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E-raamat: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

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Edited by (National Yunlin University of Science and Technology, Taiwan), Edited by (Professor and Head of Department of Computing, Macquarie University, Sydney, Au), Edited by (Dean, Department of Computer Science, Henan University of Science and Technology, China)
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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. Presents a brief overview of computational intelligence paradigms and its significant role in application domainsIllustrates the state-of-the-art and recent developments in the new theories and applications of CI approachesFamiliarizes the reader with computational intelligence concepts and technologies that are successfully used in the implementation of cloud-centric multimedia services in massive data processingProvides new advances in the fields of CI for bio-engineering application
Contributors xiii
Foreword xvii
Preface xix
Chapter 1 A Cloud-Based Big Data System to Support Visually Impaired People 1(22)
Huseyin Temucin
Ali Seydi Keceli
Aydin Kaya
Hamdi Yalin Yalic
Bedir Tekinerdogan
1.1 Introduction
1(1)
1.2 Related Work
2(1)
1.3 Background
3(5)
1.3.1 Internet of Things (IoT)
3(1)
1.3.2 Cloud Computing
4(3)
1.3.3 Face Detection and Recognition
7(1)
1.3.4 Optical Character Recognition (OCR)
7(1)
1.4 Problem Statement
8(1)
1.5 System Architecture
8(2)
1.5.1 Top-Level Architecture
8(2)
1.6 Big Data Analytics
10(3)
1.6.1 Text Recognition
11(1)
1.6.2 Face Recognition
12(1)
1.7 Prototype
13(2)
1.8 Evaluation
15(4)
1.9 Conclusion
19(1)
References
20(3)
Chapter 2 Computational Intelligence in Smart Grid Environment 23(38)
Viera Rozinajova
Anna Bou Ezzeddine
Marek Loderer
Jaroslav Loebl
Robert Magyar
Petra Vrablecova
2.1 Introduction
23(4)
2.1.1 Power Load Forecasting
25(1)
2.1.2 Electricity Price Forecasting
25(1)
2.1.3 Smart Grid Optimization
26(1)
2.2 Related Work and Open Issues
27(6)
2.2.1 Power Load Forecasting
27(2)
2.2.2 Prediction of Electricity Spot Prices in Smart Grid
29(2)
2.2.3 Optimization and Metaheuristics in Big Data and Microgrids
31(2)
2.3 Overview of Methods Used in Smart Grid Problems
33(9)
2.3.1 Forecasting Methods
33(6)
2.3.2 Optimization Methods
39(3)
2.4 Proposed Methods
42(10)
2.4.1 Electricity Price Forecasting
43(1)
2.4.2 Power Load Forecasting
43(9)
2.5 Future Work
52(1)
2.6 Conclusions
53(1)
Acknowledgment
53(1)
References
53(8)
Chapter 3 Patient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud With Hardware Acceleration 61(30)
Alex D. Torres
Hao Yan
Armin Haj Aboutalebi
Arun Das
Lide Duan
Paul Rad
3.1 Introduction
61(1)
3.2 System Overview
62(2)
3.3 Background
64(4)
3.3.1 Facial Emotion Recognition
64(1)
3.3.2 Big Data Analytics on the Cloud
65(1)
3.3.3 Deep Learning Using Convolutional Neural Networks (CNNs)
66(2)
3.4 System Architecture
68(6)
3.4.1 Face Detection in Images
69(1)
3.4.2 Facial Emotion Recognition Using CNNs
70(4)
3.4.3 The CNN Model Training
74(1)
3.5 System Implementation
74(2)
3.5.1 A Secure, Multi-tenant Cloud Storage System
76(1)
3.6 Experiments
76(7)
3.6.1 Dataset
76(1)
3.6.2 GPU Benchmarking and Comparison
77(3)
3.6.3 Facial Emotion Recognition Accuracy
80(2)
3.6.4 Model Performance and Power With Hardware Acceleration
82(1)
3.7 DeepPain: Mapping Patient Emotions to Pain Intensity Levels
