Artificial Intelligence in Industry 4.0 and 5G Technology
Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors
As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise.
Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (Io T).
* Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks.
* Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor.
* Provides guidance on implementing metaheuristics in different applications and hybrid technological systems.
* Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement.
Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches.
About the author
PANDIAN VASANT is Research Associate at MERLIN Research Centre, TDTU, HCMC, Vietnam, and Editor in Chief of International Journal of Energy Optimization and Engineering (IJEOE). He holds Ph D in Computational Intelligence (UNEM, Costa Rica), MSc (University Malaysia Sabah, Malaysia, Engineering Mathematics) and BSc (Hons, Second Class Upper) in Mathematics (University of Malaya, Malaysia). He has co-authored research articles in journals, conference proceedings, presentations, special issues Guest Editor, chapters and General Chair of EAI International Conference on Computer Science and Engineering in Penang, Malaysia (2016) and Bangkok, Thailand (2018).
ELIAS MUNAPO, Ph D, currently heads the Department of Business Statistics and Operations research at North West University-Mafikeng, South Africa. He has published 50+ articles and contributed to five chapters on industrial engineering and management texts.
J. JOSHUA THOMAS is an Associate Professor at UOW Malaysia KDU Penang University College. He obtained his Ph D (Intelligent Systems Techniques) from University Sains Malaysia, Penang and master’s degree from Madurai Kamaraj University, India. He is working with Deep Learning algorithms, specially targeting on Graph Convolutional Neural Networks (GCNN) and Bi-directional Recurrent Neural Networks (RNN) for drug target interaction and image tagging with embedded natural language processing. His work involves experimental research with software prototypes and mathematical modelling and design.
GERHARD-WILLIAM WEBER, Ph D, is Professor and Chair of Marketing and Economic Engineering at Poznan University of Technology, Poland. He is also an Adjunct Professor at Department of Industrial and Systems Engineering, College of Engineering at Istinye University, Istanbul, Turkey.
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