzed using structural equation modelling. Results: The findings revealed that all barriers under study, expect value barrier, substantially derive resistance towards robo-advisors, with inertia being the strongest determinant. Further, this resistance impedes both the intention to use FRAs and to recommend them. Moderation analysis results finds that users attitude towards AI significantly weakens the influence of inertia, overconfidence bias and data privacy risk on resistance, with no such impact on other relationships. Discussion: Overall, the study enriches IRT in Fintech context and provides theoretical and practical insights to enhance FRAs adoption in emerging markets. Copyright 2025 Verma, Schulze, Goswami and Upreti.
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artificial intelligence; attitude toward AI; cognitive financial systems; FRA; resistance; robo-advisors
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Frontiers Media SA
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ISSN: 26248212;
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English
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Article
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All Open Access; Gold Open Access; Green Open Access
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online
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Antony, Joyal; Bobby, George; Joshi, Jowin; Jayapandian, N.
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Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing
Date
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01-01-2026
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Communications in Computer and Information Science;Volume;2621 CCIS;pp.343-355
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<a href="https://doi.org/10.1007/978-3-032-00793-3_27" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-00793-3_27</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015690703?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015690703?origin=resultslist</a>
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Antony J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Bobby G., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Joshi J., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India
Description
An account of the resource
Question paper generation is a crucial task in education, where the objective is to design an assessment that effectively evaluates students knowledge and understanding of various subjects. Traditional methods of question paper generation can be exceedingly difficult, time-consuming, and inappropriate and may not be fully optimized. They ensure a comprehensive assessment of students knowledge. The system also offers the flexibility to customize question papers based on specific preferences and requirements. This research introduces a comparative approach to question paper generation using Latent Semantic Analysis (LSA), Word Embedding, and Sequence-to-Sequence (Seq2Seq) models, leveraging the power of Artificial Intelligence (AI) and Natural Language Processing (NLP). This model compares their Semantic Representation Quality, Context Understanding, and Computational Complexity. Comparing these techniques shows that LSA offers simplicity but may lack precision, while word embedding balances complexity and semantic understanding. Seq2Seq models, despite their complexity, provide contextually rich mappings with the highest degree of fine-tuning potential. This comparative analysis underscores the importance of understanding the nuances and trade-offs of each approach, enabling educators to make informed decisions in adopting these technologies to enhance educational practices and student learning experiences. A few modules are included in this system, including the admin module, add user, subject selection, question entry, question management, paper management and difficulty level. By capitalizing on the capabilities of LSA, Seq2Seq models, and word embedding, educators can revolutionize the process of question paper generation, ultimately leading to more effective and impactful student learning outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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Artificial Intelligence; Latent Semantic Analysis; Natural Language Processing; Recurrent Neural Networks; Seq2Seq models
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Springer Science and Business Media Deutschland GmbH
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ISSN: 18650929; ISBN: 978-303200792-6;
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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61000346
Title
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Artificial Intelligence Based Computational Framework for Identification and Classification of Interstitial Lung Diseases Using HRCT Images
Subject
The topic of the resource
Computer Science
Description
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Interstitial Lung Diseases (ILDs) refer to a wide array of respiratory disorders characterised via infection and scarring of the lung's interstitial tissue. These conditions affect the space within the air sacs, compromising the lungs' ability to expand and contract properly. ILDs manifest with a range of symptoms, including persistent cough, shortness of breath, and fatigue. Diagnosis of ILDs often involves imaging methods, mainly High-Resolution Computed Tomography (HRCT), to assess lung abnormalities. ILDs can have lasting effects on respiratory function, leading to progressive fibrosis. The primary obstacle in identifying ILDs lies in the diverse array of symptoms they present, making it challenging to distinguish them from other pulmonary disorders. The HRCT is a commonly employed method in ILD diagnosis. These images provide a detailed depiction of lung tissue, revealing its size, shape, and any notable abnormalities or changes. Moreover, HRCT plays a crucial role in monitoring disease progression over time. Deep Learning (DL) excels in detecting patterns
in intricate medical images that may pose challenges for traditional methods. Moreover, DL algorithms exhibit the ability to identify subtle changes in medical images indicative of pathology, and they can automate object detection tasks. The application of DL in medical contexts can enrich the precision and rapidity of diagnoses. In this research aimed at improving the accuracy of artificial intelligence AI-based ILD identification, we harnessed the benefits of deep learning, employing full-training, Transfer Learning (TL), and ensemble voting techniques. Our approach involved the construction of three Convolutional Neural Networks (CNNs) from scratch for ILD detection. Additionally, we customized models named InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50 for both full-training and TL strategies. This comprehensive methodology aimed to take benefits of DL architectures to enhance the precision of ILD identification in medical imaging. Both the first dataset consisting of HRCT images and the second dataset comprising Chest X-ray were
employed in our study. However, during the initial training phase of the TL models, we utilized pre-trained ImageNet weights. To enhance performance, modifications were made to the classification layers of all five models for both TL and full-training processes. To further improve training outcomes, a soft-voting ensemble approach was employed. The ensemble, combining the predictions of all three newly developed CNN models (ILDNetV1, ILDNetV2 and ILDNetV3), and ILDNetV1 achieved the highest test accuracy at 98.14%. Additionally, we incorporated machine learning (ML) models, including Logistic Regression, BayesNet, RandomForest, Multilayer Perceptron (MLP), and J48, using statistical measurements derived from HRCT images. Our study introduces a novel AI-based system for predicting ILD categories. This system demonstrated superior performance on unseen data by leveraging the results from the newly constructed CNNs, transfer learning, and ML models. This comprehensive approach holds promise for advancing ILD category prediction, providing a more robust and accurate tool for medical diagnosis and decision- making.
