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Artificial intelligence applications...

Elhoseny, Mohamed.

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Artificial intelligence applications for smart societies = recent advances /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence applications for smart societies/ edited by Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset.
其他題名:	recent advances /
其他作者:	Elhoseny, Mohamed.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xii, 251 p. :ill., digital ;24 cm.
內容註:	Chapter 1-Simulation, deployment, and testbed platforms for WSNs in Smart Cities. -- Chapter 2-Accessibility, resilience, and security of IoT infrastructures for Smart Cities. -- Chapter 3-Real-time information systems for intelligent transportation systems in Smart Cities. -- Chapter 4-Mobile apps and WSN applications integrated in Smart Cities. -- Chapter 5-AI techniques for devices and embedded systems in Smart Cities. -- Chapter 6-Data processing. -- Chapter 7-Self-learning. -- Chapter 8-Computing and sensing infrastructures. -- Chapter 9-Evolutionary algorithms for data mining in IoT based smart societies. -- Chapter 10-Cloud computing-based evolutionary algorithms for IoT. -- Chapter 11-Artificial intelligence algorithms for processing medical data in IoT. -- Chapter 12-Machine learning for data processing in IoT based smart societies. -- Chapter 13-Intelligent face recognition system using neural learning for smart security in IoT. -- Chapter 14-Distributed attack detection using a deep neural network approach for IoT. -- Chapter 15-Modeling and optimizing features selection in data system-based social IoT. -- Chapter 16-Crowdsourcing systems and services for smart cities. -- Chapter 17-Network communications technology for smart cities. -- Chapter 18-Human mobility models for smart cities. -- Chapter 19-Application of pervasive and mobile computing technology for smart cities. -- Chapter 20-Safety, security, privacy and trust in applications and services for smart cities. -- Chapter 21-Fault tolerance, reliability and survivability in smart cities. -- Chapter 22-Multi-sourced heterogeneous data acquisition and fusion for smart cities. -- Chapter 23-Case studies and test beds.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-63068-3
ISBN:	9783030630683

Artificial intelligence applications for smart societies = recent advances /
Artificial intelligence applications for smart societiesrecent advances /[electronic resource] :edited by Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset. - Cham :Springer International Publishing :2021. - xii, 251 p. :ill., digital ;24 cm. - Studies in distributed intelligence,2662-3706. - Studies in distributed intelligence..

Chapter 1-Simulation, deployment, and testbed platforms for WSNs in Smart Cities. -- Chapter 2-Accessibility, resilience, and security of IoT infrastructures for Smart Cities. -- Chapter 3-Real-time information systems for intelligent transportation systems in Smart Cities. -- Chapter 4-Mobile apps and WSN applications integrated in Smart Cities. -- Chapter 5-AI techniques for devices and embedded systems in Smart Cities. -- Chapter 6-Data processing. -- Chapter 7-Self-learning. -- Chapter 8-Computing and sensing infrastructures. -- Chapter 9-Evolutionary algorithms for data mining in IoT based smart societies. -- Chapter 10-Cloud computing-based evolutionary algorithms for IoT. -- Chapter 11-Artificial intelligence algorithms for processing medical data in IoT. -- Chapter 12-Machine learning for data processing in IoT based smart societies. -- Chapter 13-Intelligent face recognition system using neural learning for smart security in IoT. -- Chapter 14-Distributed attack detection using a deep neural network approach for IoT. -- Chapter 15-Modeling and optimizing features selection in data system-based social IoT. -- Chapter 16-Crowdsourcing systems and services for smart cities. -- Chapter 17-Network communications technology for smart cities. -- Chapter 18-Human mobility models for smart cities. -- Chapter 19-Application of pervasive and mobile computing technology for smart cities. -- Chapter 20-Safety, security, privacy and trust in applications and services for smart cities. -- Chapter 21-Fault tolerance, reliability and survivability in smart cities. -- Chapter 22-Multi-sourced heterogeneous data acquisition and fusion for smart cities. -- Chapter 23-Case studies and test beds.

This volume discusses recent advances in Artificial Intelligence (AI) applications in smart, internet-connected societies, highlighting three key focus areas. The first focus is on intelligent sensing applications. This section details the integration of Wireless Sensing Networks (WSN) and the use of intelligent platforms for WSN applications in urban infrastructures, and discusses AI techniques on hardware and software systems such as machine learning, pattern recognition, expert systems, neural networks, genetic algorithms, and intelligent control in transportation and communications systems. The second focus is on AI-based Internet of Things (IoT) systems, which addresses applications in traffic management, medical health, smart homes and energy. Readers will also learn about how AI can extract useful information from Big Data in IoT systems. The third focus is on crowdsourcing (CS) and computing for smart cities. this section discusses how CS via GPS devices, GIS tools, traffic cameras, smart cards, smart phones and road deceleration devices enables citizens to collect and share data to make cities smart, and how these data can be applied to address urban issues including pollution, traffic congestion, public safety and increased energy consumption. This book will of interest to academics, researchers and students studying AI, cloud computing, IoT and crowdsourcing in urban applications.

ISBN: 9783030630683

Standard No.: 10.1007/978-3-030-63068-3doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q337 / .A785 2020

Dewey Class. No.: 006.3

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520 $a This volume discusses recent advances in Artificial Intelligence (AI) applications in smart, internet-connected societies, highlighting three key focus areas. The first focus is on intelligent sensing applications. This section details the integration of Wireless Sensing Networks (WSN) and the use of intelligent platforms for WSN applications in urban infrastructures, and discusses AI techniques on hardware and software systems such as machine learning, pattern recognition, expert systems, neural networks, genetic algorithms, and intelligent control in transportation and communications systems. The second focus is on AI-based Internet of Things (IoT) systems, which addresses applications in traffic management, medical health, smart homes and energy. Readers will also learn about how AI can extract useful information from Big Data in IoT systems. The third focus is on crowdsourcing (CS) and computing for smart cities. this section discusses how CS via GPS devices, GIS tools, traffic cameras, smart cards, smart phones and road deceleration devices enables citizens to collect and share data to make cities smart, and how these data can be applied to address urban issues including pollution, traffic congestion, public safety and increased energy consumption. This book will of interest to academics, researchers and students studying AI, cloud computing, IoT and crowdsourcing in urban applications.
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