Advances in Data-Driven Computing and Intelligent Systems: Selected Papers from ADCIS 2024, Volume 3

by Jagdish Chand Bansal (Editor), Snehanshu Saha (Editor), Carlos A. Coello Coello (Editor), Hemant Rathore (Editor)

Overview

This book is a collection of best-selected research papers presented at the International Conference on Advances in Data-driven Computing and Intelligent Systems (ADCIS 2024) held at BITS Pilani, K K Birla Goa Campus, Goa, India, during September 20–21, 2024. It includes state-of-the-art research work in the cutting-edge technologies in the field of data science and intelligent systems. The book presents data-driven computing; it is a new field of computational analysis which uses provided data to directly produce predictive outcomes. The book is useful for academicians, research scholars, and industry persons.

Product Details

ISBN-13:	9789819653706
Publisher:	Springer-Verlag New York, LLC
Publication date:	09/26/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	570
File size:	84 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Jagdish Chand Bansal is Associate Professor at South Asian University New Delhi and Visiting Faculty at Maths and Computer Science, Liverpool Hope University UK. Dr. Bansal has obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi, he has worked as Assistant Professor at ABV-Indian Institute of Information Technology and Management Gwalior and BITS Pilani. His primary area of interest is Swarm Intelligence and Nature Inspired Optimization Techniques. Recently, he proposed a fission-fusion social structure-based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems from engineering domain. He has published more than 70 research papers in various international journals/conferences.

 

Snehanshu Saha holds Master’s Degree in Mathematical and Computational Sciences at Clemson University, USA, and Ph.D. from the Department of Applied Mathematics at the University of Texas at Arlington in 2008. He was the recipient of the prestigious Dean's Fellowship during Ph.D. and Summa Cum Laude for being in the top of the class. After working briefly at his Alma matter, Snehanshu moved to the University of Texas El Paso as a regular full-time faculty in the Department of Mathematical Sciences, University of Texas El Paso. Currently, He is  Professor of Computer Science and Engineering at PES University since 2011 and heads the Center for AstroInformatis, Modeling and Simulation. He is also Visiting Professor at the department of Statistics, University of Georgia, USA and BTS Pilani, India.

 

Carlos A. Coello Coello (Fellow, IEEE) received the Ph.D. degree in computer science from Tulane University, New Orleans, LA, USA, in 1996. He is currently Professor with Distinction (CINVESTAV-3F Researcher), Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico. He has authored and co-authored over 500 technical papers and book chapters. He has also co-authored the book Evolutionary Algorithms for Solving Multiobjective Problems (2nd ed., Springer, 2007) and has edited 3 more books with publishers such as World Scientific and Springer. His publications currently report over 60000 citations in Google Scholar (his H-index is 96). His major research interests are evolutionary multiobjective optimization and constraint-handling techniques for evolutionary algorithms.

 

Hemant Rathore has completed his Bachelor of Engineering in computer science from RGTU, followed by Master of Engineering (M.E) in software systems and Doctor of Philosophy (Ph.D.) from BITS Pilani. He is currently working as Assistant Professor in the Department of CS & IS, BITS Pilani, K K Birla Goa Campus, India. He has strong academic and industry research experience in the field of data science. His current research interests are in the area of machine learning and deep learning, specifically adversarial robustness, explainability, and interpretability. He has published many research papers in various top journals. He has taught and likes to teach courses like Machine Learning, Advanced Data Mining, Data Mining, and Network Security to undergraduate and postgraduate students. He has also guided numerous students for short-term various projects.

Table of Contents

Prediction of Employee Attirition using Machine Learning Algorithms.- ADGen  A Multimodal Advertisement Generation Dataset and Video Captioning Framework.- Dynamic Tuning of Hyperparameters for Optimization of VGG 16 Model using Stripe Wise Pruning.- Prediction of fire resistance of reinforced concrete columns subjected to axial force.- Chili Leaves Disease Detection using Thermal Images and Deep Learning Classification.