83(2)
3.8 Conclusions and Future Work
85(1)
Acknowledgments
86(1)
References
86(5)
Chapter 4 Novel Computational Intelligence Techniques for Automatic Pain Detection and Pain Intensity Level Estimation From Facial Expressions Using Distributed Computing for Big Data 91(32)
A. Sherly Alphonse
Dejey Dharma
4.1 Introduction
91(1)
4.2 Background and History of Computational Techniques
92(3)
4.2.1 Feature Extraction Techniques
93(1)
4.2.2 Dimension Reduction Techniques
94(1)
4.2.3 Machine Learning Techniques for Classification
95(1)
4.2.4 Distributed Computing for Heavy Computations
95(1)
4.3 System Architecture for Distributed Computing
95(2)
4.4 Design of the Novel System for Pain Detection and Pain Intensity Estimation
97(11)
4.4.1 Preprocessing
97(1)
4.4.2 Feature Extraction
97(7)
4.4.3 Universal Kernel-Based Dimension Reduction System (UKDRS)
104(3)
4.4.4 Classification Using ELM-RBF
107(1)
4.5 Experiments and Results
108(10)
4.5.1 Datasets Used
108(1)
4.5.2 Evaluation of Classification Results While Detecting Pain
108(4)
4.5.3 Evaluation of Classification Results in Pain Intensity Level Estimation
112(2)
4.5.4 Evaluation of Computational Time for Pain Detection and Pain Intensity Level Estimation
114(2)
4.5.5 Discussion
116(2)
4.6 Conclusion and Future Outlook
118(1)
References
118(5)
Chapter 5 Computational Intelligence Enabling the Development of Efficient Clinical Decision Support Systems: Case Study of Heart Failure 123(12)
Oluwarotimi Williams Samuel
Grace Mojisola Asogbon
Arun Kumar Sangaiah
Guanglin Li
5.1 Introduction
123(1)
5.2 Core Components of Diagnoses Based CDSS
124(1)
5.3 CI Predictor Based on Fuzzy Reasoning Technique
125(3)
5.4 CI Predictor Based on Multi Layer Perceptron Network
128(2)
5.5 CI Based CDSS Evaluation Methods
130(1)
5.6 Conclusion
131(1)
Acknowledgments
132(1)
References
132(3)
Chapter 6 Aspect Oriented Modeling of Missing Data Imputation for Internet of Things (loT) Based Healthcare Infrastructure 135(12)
Senthil Murugan Balakrishnan
Arun Kumar Sangaiah
6.1 Introduction
135(1)
6.2 Literature Review
136(3)
6.3 Proposed Framework
139(1)
6.4 Proposed Missing Data Imputation Service
140(1)
6.5 Experimentation and Results
141(3)
References
144(3)
Chapter 7 A Hybrid Computational Intelligence Decision Making Model for Multimedia Cloud Based Applications 147(12)
Chinu Singla
Sakshi Kaushal
Amandeep Verma
Harish Kumar
7.1 Introduction
147(1)
7.2 Literature Review
148(1)
7.3 Research Background
149(2)
7.3.1 Cloud Computing
149(1)
7.3.2 Fuzzy Delphi Method
150(1)
7.3.3 Fuzzy Analytic Hierarchy Process (FAHP)
151(1)
7.4 The Proposed Hybrid MCDM Model
151(3)
7.5 A Numeric Application of the Proposed Hybrid Approach
154(2)
7.6 Conclusion and Future Study
156(1)
References
156(3)
Chapter 8 Energy-Constrained Workflow Scheduling in Cloud Using E-DSOS Algorithm 159(12)
Megha Sharma
Amandeep Verma
Arun Kumar Sangaiah
8.1 Introduction
159(1)
8.2 Related Work
160(1)
8.3 System Model
161(3)
8.4 The Application Model
164(1)
8.5 Experimental Results and Discussion
165(1)
8.6 Conclusion and Future Work
166(2)
References
168(3)
Chapter 9 Producing Better Disaster Management Plan in Post-Disaster Situation Using Social Media Mining 171(14)
Sounak Sadhukhan
Soumya Banerjee
Prasun Das
Arun Kumar Sangaiah
9.1 Introduction
171(1)
9.2 Literature Survey
172(1)
9.3 Data Description
173(2)
9.3.1 Study Event
173(1)
9.3.2 Data Collection
173(1)
