Recent Trends in Artificial Intelligence and Data Sciences
Description
This book presents original, peer-reviewed research papers from the International Conference on Recent Trends in Artificial Intelligence and Data Sciences-CONFLUENCE 2025. It highlights the latest advancements across diverse areas of data science and computational techniques. The volume focuses on artificial intelligence, machine learning, deep learning, soft computing, and other emerging methodologies. By sharing recent findings in these domains, the book serves as a valuable resource for researchers, scientists, industry professionals, and students alike.
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Persons
Sumit Kumar is currently a Professor at Amity University, Noida, Uttar Pradesh, India. He earned his Ph.D. from the National Institute of Technology (NIT) Jamshedpur, Jharkhand. With over 20 years of teaching and research experience at reputed colleges and universities across India, he has made significant contributions in the fields of machine learning, image processing, metaheuristics, and soft computing. Dr. Kumar has authored and co-authored more than 40 peer-reviewed research papers. He has delivered keynote addresses at various Faculty Development Programs (FDPs) and international conferences. Additionally, he has served as a program committee member for several international conferences and as a reviewer for numerous reputed international journals.
Garima Aggarwal is a Professor in the Department of Computer Science and Engineering at Amity University, Noida. She brings over 15 years of academic and research experience in Computer Science and Engineering and currently serves as the Head of International Collaboration for Engineering & Technology at ASET. Dr. Aggarwal earned her B.Tech. in Electronics Engineering from Kurukshetra University in 2005, followed by an M.Tech. in Computer Science from Chaudhary Devi Lal University in 2007. She completed her Ph.D. in Computer Science and Engineering from Amity University in 2018. Her research interests include digital data security, steganography, cryptography, artificial intelligence, machine learning, and image processing. She has authored and co-authored over 40 peer-reviewed research papers.
Bhuvan Unhelkar is a distinguished IT professional and Professor at the Muma College of Business, University of South Florida. He also serves as an Adjunct Professor at Western Sydney University and an Honorary Professor at Amity University, India. Dr. Unhelkar is the Founding Consultant at Method Science and is recognized for his expertise in business analysis, software engineering (UML), big data strategies, AI, agile methodologies, mobile business, and green IT. His domain experience spans banking, finance, insurance, government, and telecommunications. A thought leader and prolific author, he has written 28 books. He has published extensively in peer-reviewed journals, authored Cutter executive reports, and delivered keynote speeches at international conferences. He has also supervised numerous Ph.D. and postdoctoral researchers.
Raju Pal is an Assistant Professor in the Department of Computer Science and Engineering at the University School of Information and Communication Technology, Gautam Buddha University (GBU), India. He has over 12 years of academic and research experience in artificial intelligence, machine learning, computer vision, and digital forensics. Dr. Pal also serves as a Global Professor of Practice at Golden Gate University (GGU), USA, where he supervises doctoral research in business analytics and AI applications. He earned his Ph.D. in Computer Science and Engineering from Jaypee Institute of Information Technology (JIIT), Noida, in 2019, and his M.Tech. in Computer Science and Engineering from Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, in 2012. He has been actively involved in several government-funded research projects supported by SERB, MeitY, ICSSR, and DST.
Content
A Comparative Analysis of Existing and AI-Driven Frameworks for Industrial Interaction Practices.- A Comprehensive Review: Machine and Deep Learning Techniques for Sentiment Analysis on Datasets of Political Tweets.- Comparative Analysis of Feature Selection Techniques for Malware Detection in URLs.- Google and Flower Federated Learning Frameworks Comparison in Bug Prediction in terms of Flexibility and Technical Factors.- Exploring Sentiments in Stack Overflow Score and Discussion: A Dual Approach with Machine Learning Models and Expert Evaluation.- An AI-Enhanced Framework for Mental Health Management.- dvancing Diabetic Retinopathy Detection through Collaborative Vision Transformer and CNN Architectures Integrated with Explainable AI.- Diagnosis and Prediction of Multiple Sclerosis Disease Using Quantum Machine Learning Classifiers.- Predictive Modeling for Breast Cancer Diagnosis Using Machine Learning Algorithms.- Early Detection of Cardiovascular Disease through the Utilization of Multiple Machine Learning Techniques.- Early Prediction and Detection of Liver Disease Using Deep Learning.- Predictive Analytics based on Public Health Response using Epidemiological Models: A Data Driven Approach.- Prognosis, and Diagnosis of Diabetes through Minkowski Distance Metric.- Mental Health Reaction Based On Behavioral Analysis And Digital Device Usage Using Machine Learning Algortihm.- Automated Onset Seizure Detection Using EEG Signals by Machine Learning.
