Data Management, Analytics and Innovation - Proceedings of ICDMAI 2025, Volume 2

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 9th International Conference on Data Management, Analytics and Innovation (ICDMAI 2025), held during 17-19 January 2025 at St. Xavier's College (Autonomous), Kolkata, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into three volumes.

List of contents

Chapter 1 :"A Bipartite Graph Approach to Linking Selection Marks and Text".- Chapter 2 :"Enhancing Food Demand Forecasting to Minimize Food Waste in Supply Chains: A Hybrid ARIMA-DQN Framework".- Chapter 3 :"Global Conversations on Climate Change: Insights from Social Media Analysis of GHGs, Climate Action and Public Perception".- Chapter 4 :"An Inventive Feature Importance based Dementia PredictiveModel using SHAP Analysis".- Chapter 5 :"Quantum Convolutional Neural Networks for Enhanced Crop Disease Detection and Management: A Step Towards Intelligent Farming Systems".- Chapter 6 :"Towards Classifying Bird Sounds Using a Deep Transfer Learning Model".- Chapter 7 :"Multi-Objective Food Delivery Route Optimization using Hybrid Pareto based Differential Evolution Algorithm".- Chapter 8 :"Energy Efficient XceptionNet Accelerator with e-FPGAs for AI based Risk Prediction of Skin Abnormalities".- Chapter 9 :"A clustering based prediction approach for new cases of COVID-19 in India".- Chapter 10 :"Efficiency-Optimized Non-linear Transformers for Fake News Detection: A Case Study".- Chapter 11 :"Isolation Forest based Anomaly Detection of Vessels using AIS Data".- Chapter 12 :Detecting Bias in Social Media using SentiWordNet .- Chapter 13 :"A LLM and Explainable AI Assistive Model for Improvement ofUser s Query in a Chatbot for Women Cancer Awareness for Indian Lay People".- Chapter 14 :"Amalgamated Business and Technology Operations Observability Framework".- Chapter 15 :"Enhanced Tabular Convolution (TAC) Method for RNA-Seq GeneExpression Classification".- Chapter 16 :"Solutions to Higher Order Differential Equations by usingIntegrating Factor techniques, Numerical Methods and Neural Networks".- Chapter 17 :"Explainable and Interpretable Isolation Forest for Bankingand Finance".- Chapter 18 :"Water Withdrawal Trends Across Multiple UN Member Nationsusing Time Series Forecasting".- Chapter 19 :"EGA: Explainable Deep Belief Network based Auto encoderusing novel Extended Garson Algorithm".- Chapter 20 :Classification of Geophysical Events in the Getchell Mine"Integrating Product Coefficients for Improved 3D LiDAR Data Classification".- Chapter 21 :"Deep learning model for optimizing time-delay and forecasting ""Discrimination of earthquakes and mining explosion seismicsignals using partitioned local depth".- Chapter 22 :"Crime Dataset Analysis to Optimize the Requirement of Copsusing Clustering Approaches".- Chapter 23 :"A Study of Genome Compression Algorithms for Industrial versus Scientific Applications Focusing Sequences in Raw and FASTA/Q Formats".

About the author

Dr. Saptarsi Goswami has 22+ years of experience in industry and academics. He holds a B.E. in ECE from NIT Jaipur, a diploma in business finance from ICFAI, and an M.Tech and Ph.D. from the University of Calcutta. He worked for 10+ years in IT at Tata Infotech Ltd, PwC, and Cognizant Technology Solutions. Currently, he is an Assistant Professor & Head of Computer Science at Bangabasi Morning College (University of Calcutta). He was a Visiting Scientist at Iwate Prefectural University, Japan, and is a founding member of the Data Science Lab at the University of Calcutta, serving as a lead researcher and research supervisor. Dr. Goswami has an H-Index of 20 and 2,000+ citations on Google Scholar. He leads the ODSC and Azure Community in Kolkata, is an executive member of the Society for Data Science (S4DS), and has chaired multiple editions of ICDMAI. His Machine Learning lectures are featured in the National Digital Library of India (NDLi). He has contributed to state-level programs for the Government of West Bengal and authored “Deep Learning” and “AI for Everyone” (Pearson India).
Dr. Sajal Saha has 20+ years of academic experience and is Professor & Head of Computer Science and Engineering and Director of Product & Innovation at Adamas University. He earned his Ph.D. from NIT Durgapur in 2016, focusing on QoS and Mobility Management in WiMAX and Next-Gen Networks. He began his career at ISRO in 2004 as a Project Trainee in GIS and Remote Sensing. He holds certifications from IBM, AWS Academy, and Dale Carnegie and serves as a Mentor of Change for Atal Innovation Mission, Coordinator for IIRS ISRO Outreach Program, and research liaison with Emory University and the Asian Institute of Technology. Dr. Saha has led TEQIP, NBA, NAAC, and NIRF assessments and facilitated cloud computing curriculum and plagiarism detection tools. He is an active member of IEEE, ACM, and ISoC-Internet Society, contributing to global research and networking.
Prof. Kanadpriya Basu is Professor of the Practice in Data Science & Operations at the Marshall School of Business, University of Southern California (USC), and Co-Founder at ArrowsUp.. Previously, he was a Professor of the Practice at Thunderbird School of Global Management (ASU), specializing in Data Science & Leadership Development. He has led 4IR initiatives aimed at reaching 100 million learners and was Director of the Global Business Analytics Executive Program. With 20+ years in AI and data science, he has built high-performing teams at MFour Research, SnackNation, Covisus, and Medallia, contributing to cutting-edge AI innovations. He holds multiple patents and has published papers in geophysics and AI. Kanad has taught at Occidental College and the University of Texas. He holds a Ph.D. in Applied & Computational Mathematics and is an established thought leader in AI, frequently speaking at global conferences.
Dr. Romit S Beed is an Associate Professor and Head of the Postgraduate and Research Department of Computer Science with 20+ years of teaching experience. He is known for his innovative teaching methods and commitment to academic and student development. His expertise includes Multi-Objective Optimization, Computational Intelligence, DBMS, Software Engineering, and IoT. Actively engaged in research and consultancy, he contributes to mentorship, academic leadership, and co-curricular activities. His work fosters a culture of learning and innovation at St. Xavier’s College (Autonomous), Kolkata.
 

Summary

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 9th International Conference on Data Management, Analytics and Innovation (ICDMAI 2025), held during 17–19 January 2025 at St. Xavier’s College (Autonomous), Kolkata, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into three volumes.

Product details

• Assisted by: Kanadpriya Basu (Editor), Kanadpriya Basu et al (Editor), Romit S Beed (Editor), Saptarsi Goswami (Editor), Sajal Saha (Editor)
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Release: 19.12.2025
• EAN: 9789819658596
• ISBN: 978-981-9658-59-6
• No. of pages: 408
• Illustrations: X, 408 p. 159 illus., 130 illus. in color.
• Series: Lecture Notes in Networks and Systems
• Subjects: Natural sciences, medicine, IT, technology > Technology > General, dictionaries; Big Data, Datenbanken, Supervised Learning, Unsupervised Learning, Feature Engineering, Reinforcement Learning, Data Engineering, Computational Intelligence, semi-supervised learning, recommendation system, Algorithms and Models, Fuzzy and Rough Set, Association Rule Mining, ICDMAI 2025 Proceedings