Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.
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Discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. The book features research on topics such as digital security, renewable energy, and intelligence management.
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Produktdetaljer

ISBN
9781799836452
Publisert
2020-06-30
Utgiver
Vendor
Business Science Reference
Høyde
279 mm
Bredde
216 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
491

Om bidragsyterne

J. Joshua Thomas is a senior lecturer at KDU Penang University College, Malaysia since 2008. He obtained his PhD (Intelligent Systems Techniques) in 2015 from University Sains Malaysia, Penang, and Master's degree in 1999 from Madurai Kamaraj University, India. From July to September 2005, he worked as a research assistant at the Artificial Intelligence Lab in University Sains Malaysia. From March 2008 to March 2010, he worked as a research associate at the same University. Currently, he is working with Machine Learning, Big Data, Data Analytics, Deep Learning, specially targeting on Convolutional Neural Networks (CNN) and Bi-directional Recurrent Neural Networks (RNN) for image tagging with embedded natural language processing, End to end steering learning systems and GAN. His work involves experimental research with software prototypes and mathematical modelling and design He is an editorial board member for the Journal of Energy Optimization and Engineering (IJEOE), and invited guest editor for Journal of Visual Languages Communication (JVLC-Elsevier). He has published more than 30 papers in leading international conference proceedings and peer reviewed journals. Dr. Pandian Vasant is a senior lecturer at Department of Fundamental and Applied Sciences, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS in Malaysia. He holds PhD (UNEM, Costa Rica) in Computational Intelligence, MSc (UMS, Malaysia, Engineering Mathematics) and BSc (2nd Class Upper- Hons, UM, Malaysia) in Mathematics. His research interests include Soft Computing, Hybrid Optimization, Holistic Optimization, Innovative Computing and Applications. He has co-authored research papers and articles in national journals, international journals, conference proceedings, conference paper presentation, and special issues lead guest editor, lead guest editor for book chapters' project, conference abstracts, edited books , keynote lecture and book chapters (175 publications indexed in SCOPUS). In the year 2009, Dr. Pandian Vasant was awarded top reviewer for the journal Applied Soft Computing (Elsevier) and awarded outstanding reviewer in the year 2015 for ASOC (Elsevier) journal. He has 26 years of working experience at the various universities from 1989-2017. Currently he is Editor-in-Chief of IJCO, IJSIEC, IEM, IJEOE and Editor of GJTO. H-Index SCOPUS Citations = 36, H-Index Google Scholar = 26, i-10 index = 76