Most events and activities in today's world are ordinarily captured using photos, videos and other multimedia content. Such content has some limitation of storing data and fetching them effectively. Three-dimensional continuous PC animation is the most proper media to simulate these occasions and a...
, Contents
1. Editors
2. Contributors
3. Chapter 1 Use of Virtual Reality in Exposure Therapy and Other Psychological Treatment
MethodsAman Sariya, Rishabh Nanawati and Supriya Agrawal
4. 1.1 Introduction
5. 1.2 Psychology
6. 1.3 The workings and quality of the simulation
7. 1.4 Benefits
8. 1.5 VR in the Use of Specific Disorders
9. 1.6 Methods to Administer VRET in Therapy
10. 1.7 Issues and resolution
11. 1.8 Discussion
12. References
13. Chapter 2 Role of Swarm Intelligence and Neural Network in Intelligent Traffic
ManagementUmesh Kumar Lilhore and Sarita Simaiya
14. 2.1 Introduction
15. 2.2 Related Work
16. 2.3 Swarm Intelligence, IoT and Neural Network in ITM
17. 2.4 Swarm Intelligence (SI)–Based Methods
18. 2.5 Element of IoT Technology
19. 2.6 IoT Architecture
20. 2.7 Deep Learning
21. 2.8 Advantages of an ITM
22. 2.9 Key Applications of ITM
23. 2.10 Intelligent Transport System (ITM) Functionalities
24. 2.11 Challenges Besides Transport in Metropolitan Centers
25. 2.12 Conclusions
26. References
27. Chapter 3 Image Systems and VisualizationsPulkit Narwal, Neelam Duhan and Komal
Kumar Bhatia
28. 3.1 Introduction
29. 3.2 Fundamentals of Computer Vision
30. 3.3 Vision in Single and Multiple Images
31. 3.4 Applications of Computer Vision
32. 3.5 Problem Implementation: Unattended Object Detection
33. 3.6 Conclusion
34. References
35. Chapter 4 Virtual Reality in Social Media Marketing: The New Age PotentialSheetal Soni,
Kaustubhi Shuklaa, Usha Yadav and Harjeev Singh Ahluwalia
36. 4.1 Introduction
37. 4.2 The Changing Era of Technology
38. 4.3 The Evolution of Marketing Strategies
39. 4.4 Applications of Extended Reality – Companies Being Innovators
, 40. 4.5 The Future of Virtual Reality and Social Media Marketing
41. 4.6 Conclusion
42. References
43. Chapter 5 An Efficient Deep Learning Framework for Multimedia Big Data AnalyticsG.
S. Pradeep Ghantasala, L.R. Sudha, T. Veni Priya, P. Deepan and R. Raja Vignesh
44. 5.1 Introduction
45. 5.2 Deep Learning
46. 5.3 Deep Learning Frameworks
47. 5.4 Classification
48. 5.5 Object Detection
49. 5.6 Single Shot Detector
50. 5.7 Applications of Multimedia Analytics Based on Deep Learning Techniques
51. 5.8 Conclusion
52. References
53. Chapter 6 An Optimal System on Data Challenge with Distributed Data Management on
Cloud, Fog and Edge ComputingM. Arvindhan, Abhineet Anand and Md. Abdul Wassey
54. 6.1 Introduction
55. 6.2 Infrastructure and Integration with Provisioning and Management Systems
56. 6.3 Five Challenges of a Traditional Data Warehouse
57. 6.4 The Importance of Adaptation
58. 6.5 Edge Taxonomy and Framework
59. 6.6 The Fog Computing Data Platform
60. 6.7 Conclusion
61. References
62. Chapter 7 Anomaly Detection in Real-Time Videos using Match Subspace System and
Deep Belief NetworksD. Ratna Kishore, D. Suneetha, G. S. Pradeep Ghantasala and B.
Ravi Sankar
63. 7.1 Introduction
64. 7.2 Categories of Anomaly
65. 7.3 Digital Video Tampering Detection
66. 7.4 Spatio-Temporal Video-Volume Conformation Inside Video
67. 7.5 Rotation-Invariant Attribute Modeling Motion Coherence (RIMOC)
68. 7.6 Learning Deep Depictions of Arrival and Motion
69. 7.7 Real-Time Troop Comportment Detection in Video
70. 7.8 Locality Sensitive Hashing Filters (LSHF)
71. 7.9 Frame Deletion Detection Method
72. 7.10 Challenges
73. 7.11 Related Works
74. 7.12 Proposed Model
75. 7.13 Anomaly Detection with One Class Support Vector Machines
76. 7.14 Results
77. 7.15 Conclusion & Future Scope
78. References
79. Chapter 8 Innovation in Multimedia Using IoT SystemsAbdullah Ayub Khan, Asif Ali
Laghari, Aftab Ahmed Shaikh, Zaffar Ahmed Shaikh and Awais Khan Jumanni
80. 8.1 Introduction
, 81. 8.2 IoT-Enabled Multimedia Frameworks
82. 8.3 IoT-Based Multimedia Applications
83. 8.4 Proposed Framework for IoT Multimedia Service Delivery
84. 8.5 Challenges and Limitations
85. 8.6 Open Research Issues
86. 8.7 Conclusion
87. Author Contributions
88. References
89. Chapter 9 Virtual Reality and Augmented Reality for EducationAwais Khan Jumani,
Waqas Ahmed Siddique, Asif Ali Laghari, Ahad Abro and Abdullah Ayub Khan
90. 9.1 Introduction of Virtual Reality
91. 9.2 Three Hundred and Sixty–Degree Images and Videos
92. 9.3 User Experience of Video With VR Devices
93. 9.4 Classification of Virtual Reality Systems
94. 9.5 3D Visualization
95. 9.6 Uses of Virtual Reality
96. 9.7 VR in Education
97. 9.8 Teacher Preparation and New Teacher Training
98. 9.9 Virtual Tools for Learning in the Real World
99. 9.10 Creating Additional Content
100. 9.11 Augmented Reality in Education
101. 9.12 How AR Works in Education
102. 9.13 How do We use Augmented Reality in the Education Field?
103. 9.14 AR Development in Education: Lead the Way of Innovation or Are Left Behind
104. 9.15 Augmented Reality Apps for Education
105. 9.16 Augmented Reality Technology in Education
106. 9.17 Open Research Issues and Future of VR and AR
107. 9.18 Conclusion
108. References
109. Chapter 10 Fog- or Edge-Based Multimedia Data Computing and Storage PoliciesPreety,
Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal
110. 10.1 Introduction
111. 10.2 Innovation Services
112. 10.3 Features of Fog and Edge Computing
113. 10.4 Concepts of Fog Computing
114. 10.5 Models/Architectures
115. 10.6 Comparison between Cloud Computing and Fog Computing
116. 10.7 Promise of Cloud and Fog Computing
117. 10.8 Contradiction in Cloud Computing and Fog Computing
118. 10.9 Legal Dimensions of Cloud Computing and Fog Computing
119. 10.10 Conclusion
120. References
121. Chapter 11 Role of Virtual Reality and Multimedia Computing in Industrial Automation
SystemUmesh Kumar Lilhore, Sarita Simaiya, Leeladhar Chourasia, Naresh Kumar
Trivedi and Abhineet Anand
122. 11.1 Introduction
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