The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems discusses the recent, rapid development of Internet of things (IoT) and its focus on research in smart cities, especially on surveillance tracking systems in which computing devices are widely distributed and huge amounts of dynamic real-time data are collected and processed. Efficient surveillance tracking systems in the Big Data era require the capability of quickly abstracting useful information from the increasing amounts of data. Real-time information fusion is imperative and part of the challenge to mission critical surveillance tasks for various applications.
This book presents all of these concepts, with a goal of creating automated IT systems that are capable of resolving problems without demanding human aid.
- Examines the current state of surveillance tracking systems, cognitive cloud architecture for resolving critical issues in surveillance tracking systems, and research opportunities in cognitive computing for surveillance tracking systems
- Discusses topics including cognitive computing architectures and approaches, cognitive computing and neural networks, complex analytics and machine learning, design of a symbiotic agent for recognizing real space in ubiquitous environments, and more
- Covers supervised regression and classification methods, clustering and dimensionality reduction methods, model development for machine learning applications, intelligent machines and deep learning networks
- includes coverage of cognitive computing models for scalable environments, privacy and security aspects of surveillance tracking systems, strategies and experiences in cloud architecture and service platform design
J. Dinesh Peter is Program Coordinator for the Department of Computer Sciences Technology at Karunya University, and author of more than 25 academic articles/chapters/conference papers. He has been active in government and industry as the developer of new technologies including Digital Image Processing, Virtual Reality Technology, Medical Image Processing, Computer Vision, and Optimization. He has been Guest Editor of a special issue of the Elsevier journal Computers and Electrical Engineering, and Guest Editor of special issues of the Journal of Cloud Computing and Journal of Big Data Intelligence. Dr. Peter received his Ph.D. in Computer Science and Engineering from National Institute of Technology Calicut, India.
Amir Hossein Alavi is an Assistant Professor in the Department of Civil and Environmental Engineering, and holds courtesy appointments in the Department of Bioengineering and Department of Mechanical Engineering and Materials Science, at the University of Pittsburgh, United States. His multidisciplinary scientific studies are organized around three research thrusts: 1) mechanics and electronics of multifunctional materials and structures, 2) embedded self-powered sensing systems, and 3) data-driven characterization, design and discovery of engineering systems.
Bahman Javadi is a Senior Lecturer in Networking and Cloud Computing at the Western Sydney University, Australia. Prior to this appointment, he was a Research Fellow at the University of Melbourne, Australia. From 2008 to 2010, he was a Postdoctoral Fellow at the INRIA Rhone-Alpes, France. He received his MS and PhD degrees in Computer Engineering from the Amirkabir University of Technology in 2001 and 2007, respectively. He has been a Research Scholar at the School of Engineering and Information Technology, Deakin University, Australia during his PhD course. He is co-founder of the Failure Trace Archive, which serves as a public repository of failure traces and algorithms for distributed systems. He has published more than 90 research papers and received numerous Best Paper Awards at IEEE/ACM conferences for his papers. He served as a program committee of many international conferences and workshops. His research interests include Cloud computing, performance evaluation of large scale distributed computing systems, and reliability and fault tolerance. He is a member of ACM and senior member of IEEE.
Steven L. Ferandes is a post-doctoral researcher in the Department of Electrical and Computer Engineering, University of Alabama at Birmingham, and author of more than 35 academic articles/chapters/conference papers. He has been active in industry as the developer of new technologies including Socket Development for Validation of Standard Cll Automation Tool Used in Test Chip Design, Automation Framework for Web Services, Automation Framework for Mobile Applications (Android, iOS, Windows), Python programming for Computer Vision, Machine Learning, and Deep Learning. Dr. Fernandes received his Ph.D. in Computer Vision and Machine Learning from Karunya University, Coimbatore, India.
