This book contains a collection of 24 chapters emerging from the International Conference on Computations and Data Science (CoDS 2024), held at the Indian Institute of Technology (IIT) Roorkee, India, from 8–10 March 2024. It discusses recent advances in computational algorithms and numerical mathematics, applied optimization, machine learning and data-driven scientific computing, offering a balanced blend of theoretical foundations and practical and engineering applications. This book includes chapters on multi-objective optimization strategies, equilibrium problems with complex constraints, fuzzy and interval-based decision models and fuzzy finite element methods. Additional chapters address graph-theoretic studies, computational fluid dynamics, heat transfer and uncertainty quantification in porous media.
The chapters span a wide range of themes. Chapters in the book address finite element techniques for mechanical fatigue, numerical solutions of time-fractional Black–Scholes equations, B-spline collocation methods for nonlinear problems and wavelet-based approaches for fractional differential equations. It also demonstrates the fusion of classical numerical analysis with artificial intelligence, including physics-informed neural networks for engineering predictions. This book explores applications such as stock price forecasting, biomedical prediction models and e-commerce analytics. Advances in deep learning are highlighted through transformer-based video summarization, LiDAR–RGB sensor fusion for object detection, medical image classification, EEG artifact removal using generative adversarial networks and speech recognition systems for low-resource languages.

Cornelis Vuik is a professor of Numerical Analysis at the Delft Institute of Applied Mathematics (DIAM), TU Delft. He earned his M.Sc. in Applied Mathematics from TU Delft (1982) and Ph.D. in Mathematics from Utrecht University (1988). After a brief stint at Philips Research, he joined TU Delft in 1988, rising from an assistant professor to associate (2000) and a full professor (2007). He has held major leadership roles as Scientific Director of DCSE, DHPC, and 4TU.AMI. His research spans numerical linear algebra, iterative solvers, domain decomposition, Krylov methods, multigrid and multilevel preconditioning, with applications in fluid dynamics, porous media, semiconductors, and Helmholtz/Maxwell equations. Author of 240+ papers, he has delivered numerous invited lectures worldwide, supervised many Ph.D. scholars, and fostered international collaborations. His contributions have been honored with the Officer in the Order of Orange-Nassau and TU Delft’s Professor of Excellence Award for outstanding teaching and mentorship.

Suresh Chandra is a formerly professor in the Department of Mathematics, IIT Delhi. He is a Fellow of the Operational Research Society of India, and a member of the International Working Group on Generalized Convexity and Applications. He earned his Ph.D. from the IIT Kanpur in 1970. His research interests include numerical optimization, mathematical programming, generalized convexity, fuzzy optimization and fuzzy matrix games, machine learning and financial mathematics. He has authored and co-authored over 200 publications in reputed journals with more than 7000 citations. He has also co-authored some of the widely acclaimed books entitled Numerical Optimization with Applications, Principles of Optimization Theory, Financial Mathematics: An Introduction, Twin Support Vector Machines: Models, Extensions and Applications, Fuzzy Mathematical Programming and Fuzzy Matrix Games, Fuzzy Portfolio Optimization and Fuzzy Sets and Applications: Logic Modelling and Decision Making. He is on the Editorial Board of the journal OPSEARCH.

Sanjeev Kumar is a professor in the Department of Mathematics at IIT Roorkee since 2010 and heads the Mehta Family School of Data Science and Artificial Intelligence. He earned his Ph.D. from IIT Roorkee in 2008 and pursued postdoctoral research at the University of Udine, Italy. His work focuses on computational methods for inverse problems, image processing, and machine learning, with extensive publications in leading journals and conferences. He has directed sponsored projects supported by DST, SERB, MeitY, ISRO, DRDO, and industry partners, supervised 12 Ph.D. scholars and 30+ master’s theses, and currently leads a vertical developing India’s foundational AI model under the IndiaAI Mission. He has also contributed to NPTEL online courses initiated by the Ministry of Education, Government of India.

Shiv Kumar Gupta is a professor in the Department of Mathematics at IIT Roorkee. He earned his Ph.D. from IIT Roorkee in 2008, began his career as an Assistant professor at IIT Patna (2008–2012), and joined IIT Roorkee in 2012. His research focuses on optimization theory, non-smooth optimization, support vector machines, multicriteria decision-making, and optimization under uncertainty. With over 70 papers in reputed international journals, he has supervised 11 Ph.D. theses. His teaching excellence has been recognized with the Best Teacher Award at IIT Patna, the Outstanding Teacher Award, and the Prof. Balakrishna Outstanding Teacher Award at IIT Roorkee. He is an active reviewer for leading journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems and Applied Soft Computing.