Academic Press Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications (Intelligent Data-Centric Systems)

Product Description Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful. Review An excellent guide for surveying the state-of-the-art in computational intelligence as it is applied to challenging and real-world problems From the Back Cover Arun Kumar Sangaiah School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India Michael Sheng School of Computer Science, The University of Adelaide, Australia Zhiyong Z. Zhang Dean, Department of Computer Science, Henan University of Science & Technology, China As an overwhelming volume of data is being generated rapidly from various sources (e.g., intelligent terminals, multimedia information, cloud services, energy system, bio-engineering and health informatics), the computational intelligence paradigms constitutes a promising role in handling the uncertainty, unpredictability (e.g., data volume, velocity and heterogeneity or variety) and real-world optimization problems. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications proposes the broad overview of CI paradigms that covers: Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS) and etc. Furthermore, this book highlights the recent research on some representative techniques to elaborate how data centric system formed a powerful platform for processing of cloud hosted multimedia big data could be analyzed, processed and characterized by CI. The CI paradigms not limited to feature selection, optimization approaches for handling high dimensional data, deep learning architectures, hybrid classification and clustering methods in the context of big data infrastructure designs to support big data science centric systems. Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications also provides a view at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the bio-engineering field. It is written for domain experts and developers, who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of data centric system in the context of multi-media cloud, big data era and its related applications such as smarter health care, homeland security, traffic control, trading analysis and telecom etc. The book is well suited for researchers and PhD exploring the significance of data centric systems in the next paradigm of computing. About the Author Michael Sheng is a full Professor and Deputy Head of the School of Computer Science at the University of Adelaide. Michael holds a PhD degree in computer science from