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 Ph D students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful. – Presents a brief overview of computational intelligence paradigms and its significant role in application domains- Illustrates the state-of-the-art and recent developments in the new theories and applications of CI approaches- Familiarizes the reader with computational intelligence concepts and technologies that are successfully used in the implementation of cloud-centric multimedia services in massive data processing- Provides new advances in the fields of CI for bio-engineering application
Arun Kumar Sangaiah & Michael Sheng
Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications [EPUB ebook]
Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications [EPUB ebook]
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Limba Engleză ● Format EPUB ● ISBN 9780128133279 ● Editor Arun Kumar Sangaiah & Michael Sheng ● Editura Elsevier Science ● Publicat 2018 ● Descărcabil 3 ori ● Valută EUR ● ID 5812435 ● Protecție împotriva copiilor Adobe DRM
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