Reshaping Intelligent Business and Industry (eBook)

Convergence of AI and IoT at the Cutting Edge

Neeraj Dahiya, Surjeet Dalal, Vivek Jaglan, Deepika Koundal, Dac-Nhuong Le (Herausgeber)

eBook Download: EPUB

2024
1198 Seiten
Wiley (Verlag)
978-1-119-90518-9 (ISBN)

Infos

The convergence of Artif?icial Intelligence (AI) and Internet of Things (IoT) is reshaping the way industries, businesses, and economies function; the 34 chapters in this collection show how the full potential of these technologies is being enabled to create intelligent machines that simulate smart behavior and support decision-making with little or no human interference, thereby providing startling organizational efficiencies.

Readers will discover that in Reshaping Intelligent Business and Industry:

• The book unpacks the two superpowers of innovation, AI and IoT, and explains how they connect to better communicate and exchange information about online activities;
• How the center and the network's edge generate predictive analytics or anomaly alerts;
• The meaning of AI at the edge and IoT networks.
• How bandwidth is reduced and privacy and security are enhanced;
• How AI applications increase operating efficiency, spawn new products and services, and enhance risk management;
• How AI and IoT create 'intelligent' devices and how new AI technology enables IoT to reach its full potential;
• Analyzes AIOT platforms and the handling of personal information for shared frameworks that remain sensitive to customers' privacy while effectively utilizing data.

Audience
This book will appeal to all business and organization leaders, entrepreneurs, policymakers, and economists, as well as scientists, engineers, and students working in artificial intelligence, software engineering, and information technology.

Surjeet Dalal, PhD, is an associate professor in the Depar?tment of Computer Science & Engineering at SR?M Universit?y, Har??yana, India. His cur?rent research areas are ar??tif??icial intelligence, cloud computing, and IoT. He has published t?wo cloud computing books and published 20+ papers in inter??national jour??nals.

Neeraj Dahiya, PhD, is an assistant professor in the Depar?tment of Computer Science & Engineering, SR?M University, Har?yana, India. His research areas include artificial intelligence, machine learning, speech processing, etc. Along with publishing research papers, Dahiya has four patents in artif?icial intelligence and machine lear?ning.

Vivek Jaglan, PhD, is a professor at the DPG Institute of Technology and Management, Haryana, India. His research areas include artificial intelligence, neural networks & fuzzy logic, and IoT. Jaglan has published one book on cloud computing, 30+ papers in national/international journals, and 40+ papers for national and international conferences.

Deepika Koundal, PhD, is an assistant professor at the University of Petroleum and Energy St?udies,Dehradun, India. Her areas of interest are ar???tif???icial intelligence, biomedical imaging and signals, image processing, etc. She has published two books and 40+ research papers in inter??national jour??nals.

Dac-Nhuong Le, PhD, obtained his doctorate in computer science from Vietnam National University, Vietnam in 2015. He is deputy head of the Faculty of Information Technology, Haiphong University, Vietnam. His area of research includes evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT, and image processing in biomedicine. He has over 50 publications and edited/authored many computer science books with the Wiley-Scrivener imprint.

