Engineering Science Reference Deep Learning Techniques and Optimization Strategies in Big Data Analytics (Advances in Systems Analysis, Software Engineering, and High Performance)

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry. About the Author Pinar Karagoz received her PhD. degree from Middle East Technical University (METU), Computer Engineering Department, in 2003. She worked as a visiting researcher in State University of New York (SUNY) at Stony Brook. Her research interests include data mining, web usage mining, social network analysis, information extraction from the web, semantic web services, web service discovery and composition. Dr. Karagoz has authored several publications in international journals and leading conferences. Some of her papers were published in journals such as IEEE TKDE, IEEE Industrial Informatics, ACM TWEB, Information Systems Journal, SIGMOD Record, Knowledge and Information Systems and her research were presented and published in conferences including VLDB, CIKM, ASONAM, DAWAK, ICWS. In addition to nationally funded research projects, she took part in two international collaboration projects. Recently she served in the management commitee of the COST Action ENERGIC (European Network Exploring Research into Geospatial Information Crowdsourcing). 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.