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Computational intelligence for multimedia big data on the cloud with engineering applications /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Computational intelligence for multimedia big data on the cloud with engineering applications // edited by Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang.
其他作者:	Sangaiah, Arun Kumar,
出版者:	London ;Academic Press is an imprint of Elsevier,c2018.
面頁冊數:	xxii, 340 p. :ill. ;24 cm.
標題:	Computational intelligence. -
ISBN:	0128133147 (pbk.)
ISBN:	9780128133149 (pbk.) :

Computational intelligence for multimedia big data on the cloud with engineering applications /
Computational intelligence for multimedia big data on the cloud with engineering applications /edited by Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang. - London ;Academic Press is an imprint of Elsevier,c2018. - xxii, 340 p. :ill. ;24 cm. - Intelligent data centric systems. - Intelligent data centric systems..

Includes bibliographical references and index.

Front Cover; Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications; Copyright; Contents; Contributors; Foreword; Preface; Organization of the Book; Audience; 1 A Cloud-Based Big Data System to Support Visually Impaired People; 1.1 Introduction; 1.2 Related Work; 1.3 Background; 1.3.1 Internet of Things (IoT); 1.3.2 Cloud Computing; 1.3.3 Face Detection and Recognition; 1.3.4 Optical Character Recognition (OCR); 1.4 Problem Statement; 1.5 System Architecture; 1.5.1 Top-Level Architecture; 1.6 Big Data Analytics; 1.6.1 Text Recognition.

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.

ISBN: 0128133147 (pbk.)Subjects--Topical Terms:

157474
Computational intelligence.

LC Class. No.: Q342 / .C66 2018

Dewey Class. No.: 006.3

Computational intelligence for multimedia big data on the cloud with engineering applications /
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245 0 0 $a Computational intelligence for multimedia big data on the cloud with engineering applications / $c edited by Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang.
260 # $a London ; $a San Diego, CA : $b Academic Press is an imprint of Elsevier, $c c2018.
300 $a xxii, 340 p. : $b ill. ; $c 24 cm.
490 1 $a Intelligent data centric systems
504 $a Includes bibliographical references and index.
505 0 # $a Front Cover; Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications; Copyright; Contents; Contributors; Foreword; Preface; Organization of the Book; Audience; 1 A Cloud-Based Big Data System to Support Visually Impaired People; 1.1 Introduction; 1.2 Related Work; 1.3 Background; 1.3.1 Internet of Things (IoT); 1.3.2 Cloud Computing; 1.3.3 Face Detection and Recognition; 1.3.4 Optical Character Recognition (OCR); 1.4 Problem Statement; 1.5 System Architecture; 1.5.1 Top-Level Architecture; 1.6 Big Data Analytics; 1.6.1 Text Recognition.
505 8 # $a 1.6.2 Face Recognition1.7 Prototype; 1.8 Evaluation; 1.9 Conclusion; References; 2 Computational Intelligence in Smart Grid Environment; 2.1 Introduction; 2.1.1 Power Load Forecasting; 2.1.2 Electricity Price Forecasting; 2.1.3 Smart Grid Optimization; 2.2 Related Work and Open Issues; 2.2.1 Power Load Forecasting; 2.2.1.1 Stream Forecasting; 2.2.1.2 Adaptivity; 2.2.2 Prediction of Electricity Spot Prices in Smart Grid; 2.2.3 Optimization and Metaheuristics in Big Data and Microgrids; 2.3 Overview of Methods Used in Smart Grid Problems; 2.3.1 Forecasting Methods.
505 8 # $a 2.3.1.1 Statistical Techniques2.3.1.2 Arti cial Intelligence Techniques; 2.3.1.3 Hybrid Techniques (Ensemble Learning); 2.3.2 Optimization Methods; 2.3.2.1 Particle Swarm Optimization; 2.3.2.2 Arti cial Bee Colony; 2.3.2.3 Genetic Algorithm; 2.3.2.4 Hyper-Heuristics; 2.4 Proposed Methods; 2.4.1 Electricity Price Forecasting; 2.4.2 Power Load Forecasting; 2.4.2.1 Adaptive Ensemble Learning for Power Load Forecasting; 2.4.2.2 Online Support Vector Regression; 2.4.2.3 Data; 2.4.2.4 Results; 2.5 Future Work; 2.6 Conclusions; Acknowledgment; References.
505 8 # $a 3 Patient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud With Hardware Acceleration3.1 Introduction; 3.2 System Overview; 3.3 Background; 3.3.1 Facial Emotion Recognition; 3.3.2 Big Data Analytics on the Cloud; 3.3.3 Deep Learning Using Convolutional Neural Networks (CNNs); 3.4 System Architecture; 3.4.1 Face Detection in Images; 3.4.2 Facial Emotion Recognition Using CNNs; 3.4.3 The CNN Model Training; 3.5 System Implementation; 3.5.1 A Secure, Multi-tenant Cloud Storage System; 3.6 Experiments; 3.6.1 Dataset; 3.6.2 GPU Benchmarking and Comparison.
505 8 # $a 3.6.3 Facial Emotion Recognition Accuracy3.6.4 Model Performance and Power With Hardware Acceleration; 3.7 DeepPain: Mapping Patient Emotions to Pain Intensity Levels; 3.8 Conclusions and Future Work; Acknowledgments; References; 4 Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data; 4.1 Introduction; 4.2 Background and History of Computational Techniques; 4.2.1 Feature Extraction Techniques; 4.2.2 Dimension Reduction Techniques.
520 # $a 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.
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650 # 0 $a Cloud computing. $3 186693
650 # 0 $a Big data. $3 1000002445
650 # 0 $a Multimedia data mining. $3 1000121791
700 1 # $a Sangaiah, Arun Kumar, $d 1981- $3 1000121787
700 1 # $a Sheng, Quan Z. $3 1000121788
700 1 # $a Zhang, Zhiyong. $3 1000121789
830 0 $a Intelligent data centric systems. $3 1000121790