Textauszug

List of Figures

1. 1.1 Components of artificial intelligence learning.
2. 1.2 Levels of artificial intelligence.
3. 1.3 AI classification on the basis of functionality.
4. 1.4 Components of artificial intelligence system.
5. 1.5 Subsets of artificial intelligence.
6. 1.6 Applications of artificial intelligence.
7. 2.1 Deep learning.
8. 2.2 Deep learning process.
9. 2.3 Convolutional neural network model.
10. 2.4 Edge computing.
11. 2.5 Sliding window.
12. 2.6 Region-based network (R-CNN).
13. 2.7 Fast R-CNN.
14. 2.8 Faster R-CNN.
15. 2.9 Single shot detector (SSD) framework.
16. 2.10 MobileNet SSD.
17. 2.11 You only look once (YOLO).
18. 2.12 Object detection performance.
19. 2.13 GOTURN.
20. 2.14 ROLO.
21. 2.15 Multiple object tracking (MOT).
22. 2.16 Tracktor++.
23. 2.17 Joint detection and embedding (JDE).
24. 2.18 Functional model.
25. 3.1 Search engine optimization.
26. 3.2 Web analytics.
27. 3.3 Web architecture.
28. 3.4 SEO testing.
29. 3.5 Yoast SEO testing.
30. 3.6 Google Analytics testing.
31. 3.7 Hit test.
32. 3.8 Structured data testing.
33. 3.9 Keyword research using KWFinder-1.
34. 3.10 Keyword research using KWFinder-2.
35. 3.11 Keyword research using SEOMoz.
36. 3.12 Adding meta details.
37. 3.13 Suggestion by Yoast SEO.
38. 3.14 Implementing GA code.
39. 3.15 Connect GA to data studio.
40. 3.16 Choosing the view.
41. 3.17 Adding data to report.
42. 3.18 Ranking result shown at the top of the SERP.
43. 3.19 Google Analytics capturing real-time data.
44. 4.1 The structured view of XAI.
45. 4.2 The need for XAI.
46. 4.3 Local explanation model.
47. 4.4 Global explanation model.
48. 6.1 Steps involved in predictive analytics.
49. 6.2 Process of supervised learning.
50. 6.3 Classification of supervised learning.
51. 6.4 KNN algorithm process.
52. 6.5 Logistic regression - sigmoid function.
53. 6.6 Unsupervised learning process.
54. 7.1 Block diagram of our proposed methodology.
55. 7.2 Correlation between age of vehicle and price.
56. 7.3 Correlation between fuel type and price.
57. 7.4 Correlation between transmission and price.
58. 7.5 Average vehicle price by specific brand.
59. 7.6 Fitting the model.
60. 7.7 Calculating the R2 Score.
61. 8.1 Physical objects in IoT concept.
62. 8.2 State of physical devices over time.
63. 8.3 Three-layer conventional architecture for IoT.
64. 8.4 Five-layer conventional architecture for IoT.
65. 8.5 Some of the application domains of IoT.
66. 8.6 IoT architecture for smart cars.
67. 8.7 Architecture of smart traffic management system.
68. 8.8 Blockchain-based healthcare architecture of smart healthcare.
69. 8.9 A conventional smart farming architecture system.
70. 8.10 Security surveillance architecture.
71. 9.1 Automobile layout with sensors.
72. 9.2 Model of environmental monitoring.
73. 9.3 IoT medical devices.
74. 9.4 IoT home automation.
75. 9.5 IoT energy management.
76. 9.6 IoT in agriculture.
77. 9.7 IoT-based water supply system.
78. 9.8 IoT application graph.
79. 9.9 The rise in the use of home voice controllers over the years.
80. 9.10 Echo Plus voice controller.
81. 9.11 Doorbell cam.
82. 9.12 Philips Hue bulbs and lighting system.
83. 9.13 Smartwatches.
84. 9.14 Ring doorbell.
85. 9.15 Wemo Insight Smart Plug.
86. 9.16 Logitech Harmony universal remote.
87. 9.17 Photon Wi-Fi with headers.
88. 11.1 Hardware setup.
89. 11.2 Dataset for crops.
90. 11.3 Crop prediction system.
91. 11.4 Connectivity of the workflow.
92. 11.5 Phases of workflow.
93. 11.6 Overall view of the mobile application.
94. 11.7 Live stock.
95. 11.8 Weather monitoring.
96. 11.9 Irrigation control.
97. 11.10 Animal intrusion check module.
98. 11.11 Crop news.
99. 11.12 Drone control device.
100. 11.13 Images from Firebase on the mobile app.
101. 11.14 Disease prediction.
102. 11.15 Nutrient values and content in fertilizer.
103. 11.16 Crop guidance.
104. 11.17 Crop price.
105. 12.1 Wi-Fi communication development board.
106. 12.2 Remote access of IoT-based smart home appliances.
107. 12.3 Source code uses “auth token” in Arduino.