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Nidhin, Raju
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Author's Submission
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Christ(Deemed to be University)
Date
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2024-01-01
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Peter Augustin,D
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Open Access
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PDF
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English
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PhD
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<a href="http://hdl.handle.net/10603/575084" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/575084</a>
-
https://archives.christuniversity.in/files/original/9d8901a678bb4bc5f1ba50b61c199298.pdf
19f3595e202d577dccc4ec82359e8746
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Thesis
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Title
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Artificial intelligence based computational framework for identification and classification of interstitial lung diseases using HRCT images /
Subject
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Computer Science
Creator
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Nidhin Raju - 1981307
Publisher
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CHRIST (Deemed to be University)
Date
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2024-07-19
Language
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English
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PhD
-
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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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Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Subject
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Artificial Intelligence; Computer Vision; Hand Gestures; Machine Learning; Virtual Mouse; Virtual Reality; YOLO Algorithm
Description
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Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE.
Creator
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Karthick S.; Dinesh M.; Raj C J.D.; Jayapandian N.
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2023 IEEE 2nd International Conference on Data, Decision and Systems, ICDDS 2023
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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/ICDDS59137.2023.10434330" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDDS59137.2023.10434330</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186659487&doi=10.1109%2FICDDS59137.2023.10434330&partnerID=40&md5=cb9a1b136ec3213ba1e17a02f8a28c85" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186659487&doi=10.1109%2fICDDS59137.2023.10434330&partnerID=40&md5=cb9a1b136ec3213ba1e17a02f8a28c85</a>
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Restricted Access
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ISBN: 979-835031416-8
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Online
Language
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English
Type
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Conference paper
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Karthick S., Christ University, Department of CSE, Bangalore, India; Dinesh M., Christ University, Department of CSE, Bangalore, India; Raj C J.D., Christ University, Department of CSE, Bangalore, India; Jayapandian N., Department of CSE Christ University, India
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Title
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Faculty Publications
Article
Faculty Publications -Articles
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Tripathi, Mano Ashish; Komatiguntala, Dhanalakshmi; Moorthygari, Sree Lakshmi; Dadhabai, Sundari; Mishra, Amit; Bommisetti, Ravi Kumar
Title
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Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency
Date
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01-01-2025
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Journal of Machine and Computing;Volume;5;Issue;3;pp.1852-1863
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<a href="https://doi.org/10.53759/7669/jmc202505145" target="_blank" rel="noreferrer noopener">https://doi.org/10.53759/7669/jmc202505145</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010143052?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010143052?origin=resultslist</a>
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Tripathi M.A., School of Management Studies, Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh, Prayagraj, India; Komatiguntala D., School of Business and Management, Christ University, Yeswanthpur Campus, Bangalore, India; Moorthygari S.L., Department of Business Management, Mahatma Gandhi University, Telangana, Nalgonda, India; Dadhabai S., KL Business School, Koneru Lakshmaiah Educational Foundation, Andhra Pradesh, Vaddeswaram, India; Mishra A., Department of Computer Science and Applications, Dr. Vishwanath Karad, MITWPU, Maharashtra, Pune, India; Bommisetti R.K., Andhra Pradesh, India
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In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors.
Subject
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Classification Performance; Employee Attrition; Ensemble Learning; Natural Language Processing; Predictive Modelling; Sentiment Analysis; Workforce Analytics
Publisher
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AnaPub Publications
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ISSN: 27891801;
Language
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English
Type
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Article
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All Open Access; Hybrid Gold Open Access
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online
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Title
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Title
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Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Subject
The topic of the resource
bag-of-words; Continuous Bag of Words; natural language processing; Semantic Textual Similarity (STS); Word2Vec
Description
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Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE.
Creator
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Chandra J.; Santhanam A.; Joseph A.
