Computational Optimization Techniques for Artificial Intelligence Enabled Environments

Erscheinungsjahr 2025
ISBN: 978-981-9226-75-7
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications

This book introduces cutting-edge computational optimization techniques designed especially for AI enabled systems. These techniques inculcate advanced algorithms with real world applications to enhance efficiency and accuracy. Its focus on the hybrid and ensemble models makes it stand out, presenting novel and creative solutions for the complex problems. Through case studies and real-world applications the book not only highlights the theoretical aspects of the optimization techniques but also provides a link between theory and practice. With the help of clear explanations and practical examples the book empowers the readers to delve into the complex problems and providing optimized solutions leading to enhancing the performance of AI enabled systems. 

The book explores the advanced computational optimization techniques covering wide range of topics like nature-inspired algorithms, metaheuristics and hybrid optimization methods. This book caters the need of AI researchers, data scientists and machine learning engineers who aim to optimize their models and algorithms. It will be a valuable resource for the academicians and students studying AI and its related subfield. Professionals in the field of finance, healthcare, agriculture, education where AI applications are prevalent, will benefit from the practical insights provided. Overall, it caters to anyone interested in enhancing AI systems through sophisticated optimization strategies.

Zielgruppe

Research

Autoren/Hrsg.

Srivastava, Shilpa

Arora, Nidhi

Pant, Millie

Nagar, Atulya

Tripathi, Aprna

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung

Inhaltsverzeichnis

Computational Optimization in AI Driven Machine Tool Environments Enhancing Precision Efficiency and Sustainability.- Efficient Sampling Techniques for AI Driven Optimization.- Deep Learning and Electronic Health Records Convergent Approach to Optimize Healthcare Data Systems.- Investigating the Components of Clinical Decision Support System for Ward Allocation using AI.- Comparative Analysis of Machine Learning Models with Hybrid Model for Enhanced Cyclone Prediction.- A Multi-Objective Relief Logistic Plan with Uncertain Demand Estimation for Assam Flood.- Applying Xai To Diverse Financial Ecosystems With A Focus On Localized Explainability.- A Novel Optimization Technique for Dead Mileage Allocation Problem.

Über Autor(innen)

Shilpa Srivastava is Associate Professor and Chairperson of the committee 'AI Policy recommendations' at Christ University Delhi NCR, Ghaziabad, India. Her areas of interest include application of soft computing in medical domain, theory of computation, algorithms, and e-health services. She has contributed numerous peer-reviewed articles to reputed international journals, authored several book chapters, and presented her research work at various international conferences. She has been curriculum development in-charge, on the board of programme committees of reputed international conferences and acted as a reviewer to review articles in many Springer and IEEE journals.

Nidhi Arora is Professor at Department of Computer Science, University of Delhi, India.  Her primary research areas are social networks, nature inspired computing and machine learning. She has published many peer reviewed research articles in international journals of repute, book chapters, and has also presented many research papers in various international conferences. She has delivered many talks on latest research topics such as data science, e-content development and deep learning to name a few in various FDPs and workshops. 

Millie Pant is Professor in the Department of Applied Mathematics and Scientific Computing (DAMSC), Saharanpur Campus of Indian Institute of Technology (IIT) Roorkee, joint faculty at the Mehta Family School of Data Science and Artificial Intelligence, IIT Roorkee, and adjunct faculty at AIT Thailand, Bangkok. Her expertise is in optimization algorithms, soft computing techniques, image processing, and decision-making processes. She has more than 8000 Google Scholar citations, and her H-index is 44. She has completed 4 bilateral sponsored projects with Germany and Russia, UK and Czech Republic and two national projects sponsored by DST and DRDO. She has conducted several short-term courses sponsored by Deloitte, TSW, DST, and QIP in the areas of optimization, evolutionary algorithms, and artificial intelligence.

Atulya Nagar holds the Foundation Chair as Professor of Mathematics at Liverpool Hope University where he is the Pro-Vice-Chancellor for Research since October 2019. A mathematician by training, he possesses multi-disciplinary expertise in nonlinear mathematics, natural computing, bio-mathematics and computational biology, operations research, and control systems engineering. Professor Nagar has authored over 550 publications, edited volumes on applied mathematics and AI, and co-edits two Springer book series. He serves on editorial boards and has been Sir CV Raman Chair at the University of Madras.

Aprna Tripathi is Associate Professor in the department of data science and engineering, Manipal University Jaipur, India. With over 15 years of teaching and research experience, her scholarly contributions can be found in prestigious national and international journals and conferences, including those recognized by SCI and Scopus. Her areas of specialization include software engineering, software testing, data visualization, and data structures & algorithms.