108. 12.4 Source code uses digital pin D2, D3, D4 and D5 of node MCU.
109. 12.5 Bluetooth communication development board with connected LED.
110. 12.6 Bluetooth communication development board with connected relay.
111. 12.7 Bluetooth communication development board with connected bulb via relay.
112. 12.8 Connect Output Pin of IR Receiver with pin 3 of Arduino Nano.
113. 12.9 Connected pin 9 of Arduino Nano to relay.
114. 12.10 Serial monitor showing the code of button 1 of IR Remote.
115. 12.11 Sending message to LCD.
116. 12.12 Jumper wires connection for LCD display.
117. 12.13 Message displayed on LCD.
118. 13.1 Three-layer architecture of IoT.
119. 13.2 Five-layer architecture of IoT.
120. 13.3 Seven crucial design principles.
121. 13.4 Five layers of technology for IoT decision framework.
122. 13.5 Applications of IoT.
123. 13.6 IoT in smart grid cities.
124. 13.7 IoT in smart home appliances.
125. 13.8 IoT in healthcare.
126. 13.9 IoT in wearables.
127. 14.1 IoT features.
128. 14.2 Data protocols for the IoT.
129. 14.3 Network protocols for the IoT.
130. 14.4 IoT deployment architecture.
131. 14.5 Levels of interoperability model.
132. 14.6 Novel IoT interoperability architecture.
133. 15.1 IoT architecture.
134. 15.2 SYN flood distributed denial-of-service (DDoS) attack.
135. 15.3 HTTP-GET flood DDoS attack.
136. 15.4 SSL/TLS flood DDoS attack.
137. 16.1 Smart city concept.
138. 16.2 Advantages of smart cities.
139. 16.3 IoT-enabled devices.
140. 16.4 Applications of IoT.
141. 16.5 IoT analytics.
142. 16.6 Types of AI.
143. 16.7 Puzzle image.
144. 16.8 Sophia from Hanson Robotics.
145. 16.9 Kismet robot made at Massachusetts Institute of Technology.
146. 16.10 Human evolution from ape to robot.
147. 16.11 Applications of AI.
148. 16.12 Pros and Cons of AI.
149. 16.13 AIoT-based robot used to clean lakes and rivers.
150. 16.14 Self-driving cars on the road.
151. 16.15 Outdoor air pollution death rate, 2017.
152. 17.1 AIoT paradigms.
153. 17.2 General architecture of AIoT-based system.
154. 17.3 The six key smart city domains.
155. 18.1 Interrelationship between AI, ML and deep learning concepts.
156. 18.2 Framework of zkLedger system with connection between three entities.
157. 19.1 Application areas of internet of things.
158. 19.2 Architecture of IoT-based SWM.
159. 19.3 Architecture of the K-Query SWM.
160. 19.4 Architecture of SWC as a service.
161. 19.5 Transmission pattern of SBM.
162. 19.6 Functional flow diagram of the smart bin mechanism.
163. 19.7 Fundamental architecture of FES.
164. 20.1 Basic architecture of IoT systems.
165. 20.2 Development of “things” of IoT.
166. 20.3 Development of IoT network.
167. 20.4 Working with IoT data.
168. 20.5 Securing IoT device.
169. 20.6 Convergence of AI and IoT.
170. 21.1 Purposed research model.
171. 21.2 SEM diagram with path coefficient and t-values.
172. 22.1 Basic architecture of IoT.
173. 22.2 A sensor to measure glucose levels.
174. 22.3 Setup for getting patient data from the sensor to the doctor.
175. 22.4 Wearable blood pressure sensor.
176. 22.5 Blood pressure sensor fixed to arm.
177. 22.6 The wireless heart rate monitoring device.
178. 22.7 Placement of wireless heart rate monitor.
179. 22.8 A wireless plaster for temperature monitoring.
180. 22.9 Depression monitoring equipment.
181. 22.10 Smart asthma inhaler.
182. 22.11 Smart contact lens.
183. 22.12 Robotic surgery.
184. 22.13 The machine learning algorithms used in AI.
185. 22.14 AI with IoT.
186. 23.1 Scenario of application of internet of things market.
187. 23.2 Impact of AIoT in healthcare transformation.
188. 23.3 Architecture of AIoT.
189. 23.4 AIoT...

Produkt-Details

• Erscheint lt. Verlag: 6.9.2024
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: AIOT • API • Artificial Intelligence • Artificial Intelligence of Things • ICMP • Internet Connected Objects • internet of things • IOT • IoT Devices • IP • machine learning • Radio Frequency • Sensor Actuator Network • Small to Medium Enterprise • TCP
• ISBN-10: 1-119-90518-4 / 1119905184
• ISBN-13: 978-1-119-90518-9 / 9781119905189