书名：Convergence of deep learning and internet of things
责任者：[] T. Kavitha (New Horizon College of Engineering (Autonomous) | Yanhui Guo (University of Illinois | USA) | Deepak Jain (Chongqing University of Posts and Telecommunications | China) | India & Visvesvaraya Technological University | India) | G. Senbagavalli (AMC Engineering College | Visvesvaraya Technological University | India) | Deepika Koundal (University of Petroleum and Energy Studies | Dehradun | India)
ISBN\ISSN：9781668462751,9781668462768
出版时间：2023
出版社：IGI Global,
分类号：自动化技术、计算机技术
页数：xxii, 349 pages :
摘要
Digital technology has enabled a number of internet-enabled devices that generate huge volumes of data from different systems. This large amount of heterogeneous data requires efficient data collection, processing, and analytical methods. Deep Learning is one of the latest efficient and feasible solutions that enable smart devices to function independently with a decision-making support system.
Convergence of Deep Learning and Internet of Things: Computing and Technology contributes to technology and methodology perspectives in the incorporation of deep learning approaches in solving a wide range of issues in the IoT domain to identify, optimize, predict, forecast, and control emerging IoT systems. Covering topics such as data quality, edge computing, and attach detection and prediction, this premier reference source is a comprehensive resource for electricians, communications specialists, mechanical engineers, civil engineers, computer scientists, students and educators of higher education, librarians, researchers, and academicians.
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目录
Preface xvi
Acknowledgment xxii
Chapter 1 Intelligent Devices, Device Management, and Device Security for Cloud Platforms 1
Chapter 2 Intelligent Broker Design for IoT Using a Multi-Cloud Environment 23
Chapter 3 Deep Learning-Based Intelligent Sensing in IoT 42
Chapter 4 Deep Learning-Enabled Edge Computing and IoT 71
Chapter 5 Distributed Deep Learning for IoT 96
Chapter 6 Approaches for Detecting and Predicting Attacks Based on Deep and Reinforcement Learning to Improve Information Security 113
Chapter 7 Enhancing Quality of Service in Internet of Things: Deep Learning Approach and Its Challenges 131
Chapter 8 Edge Computing in Intelligent IoT 157
Chapter 9 Edge AI-Based Crowd Counting Application for Public Transport Stops 182
Chapter 10 Patient Behavioral Analysis With Smart Healthcare and IoT 206
Chapter 11 Deep Learning Neural Networks for Online Monitoring of the Combustion Process From Flame Colour in Thermal Power Plants 224
Chapter 12 Analysis of Political and Ideological Systems in Education With Lightweight Deep Learning 245
Chapter 13 Comparative Analysis of Feature Selection Methods for Detection of Android Malware 263
Chapter 14 Applications of Internet of Things With Deep Learning 285
Compilation of References 308
About the Contributors 337
Index 347
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