Gebundenes Buch

Uncertainty, Optimization and Machine Learning with Applications

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This book is a collection of select chapters on topics related to uncertainty, optimization techniques and machine learning applications, emphasizing applications that directly address topics related to five SDG goals:
SDG 3 (Good Health and Well-Being)
SDG 7 (Affordable and Clean Energy)
SDG 9 (Industry, Innovation and Infrastructure)
SDG 12 (Responsible Consumption and Production)
SDG 13 (Climate Action)
The book studies the intricate landscape of these converging SDG fields. It is designed to facilitate a deeper understanding of the complexities involved in decision-making, risk management and predictive analysis in uncertain environments. The book serves as a comprehensive reference for researchers, practitioners and students, providing tools and insights to address real-world challenges through cutting-edge approaches in uncertainty, optimization and machine learning. Contents of the book are divided into seven parts:
Part 1: Advanced Mathematical Techniques. It offers foundational methods, including solidarity network allocation rules and applications of hidden Markov models in genomic analysis, setting the groundwork for advanced applications in diverse industries.
Part 2: Risk Assessment and Decision-Making Techniques. It addresses occupational risks, consumer behaviour in green product purchasing and optimal strategies in high-risk industries through refined decision-making models.
Part 3: Financial Analysis and Predictive Modelling. It helps readers explore anomaly detection in cryptocurrency markets and portfolio selection using genetic algorithms, as well as advanced probabilistic models that enhance predictive accuracy for financial and healthcare applications.
Part 4: Portfolio Optimization. It discusses the Markowitz portfolio optimization with beta weighting based on market trends. Evolutionary computation for portfolio optimization for Indian market and a comparison of meta-heuristic techniques for portfolio optimization has also been presented.
Part 5: Optimization and Inventory Management. It focuses on innovative inventory policies that account for factors like deteriorating demand and carbon emissions, incorporating fuzzy group decision-making to address sustainability.
Part 6: Renewable Energy and Environmental Management. It underscores sustainable practices through multi-attribute decision-making frameworks, examining the impact of government subsidies on green supply chains and optimizing renewable energy projects.
Part 7: Artificial Intelligence and Machine Learning Applications. It showcases AI s transformative impact, from algorithmic trading to advancements in healthcare and construction. Topics like deep learning in medical imaging and AI-driven portfolio optimization highlight the growing potential of AI across domains.

Weniger Details

Ranjan Kumar Jana is Associate Professor of Mathematics at the Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, Gujarat, India, and Associate Dean (Academic). An M.Sc. and Ph.D. degrees in Mathematics, he later completed a post-doctoral fellowship under Prof. Bruce C. Berndt at the University of Illinois at Urbana-Champaign, USA. He fields of interest are special functions, integral transforms, operations research and data assimilation, with additional interests in fractional calculus, numerical weather prediction and Ramanujan’s mathematics. With over 18 years of teaching experience and 20 years of research experience, he has published 75 research papers, including 54 journal articles, 8 book chapters and 4 books. He has completed six funded research projects totaling INR 130 lakhs, supported by SERB, CSIR, ISRO, DST and SVNIT. He has successfully supervised one postdoctoral researcher and 18 Ph.D. scholars. He has also supervised 34 master’s dissertations, 15 summer internships and several other student projects. An active contributor to academic conferences, he has presented at 72 events worldwide and regularly reviews for scientific journals in his fields of expertise. Laxminarayan Sahoo is Associate Professor of Computer and Information Science, Raiganj University, Raiganj, West Bengal, India. He earned his M.Sc. from Vidyasagar University, West Bengal, India, and Ph.D. from the University of Burdwan, West Bengal, India. He has received the MHRD Fellowship from the Government of India during his M.Tech. course at IIT-ISM Dhanbad, Jharkhand, India, and received Prof. M.N. Gopalan Award for the Best Ph.D. thesis on operations research from the Operational Research Society of India (ORSI). With more than 80 research articles published in his name, he has guided eight research scholars for their Ph.D. degree. He has also completed one UGC minor research project. He is a reviewer of several international journals and an academic editor of the Mathematical Problems in Engineering journal. Nabendu Sen is Associate Professor of Mathematics in Assam University, Silchar, India. He did his M.Sc. and Ph.D. from Assam University, under the supervision of Prof. Tanmoy Som in the Department of Applied Mathematics, IIT-BHU, Varanasi, Uttar Pradesh, India. Prior to joining Assam University, he served as Lecturer in Mathematics in S.D Jain Girls College, Dimapur, Nagaland, India. He also taught Mathematics in the School of Technology, Assam University, Silchar. Earlier, he was Visiting Lecturer in the Department of Computer science, Assam University. With 44 research papers and 2 book chapters, he has supervised 6 students for their Ph.D. degree and 1 student for M.Phil. In addition, he has done some research works in collaboration with professors from the University of Delhi, the University of Burdwan and the University of Maryland.