Synopsis
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.
À propos des auteurs
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.
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).
B. Bazeer Ahamed received a Bachelor of Technology in Vel Tech Engineering College, Affiliated to Anna University, Chennai, India and Master of Computer Science Engineering in Anna University of Technology, Tiruchirapalli, India Ph.D From Sathyabama Institute of Technology and Science, Chennai India. He has published more than 20 peer reviewed international journals and participated in several high profile conferences. At present he is working as Associate Professor in the department of Computer Science and Engineering, Balaji Institute of Technology and Science, Warangal, India. Prof.Bazeer research is mainly focused on Data Mining &Information retrieval; additionally his research includes Networks, Data bases, Big Data. He is a Member of IEEE, ISTE, IAENG, and CSTA. He chaired the several sessions at National and International Conferences.
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