9.3.3 Disaster Tweet Ontology
174(1)
9.3.4 Quantitative Assessment
175(1)
9.4 Tweet Classification Process
175(3)
9.4.1 Preprocessing of Tweets
176(1)
9.4.2 Feature Vector Representation
177(1)
9.4.3 Learning Algorithms
177(1)
9.4.4 Performance Evaluation
177(1)
9.5 Tweet Classification Algorithms
178(3)
9.5.1 Voting Classifier
179(2)
9.6 Information Extraction
181(1)
9.7 Conclusion
182(1)
References
183(2)
Chapter 10 Metaheuristic Algorithms: A Comprehensive Review 185(48)
Mohamed Abdel-Basset
Laila Abdel-Fatah
Arun Kumar Sangaiah
10.1 Introduction
185(1)
10.2 Metaheuristics Taxonomies
186(2)
10.3 Metaphor Based Metaheuristics
188(19)
10.3.1 Biology Based Metaheuristics
188(6)
10.3.2 Chemistry Based Metaheuristics
194(4)
10.3.3 Music Based Metaheuristics
198(3)
10.3.4 Math Based Metaheuristics
201(3)
10.3.5 Physics Based Metaheuristics
204(1)
10.3.6 Social and Sport Based Metaheuristics
205(2)
10.4 Non-Metaphor Based Metaheuristics
207(2)
10.4.1 Tabu Search (TS)
207(1)
10.4.2 Variable Neighborhood Search (VNS)
208(1)
10.4.3 Partial Optimization Metaheuristic Under Special Intensification Conditions (POPMUSIC)
208(1)
10.5 Variants of Metaheuristics
209(6)
10.5.1 Upgrading of Metaheuristics
209(2)
10.5.2 Metaheuristics Acclimatization
211(2)
10.5.3 Hybridization of Metaheuristics
213(2)
10.6 A Case Study: Weld Beam Design Problem
215(3)
10.6.1 Weld Beam Design Problem
215(2)
10.6.2 Experimental Results
217(1)
10.7 Limitation and New Trends
218(1)
10.8 Conclusion
218(7)
References
225(8)
Chapter 11 Unsupervised Anomaly Detection for High Dimensional Data-an Exploratory Analysis 233(20)
Anitha Ramchandran
Arun Kumar Sangaiah
11.1 Introduction
233(2)
11.1.1 Research Problem
233(1)
11.1.2 Research Contribution
234(1)
11.1.3 Organization
234(1)
11.2 Preliminary Discussion
235(2)
11.2.1 Related Works
235(2)
11.3 Subspace Algorithms
237(2)
11.4 Algorithm Which Do not Consider Subspaces
239(3)
11.4.1 Angle Based
239(1)
11.4.2 Approximate Nearest Neighbor Based
239(1)
11.4.3 Ensemble Methods
240(1)
11.4.4 Dimension Reduction Based
240(1)
11.4.5 Feature Selection Based
240(1)
11.4.6 Other Methods
241(1)
11.5 Datasets
242(1)
11.6 Tools and Evaluation
242(1)
11.7 Applications
243(2)
11.8 Proposed Framework DBN-K Means
245(4)
11.8.1 Experiment and Result
247(2)
11.9 Conclusion and Future Work
249(1)
References
250(3)
Chapter 12 Fog - Driven Healthcare Framework for Security Analysis 253(18)
Shalini Parasuraman
Arun Kumar Sangaiah
12.1 Introduction
253(1)
12.2 Cloud Models
254(3)
12.2.1 Deployment Models
254(1)
12.2.2 Cloud Service Models
254(3)
12.3 Cryptography
257(1)
12.3.1 Secret Key Cryptography (Symmetric Key)
258(1)
12.3.2 Public Key Cryptography (Asymmetric Key)
258(1)
12.4 RSA and ECC in Cloud
258(1)
12.4.1 Performance Comparison of RSA and ECC
258(1)
12.5 Fog Computing
259(4)
12.5.1 Characteristics of Fog Computing
260(1)
12.5.2 Data Security Issues in Fog Computing (Literature Review)
261(2)
12.6 Fog Computing Revotilising in Healthcare IoT
263(1)
12.7 Proposed Framework
263(4)
12.7.1 RSA Comparison in Cloud and Fog
264(1)
12.7.2 ECC Comparison in Cloud and Fog
265(2)
12.8 Performance Comparison of RSA and ECC in Fog
267(1)
12.8.1 Security Comparison of Cloud and Fog
267(1)
12.8.2 Result Analysis
267(1)
12.9 Cloud-Fog Variance
267(1)
12.10 Conclusion and Future Work
267(2)
References
269(2)
Chapter 13 Medical Quality of Service Optimization Over Internet of Multimedia Things 271(26)