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International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2020-01-01
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<a href="https://doi.org/10.1109/ic-ETITE47903.2020.211" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ic-ETITE47903.2020.211</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085197570&doi=10.1109%2Fic-ETITE47903.2020.211&partnerID=40&md5=fc97b9e98d458d6da14c3c764b712ced" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085197570&doi=10.1109%2fic-ETITE47903.2020.211&partnerID=40&md5=fc97b9e98d458d6da14c3c764b712ced</a>
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Restricted Access
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ISBN: 978-172814142-8
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Online
Language
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English
Type
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Conference paper
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Chandra J., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India; Santhanam A., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India; Joseph A., CHRIST Deemed to Be University, Department of Computer Science, Banglore, India
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Faculty Publications
Article
Faculty Publications -Articles
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Randhawa, Princy; Adusumilli, Sri Bhargav Krishna; Poddutoori, Jaipal Reddy; Podduturi, Mahipal; Dighe, Akshay Atul; Devesh, Sonal
Title
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Artificial Intelligence based Smart Pneumatic Tools for Industrial Applications
Date
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01-01-2025
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Engineered Science;Volume;35;Issue;;Article No.;1368;
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<a href="https://doi.org/10.30919/es1368" target="_blank" rel="noreferrer noopener">https://doi.org/10.30919/es1368</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010416222?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010416222?origin=resultslist</a>
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Randhawa P., Department of Mechatronics Engineering, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Adusumilli S.B.K., Mindquest Technology Solutions LLC, Phoenix, 85027, AZ, United States; Poddutoori J.R., BBU, 162 Bowery Lane, Downingtown, 19335, PA, United States; Podduturi M., Penske Corporation, 727 Houston St., Downingtown, 19335, PA, United States; Dighe A.A., Permobil Inc, New Berlin, 53151, WI, United States; Devesh S., School of Management, Christ University, Karnataka, Bengaluru, 560029, India
Description
An account of the resource
This study presents the development and evaluation of a next-generation pneumatic instrument integrated with intelligent technologies aimed at enhancing operational efficiency, adaptability, and sustainability in advanced manufacturing environments. The system features smart sensors, adaptive learning algorithms, and real-time adjustable control mechanisms, enabling it to function effectively under extreme operating conditions. It handles fluid velocities up to 15.2 m/s and pressures as high as 720 kPa, demonstrating robust structural integrity and reliability. Notably, the instrument maintains precise control over material deformation with an accuracy of 0.05 mm, even under mechanical stress levels reaching 180 MPa and at a Reynolds number of 350,000. The embedded smart sensors facilitate instantaneous responsiveness to fluctuations in material behavior, dynamically optimizing both force application and energy efficiency. This results in a significant 30% reduction in power consumption, with operational power decreasing from 280 W in high-pressure scenarios to just 150 W under standard conditions. Furthermore, the tool exhibits superior thermal management, maintaining operational temperatures below 65 C. Its self-calibrating functionality, driven by intelligent algorithms, ensures consistent output, minimized error margins, and enhanced safety over extended use. Compared to traditional electrically driven systems, this intelligent pneumatic tool offers a more sustainable and cost-effective solution by reducing energy demand and extending service life. The integration of advanced sensing and control systems transforms conventional pneumatic tools into adaptive, high-performance devices suitable for modern, eco-conscious manufacturing setups. This research highlights the transformative potential of intelligent pneumatic systems in driving productivity, reducing operational costs, and supporting the transition to greener, more sustainable industrial practices. The Author(s) 2025.
Subject
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Adaptive control systems; Industrial automation; Next-generation pneumatic tools; Operational efficiency; Precision engineering; Sustainable manufacturing
Publisher
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Engineered Science Publisher
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ISSN: 2576988X;
Language
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English
Type
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Article
Rights
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All Open Access; Gold Open Access
Format
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online
-
https://archives.christuniversity.in/files/original/e9df10b3fde2fc77e7fb227608beacb5.pdf
d6bb3fadd0549f58bfe6ed52613578fd
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PhD Thesis
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Title
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Artificial intelligence based steganography model for social media data set /
Subject
The topic of the resource
Computer Science
Creator
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Gurunath R - 1942062
Publisher
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CHRIST (Deemed to be University)
Date
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2023-01-03
Language
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English
Type
The nature or genre of the resource
PhD
-
https://archives.christuniversity.in/files/original/af82627277e1e263b0923affbea50b29.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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Title
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Patents
Subject
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Patent
Description
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Patent
Patent
Patents
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Title
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Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
The topic of the resource
Computer Science
Description
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Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
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Tiwari, Akhilesh.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2021-05-06
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/9aa0f90d1ccf32be66fea10c3247c876.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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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Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
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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Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
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Sharma, Durgansh.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
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Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2021-05-06
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent
-
https://archives.christuniversity.in/files/original/de61ed3b25091e98b2c02c6b4ad5678c.jpg
0b6dfeeadbd8dbf97f5aef738454f6b9
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
Patents
Subject
The topic of the resource
Patent
Description
An account of the resource
Patent
Patent
Patents
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
Artificial intelligence based system and method for management, recommendation, mapping of skill /
Subject
The topic of the resource
Computer Science
Description
An account of the resource
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702).
Creator
An entity primarily responsible for making the resource
Devassy, Viju P.
Source
A related resource from which the described resource is derived
<a href="https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view" target="_blank" title="Artificial intelligence based system and method for management, recommendation, mapping of skill /" rel="noreferrer noopener">https://drive.google.com/file/d/1qRwd-88Gj1YNb6llS1a2fOs2mo6XbE20/view</a>
Publisher
An entity responsible for making the resource available
Intellectual Property India
Date
A point or period of time associated with an event in the lifecycle of the resource
2021-05-06
Language
A language of the resource
English
Type
The nature or genre of the resource
Patent