J p tanwar (55 resultados)

Condición: New. pp. 312 Illus.

Condición: New. pp. 312.

Condición: New. pp. 312.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New.

Condición: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

paperback. Condición: Acceptable. The textblock and binding are heavily creased. The copy shows minor external wear, but is otherwise in clean condition.

Condición: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Hardcover. Condición: new. Hardcover. This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network. This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condición: Neu. Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019) | Pradeep Kumar Singh (u. a.) | Taschenbuch | xiv | Englisch | 2020 | Springer | EAN 9789811533686 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condición: Neu. Proceedings of Second International Conference on Computing, Communications, and Cyber-Security | IC4S 2020 | Pradeep Kumar Singh (u. a.) | Taschenbuch | xxviii | Englisch | 2021 | Springer | EAN 9789811607325 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: New. In English.

Condición: New. In English.

Taschenbuch. Condición: Neu. Proceedings of Third International Conference on Computing, Communications, and Cyber-Security | IC4S 2021 | Pradeep Kumar Singh (u. a.) | Taschenbuch | xxviii | Englisch | 2022 | Springer | EAN 9789811911415 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book features selected research papers presented at the Second International Conference on Computing, Communications, and Cyber-Security (IC4S 2020), organized in Krishna Engineering College (KEC), Ghaziabad, India, along with Academic Associates; Southern Federal University, Russia; IAC Educational, India; and ITS Mohan Nagar, Ghaziabad, India during 3-4 October 2020. It includes innovative work from researchers, leading innovators, and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues. …

Condición: New. In English.

Taschenbuch. Condición: Neu. Deep Learning Based Solutions for Vehicular Adhoc Networks | Jitendra Bhatia (u. a.) | Taschenbuch | xv | Englisch | 2026 | Springer | EAN 9789819651924 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: New.

Condición: New.

Paperback. Condición: Brand New. 932 pages. 9.25x6.10x1.84 inches. In Stock.

Paperback. Condición: Brand New. 1075 pages. 9.25x6.10x2.11 inches. In Stock.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network.…

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network.…

Paperback. Condición: Brand New. 939 pages. 9.25x6.10x1.85 inches. In Stock.

Condición: New.

Hardcover. Condición: new. Hardcover. This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. It begins with discussions on how the transportation system has been converted into Intelligent Transportation System (ITS). The use of VANETs is increasing in the development of ITS to enhance road safety, traffic efficiency, and driver comfort. However, the dynamic nature of vehicular environments and the high mobility of vehicles pose significant challenges to designing and implementing VANETs and ensuring reliable and efficient communication. Deep learning, a subset of machine learning, has the potential to revolutionize vehicular ad hoc networks (VANETs) to enable various applications such as traffic management, collision avoidance, and infotainment. DL has demonstrated great potential in addressing various challenges involved in VANETs by leveraging its ability to learn from vast data and make accurate predictions. It reviews the state-of-the-art DL-based approaches for various applications in VANETs, including routing, congestion control, autonomous driving, and security. In addition, this book provides a comprehensive analysis of these approaches' advantages and limitations and discusses their future research directions. The study in this book shows that DL-based techniques can significantly improve the performance and reliability of VANETs. Still, in-depth research is required to address the challenges of deploying these methods in real-world scenarios. Finally, the book discusses the potential of DL-based VANETs in supporting other emerging technologies, such as autonomous driving and smart cities. It explores the simulation/emulation tools for practical exposure to the vehicular ad hoc network. This book provides a holistic and comprehensive approach to deep learning for vehicular ad hoc networks (VANETs), covering various aspects such as applications, agency involvement, and potential ethical and legal issues. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…