Computations and Data Science

Beschreibung

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.

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.

Autor*in

Cornelis Vuik

Themen in »Computations and Data Science«

numerical analysis optimisation machine learning data science computational algorithm teaching-learning process linear alkanes graph labelings blood pressure prediction Black Scholes equations deep learning static structural problem fuzzy finite element method field variables computational cardiology

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