Ali Hassan Sodhro
Arun Kumar Sangaiah
Gul Hassan Sodhro,
Mir Muhammad Lodro
Aicha Sekhari
Yacine Ouzrout
Sandeep Pirbhulal
Kaneez Fatima
13.1 Introduction
271(2)
13.2 Literature Survey
273(2)
13.3 Convergence and Interoperability Between Telemedicine and IoT
275(3)
13.3.1 Convergence Between Telemedicine and IoT
276(1)
13.3.2 Interoperability Between Telemedicine and IoT
276(2)
13.4 Proposed Algorithms for Medical QoS Optimization Over IoT
278(9)
13.4.1 Modified Lazy Video Transmission Algorithm for Pre-recorded Video Transmission
278(2)
13.4.2 Online Video Transmission Algorithm for Live Video Transmission
280(4)
13.4.3 Rate Control Video Transmission Algorithm for High Definition Video Transmission
284(3)
13.5 Medical Quality of Service Mapping Over Joint Telemedicine and IoT
287(1)
13.6 Experimental Results and Discussion
288(4)
13.7 Conclusion
292(1)
References
293(4)
Chapter 14 Energy-Efficiency of Tools and Applications on Internet 297(22)
Ali Hassan Sodhro
Arun Kumar Sangaiah
Gul Hassan Sodhro
Aicha Sekhari
Yacine Ouzrout
Sandeep Pirbhulal
14.1 Introduction
297(1)
14.2 Related Work
298(4)
14.2.1 Energy Consumption of Software
299(1)
14.2.2 Energy Consumption of Web-Browsers
299(1)
14.2.3 Energy Consumption of Media Players
300(1)
14.2.4 Energy Consumption of File Transfer Protocols
300(1)
14.2.5 Energy Consumption of Wired Secure Protocols
301(1)
14.2.6 Energy Consumption of Wireless Secure Protocols
301(1)
14.3 Performance Indicators and Tools for Energy Consumption Measurement
302(1)
14.3.1 Performance Indicators
302(1)
14.3.2 Tools for Energy Consumption Measurement
302(1)
14.4 Methodology
303(1)
14.5 Experimental Results and Discussion
304(6)
14.5.1 Equipment
305(1)
14.5.2 Experimental Procedures for Windows 7
305(2)
14.5.3 Experimental Procedures for Linux (Ubuntu 16.04)
307(3)
14.6 Results and Discussion
310(7)
14.6.1 Web-Browser Applications for Windows 7 and Ubuntu 16.04
310(2)
14.6.2 Media Players for Ubuntu 16.04
312(1)
14.6.3 Media Players for Windows 7
312
14.6.4 File Transfer Protocols for Windows 7 and Ubuntu 16.04
115(200)
14.6.5 Wired (SSL/TLS) and Security Protocols for Windows 7 and Ubuntu 16.04
315(1)
14.6.6 Wireless (WPA2) Security Protocols for Windows 7 and Ubuntu 16.04
316(1)
14.7 Conclusions and Future Research
317(1)
References
317(2)
Chapter 15 Transforming Healthcare Via Big Data Analytics 319(16)
S.S. Blessy Trencia Lincy
N. Suresh Kumar
15.1 Introduction
319(2)
15.1.1 Data-Driven Decision Making
319(1)
15.1.2 Healthcare Population
320(1)
15.1.3 The Experience of Patients
320(1)
15.1.4 Proper Clinical Care
320(1)
15.1.5 Administration
320(1)
15.2 Data Analytics in Healthcare
321(8)
15.2.1 Lifecycle of Data Analytics in Healthcare
322(1)
15.2.2 Role of Data Analyst
323(1)
15.2.3 Healthcare Analytics
323(1)
15.2.4 Types of Analytics
324(5)
15.3 Big Data for Healthcare: Challenges in Deployment
329(1)
15.3.1 Generate New Knowledge Using Predictive Analytics
329(1)
15.3.2 Analyze Patient Data in Real-Time Using Big Data Platform Hadoop
329(1)
15.3.3 Predict Where Emergency Services Are Most Likely to Be Needed
329(1)
15.3.4 Optimize Care for Patient Populations
329(1)
15.3.5 Reduce the Cost of Care
330(1)
15.4 Big Data Platform
330(2)
15.4.1 Scalable Big Data Analytics
330(1)
15.4.2 Flattering Management Server-less Insight
330(1)
15.4.3 Hasty Queries and Scaling Datasets
331(1)
15.4.4 Unified Cohesive Stream and Batch Processing
