The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems (Intelligent Data-Centric Systems) - Tapa blanda

Acerca de los autores

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.

De la contraportada

<p>The recent rapid development of Internet of things (IoT) has focused research on 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. Frequently, human review of the recorded video is still needed for detecting abnormalities. Even though machine-driven techniques can facilitate the detection of potential abnormalities, processing the enormous amounts of data remain a demanding computational task. Cloud computing has been recognized as an ideal candidate for this kind of Big Data processing, due to many attractive features including high elasticity, good scalability, supporting pay-as-you-go service models, and the capability of overcoming the constraints in both software parallelism and hardware capacities.</p> <p>Cognitive computing is the simulation of human thought processes in a cybernetic model. This model involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The goal of this model is to create automated IT systems that are capable of resolving problems without demanding human aid. Cognitive computing systems use machine learning and deep learning algorithms. Such systems continually acquire knowledge from the data fed into them by mining data for information. The systems refine the way they look for patterns and as well as the way they process data so they become capable of anticipating new problems and modeling possible solutions. Cognitive computing is used in numerous artificial intelligence (AI) applications, including expert systems, natural language programming, neural networks, robotics, virtual reality and surveillance tracking systems.</p> <p>Machine / deep learning is being used to understand “normal operational behavior across application, infrastructure and network. The cognitive approach in surveillance tracking systems produces a cybernetic model that applies human thought processes using the cloud environment. The Cognitive Approach in Cloud Computing and Internet of Things Technologies for Surveillance Tracking Systems explores the state of the art, software tools and innovative strategies to provide better understanding of surveillance tracking systems in the cognitive cloud environment. The book states the different problems and challenges of the surveillance tracking cybernetic model with various cognitive approaches to yield state-of-the-art results. This advanced publication provides useful references for educational institutions, industry, academic researchers, professionals, developers and practitioners to apply and evaluate this leading-edge research.</p>