Machine Learning and Optimization for Engineering Design (Engineering Optimization: Methods and Applications) - Tapa dura

Acerca del autor

Apoorva S Shastri holds a Ph.D. in Optimization Algorithms and Applications from Symbiosis International (Deemed University), a Master of Technology (M. Tech.) in VLSI Design, and a Bachelor of Engineering in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also earned a Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at the Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is a research assistant professor at the Institute of Artificial Intelligence at MITWPU, Pune, India. Her research interests include optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete, combinatorial optimization, complex systems, manufacturing, and self-organizing systems. She developed socio-inspired optimization methodologies such as Multi-Cohort Intelligence Algorithm and Expectation Algorithm. She has published several research papers in peer-reviewed journals, chapters, and conferences along withone Springer authored book. 

Kailash Shaw is an associate professor in the Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, India, and also working with the Department of Artificial Intelligence and Machine Learning (AIML). Administratively, he is a member of the Board of Studies (AIML). His research and teaching interests are in the domain of data science, programming skills, optimization engineering, bio-inspired algorithms, and ICT-based education. In terms of teaching, he is the recipient of the Visweswarya Prativa Puraskar in recognition of his outstanding performance in research. He supervised over fifteen master’s dissertations. In total, he has authored over 50 peer-reviewed articles and edited two research books proceeding. He has served as a convener and organized two international conferences whose proceedings are indexed in Springer. He has also served as the track editor and the session chair for many international conferences.

Mangal Singh is working as an associate professor in Electronics and telecommunication Engineering at Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune. He has an experience of more than 21 years in the field of teaching, research, and administration. He obtained his graduation in Electronics and Telecommunication Engineering from the National Institute of Technology (formally known as GEC), Raipur, Chhattisgarh, and his M. Tech. in Communication Engineering at Jadavpur University, Kolkata, West Bengal. He obtained his Ph.D. in Communication Engineering from the National Institute of Technology, Rourkela, Odisha. He has served as an associate professor of electronics and communication Engineering at the Institute of Technology, Nirma University, Ahmedabad, and an associate professor of electronics and communication Engineering at Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh. He has 3 Indian patents published in his credit. He has guided several PG dissertations. He is a senior member of IEEE and a life member of the IETE and ISTE, India.

De la contraportada

This book aims to provide a collection of state-of-the-art scientific and technical research papers related to machine learning-based algorithms in the field of optimization and engineering design. The theoretical and practical development for numerous engineering applications such as smart homes, ICT-based irrigation systems, academic success prediction, future agro-industry for crop production, disease classification in plants, dental problems and solutions, loan eligibility processing, etc., and their implementation with several case studies and literature reviews are included as self-contained chapters. Additionally, the book intends to highlight the importance of study and effectiveness in addressing the time and space complexity of problems and enhancing accuracy, analysis, and validations for different practical applications by acknowledging the state-of-the-art literature survey. The book targets a larger audience by exploring multidisciplinary research directions such as computer vision, machine learning, artificial intelligence, modified/newly developed machine learning algorithms, etc., to enhance engineering design applications for society. State-of-the-art research work with illustrations and exercises along with pseudo-code has been provided here.