Advances in Applications of Data-Driven Computing (Advances in Intelligent Systems and Computing, Band 1319) - Softcover

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Dr. Jagdish Chand Bansal is Associate Professor at South Asian University New Delhi and Visiting Faculty at Maths and Computer Science, Liverpool Hope University UK. Dr. Bansal has obtained his Ph.D. in Mathematics from IIT Roorkee. Before joining SAU New Delhi, he has worked as Assistant Professor at ABV-Indian Institute of Information Technology and Management Gwalior and BITS Pilani, India. His primary area of interest is swarm intelligence and nature-inspired optimization techniques. Recently, he proposed a fission–fusion social structure-based optimization algorithm, Spider Monkey Optimization (SMO), which is being applied to various problems from the engineering domain. He has published more than 60 research papers in various international journals/conferences. He has also received Gold Medal at UG and PG levels. He is Series Editor of Algorithms for Intelligent Systems (AIS) and Studies in Autonomic, Data-driven and Industrial Computing published by Springer. He is Editor-in-Chief of International Journal of Swarm Intelligence (IJSI) published by Inderscience. He is also Associate Editor of IEEE ACCESS (IEEE) and ARRAY (Elsevier). He is the steering committee member and the general chair of the annual conference series SocProS. He is the general secretary of Soft Computing Research Society (SCRS). 

Emeritus Professor Lance C.C. Fung was trained as Marine Radio/Electronic Officer, and he graduated with a B.Sc. degree with First Class Honours and a M.Eng. degree from the University of Wales. His Ph.D. degree from the University of Western Australia was supervised by the late Professor Kit Po Wong. Lance taught at Singapore Polytechnic, Curtin University, and Murdoch University where he was appointed Emeritus Professor in 2015. His roles have included Associate Dean of Research and Director of the Centre for Enterprise Collaborative in Innovative Systems. He has supervised to completion over 31 doctoral students and published over 335 academic articles. His contributions can be viewed at IEEE Xplore, Google Scholar, and Scopus. Lance has been a dedicated volunteer for the IEEE in various positions for over two decades. Lance’s motto is “Learning has no Boundary”.  While currently being with RMIT University, School of Engineering, Dr. Simic is also General Editor of KES Journal and Professor of University Union Nikola Tesla, Faculty of Business and Law, Belgrade, Serbia. Adjunct Professor of Kalinga Institute of Industrial Technology (KIIT), School of Computer Engineering, Bhubaneswar, Odisha, India; Associate Director of Australia-India Research Centre for Automation Software Engineering (AICAUSE). He has bachelor’s, master’s, and Ph.D. degrees in Electronics Engineering from The University of Nis, Serbia, and Graduate Diploma in Education from RMIT University, Australia. Dr. Simic has comprehensive experience from industry (Honeywell Information Systems), CISCO, Research Institute and Academia, from overseas and Australia. For his contributions, he has received prestigious awards and recognitions, like two for industry innovation, from Honeywell, and two University awards for the excellence in teaching and provision of education to the community. 

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This book aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behaviour of applications. Applications of Artificial intelligence (AI)-based systems play a significant role in today’s software industry. The sensors data from hardware-based systems making a mammoth database, increasing day by day. Recent advances in big data generation and management have created an avenue for decision-makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioural modelling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications. Original research and review works with model and build data-driven applications using computational algorithm are included as chapters in this book.