We have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.
Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.
The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.
The book will offer valuable insights to researchers and scientists from academia and industry in ICTs, IT and computer science, data science, AI and machine learning, swarm intelligence and complex systems. It is also a useful resource for professionals in related fields, and for advanced students with an interest in optimization and IoT applications.
Show moreWe have witnessed an explosion of research activity around nature-inspired computing and bio-inspired optimization techniques, which can provide powerful tools for solving learning problems and data analysis in very large data sets. To design and implement optimization algorithms, several methods are used that bring superior performance. However, in some applications, the search space increases exponentially with the problem size. To overcome these limitations and to solve efficiently large scale combinatorial and highly nonlinear optimization problems, more flexible and adaptable algorithms are necessary.
Nature-inspired computing is oriented towards the application of outstanding information-processing aptitudes of the natural realm to the computational domain. The discipline of nature-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization. Metaheuristic search algorithms with population-based frameworks are capable of handling optimization in high-dimensional real-world problems for several domains including imaging, IoT, smart manufacturing, and healthcare. The integration of intelligence with smart technology enhances accuracy and efficiency. Smart devices and systems are revolutionizing the world by linking innovative thinking with innovative action and innovative implementation.
The aim of this edited book is to review the intertwining disciplines of nature-inspired computing and bio-inspired soft-computing (BISC) and their applications to real world challenges. The contributors cover the interaction between metaheuristics, such as evolutionary algorithms and swarm intelligence, with complex systems. They explain how to better handle different kinds of uncertainties in real-life problems using state-of-art of machine learning algorithms. They also explore future research perspectives to bridge the gap between theory and real-life day-to-day challenges for diverse domains of engineering.
The book will offer valuable insights to researchers and scientists from academia and industry in ICTs, IT and computer science, data science, AI and machine learning, swarm intelligence and complex systems. It is also a useful resource for professionals in related fields, and for advanced students with an interest in optimization and IoT applications.
Show moreRajeev Arya is an assistant professor in the Department of
Electronics and Communication Engineering at the National Institute
of Technology, Patna, India. His research interests cover the
fields of wireless communication and security issues,
device-to-device communication based IoT System, UAV communications
in 5G and beyond networks, soft computing techniques and
applications. He has published in international journals and
conferences. He is a member of IEI, IEEE ISRD-, and IAENG. He
received his Ph.D. degree in Communication Engineering from the
Indian Institute of Technology (IIT Roorkee), India.
Sangeeta Singh is an assistant professor in the Department of
Electronics and Communication Engineering at the National Institute
of Technology, Patna, India. Her research interests include soft
computing techniques and applications and beyond CMOS Devices Green
Electronics steep switching transistors. She has actively
participated in technical courses, workshops, and seminars at the
NITs. She is a member of IEEE and IEEE EDS Society. She received
her Ph.D. degree in Electronics and Communication Engineering from
PDPM-IIITDM Jabalpur, India.
Maheshwari P. Singh is a professor in the Department of Computer
Science and Engineering at NIT Patna, India. He has written and
edited several research books. His research interests include
machine learning and wireless sensor networks, fuzzy set, social
media and security. He has actively participated in technical
courses, workshops and seminars at the IITs and NITs. He is a
member of IE (Fellow), ACM (Senior), IEEE (Senior), and ISTE (Life
Member). He received his Ph.D. degree in Computer Science and
Engineering from MNNIT Allahabad, India.
Brijesh Iyer holds a Ph.D. degree in Electronics and
Telecommunication Engineering from Indian Institute of Technology,
Roorkee, India. He is presently a senior faculty member in the
Department of E&TC Engineering, Dr Babasaheb Ambedkar
Technological University, India (a state technological University
of Maharashtra-India). His research interests include RF front end
design for 5G and beyond, IoT, biomedical imaging and signal
processing. He has two patents to his credit, has authored five
books on cutting edge technologies, and published over 40 research
papers in peer-reviewed journals and conference proceedings. He has
served as a program committee member of various international
conferences. He is a member of IEEE MTTS, ISTE, IEANG, and
IETE.
Venkat N. Gudivada is the chairperson of and a professor in the
Computer Science Department at East Carolina University, USA. His
research has been funded by NSF, NASA, U.S. Department of Energy,
US Department of Navy, US Army Research Office, Marshall University
Foundation, and West Virginia Division of Science and Research. His
research interests include cognitive computing, computational
linguistics/NLP, information retrieval, automated question
generation, data management and NoSQL systems, and personalization
of learning. He has published over 110 articles in peer reviewed
journals, book chapters, and conference proceedings. He is an IEEE
Senior Member, and member of IARIA) and the Honor Society of Phi
Kappa Phi. He received his PhD degree in Computer Science from the
University of Louisiana - Lafayette, Louisiana, The USA.
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