331(1)
15.4.5 Hadoop and Spark in the Cloud Environment
331(1)
15.4.6 Controlled Databases, Storage of the Object and Its Archival
331(1)
15.4.7 The Next Arena 'The Machine Intelligence'
331(1)
15.5 Healthcare Essentials: Big Data
332(1)
15.5.1 Granular Management of Metadata
332(1)
15.5.2 Management of Privacy
332(1)
15.5.3 Transformation of Data
332(1)
15.5.4 Plays Well With Erstwhile
332(1)
15.5.5 Condense Data Slump
332(1)
15.6 Conclusion
333(1)
References
333(2)
Index 335
Prof. Arun Kumar Sangaiah received his PhD from the School of Computer Science and Engineering, VIT University, Vellore, India. He is currently a Full Professor with National Yunlin University of Science and Technology, Taiwan. He is also a Professor at the School of Computing Science and Engineering, VIT University, Vellore, India. His areas of research interest include machine learning, Internet of Things, Sustainable Computing. He has published more than 300 research articles in refereed journals, 11 edited books, one patent (held and filed), as well as four projects funded by MOST-TAIWAN, one funded by Ministry of IT of India, and several international projects (CAS, Guangdong Research fund, Australian Research Council). Dr. Sangaiah has received many awards, Yushan Young Scholar, Clarivate Top 1% Highly Cited Researcher (2021,2022, 2023), Top 2% Scientist (Standord Report-2020,2021,2022, 2023), PIFI-CAS fellowship, Top-10 outstanding researcher, CSI significant Contributor etc. He is also serving as Editor-in-Chief and/or Associate Editor of various reputed ISI journals. Dr. Sangaiah is a visiting scientist (2018-2019) with Chinese Academy of Sciences (CAS), China and visiting researcher of Université Paris-Est (UPEC), France (2019-2020) and etc.

Prof. Zhiyong Zhang received his Master, Ph.D. degrees in Computer Science from Dalian University of Technology and Xidian University, respectively. He was a Post-Doctoral Research Fellow at Xi'an Jiaotong University, China. He is currently a full Henan Province Distinguished Professor and Dean with Department of Computer Science, College of Information Engineering, Henan University of Science & Technology. Prof. Zhang is a visiting professor of Computer Science Department, Iowa State University. He is an ACM Senior Member, IEEE Senior Member, IEEE Systems, Man, Cybernetics Society Technical Committee on Soft Computing, World Federation on Soft Computing Young Researchers Committee, Membership for Digital Rights Management Technical Specialist Workgroup Attached to China National Audio, Video, Multimedia System and Device Standardization Technologies Committee. Prof. Zhangs research interests include multimedia social networks and digital rights management, applied soft computing, trusted computing, as well as security risk management. He has published over 80 scientific papers and four books on the above research fields, and held 8 granted patents. Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University, Sydney, Australia. Before moving to Macquarie University, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Prof. Sheng has more than 400 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences. He is ranked by Microsoft Academic as one of the Top Authors in Services Computing (ranked the 5th of All Time worldwide). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003).
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