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Despite DRL, there are still domain-specific challenges in complex wireless network virtualization requiring further study covered in this book; DNN architectures design with 5G network optimization, developing intelligent mechanism for automated management of virtual communications in SDNs, etc
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Dr. Pawan Singh is an Associate Professor in the Department of Computer Science & Engineering, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Lucknow, India. He has completed Ph.D. degree in Computer Science from Magadh University, Gaya. He has more than fifteen years of experience in research and teaching. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored various books. He has various National and international patents and some are granted. He holds contributions in IEEE, Elsevier, etc. repute journals. He is also a reviewer in various reputed journals. His current areas of interest include Computer Networks, Parallel Processing and Internet of Things.
Mr. Prateek Singhal is an Assistant Professor in the Department of Computer Engineering & Applications at GLA University, Mathura, Uttar Pradesh. He is pursuing a Ph.D. degree in Medical Imaging from the Maharishi University of Information Technology, Lucknow, India. He has more than four years of experience in research and teaching. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored a book on Cloud Computing. He has various National and international patents and some are granted. He holds contributions to IEEE, Elsevier, etc. reputed journals. He is on the team of the research advisory member in his present institute. His current areas of interest include Image Processing, Medical Imaging, Human Computation Interface, Neuro-Computing, Internet of Things.
Dr. Pramod Kumar Mishra is working as a Head and Professor in the Department of Computer Science & Engineering at Banaras Hindu University, Varanasi. He has completed Ph.D. degree on A study of efficient shortest path algorithms for serial and parallel computers from APS University, Rewa, India. He has more than Thirty years of experience in research and teaching. He has received various Awards and fellowships from the good repute organizations. He has also received various grants from national and international government bodies/Agency. He has published several research articles in SCI/SCIE/Scopus journals and conferences of high repute. He has also authored a book on Cloud Computing. He has various National and international patents and some are granted. He holds contributions in IEEE, Elsevier, etc. reputed journal. He is in the team of the research advisory member in his present institute. His current areas of interest include AI and Machine Learning Algorithms, Data Analytics, Parallel Computing, High-Performance Clusters, Algorithm Engineering (AE), High-Performance AE, Parallel Computation, and Computational complexity.
Dr. Avimanyou Vatsa is working as an assistant professor in the department of computer science, Fairleigh Dickinson University – Teaneck. He also worked as an assistant professor at West Texas A&M University, a teaching & research assistant at the University of Missouri, Columbia, and an assistant professor for more than ten years in several engineering colleges and a university in India. Also, he worked as a software engineer in the industry. He always motivates and inspires students with a statement: “Nothing is impossible, just put your hard work and sincere effort persistently toward your goal.
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Paperback. Zustand: new. Paperback. We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective. Despite DRL, there are still domain-specific challenges in complex wireless network virtualization requiring further study covered in this book; DNN architectures design with 5G network optimization, developing intelligent mechanism for automated management of virtual communications in SDNs, etc Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9781032426396
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Paperback. Zustand: new. Paperback. We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective. Despite DRL, there are still domain-specific challenges in complex wireless network virtualization requiring further study covered in this book; DNN architectures design with 5G network optimization, developing intelligent mechanism for automated management of virtual communications in SDNs, etc Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781032426396
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Paperback. Zustand: new. Paperback. We have seen a sharp increase in the development of data transfer techniques in the networking industry over the past few years. We can see that the photos are assisting clinicians in detecting infection in patients even in the current COVID-19 pandemic condition. With the aid of ML/AI, medical imaging, such as lung X-rays for COVID-19 infection, is crucial in the early detection of many diseases. We also learned that in the COVID-19 scenario, both wired and wireless networking are improved for data transfer but have network congestion. An intriguing concept that has the ability to reduce spectrum congestion and continuously offer new network services is providing wireless network virtualization. The degree of virtualization and resource sharing varies between the paradigms. Each paradigm has both technical and non-technical issues that need to be handled before wireless virtualization becomes a common technology. For wireless network virtualization to be successful, these issues need careful design and evaluation. Future wireless network architecture must adhere to a number of Quality of Service (QoS) requirements. Virtualization has been extended to wireless networks as well as conventional ones. By enabling multi-tenancy and tailored services with a wider range of carrier frequencies, it improves efficiency and utilization. In the IoT environment, wireless users are heterogeneous, and the network state is dynamic, making network control problems extremely difficult to solve as dimensionality and computational complexity keep rising quickly. Deep Reinforcement Learning (DRL) has been developed by the use of Deep Neural Networks (DNNs) as a potential approach to solve high-dimensional and continuous control issues effectively. Deep Reinforcement Learning techniques provide great potential in IoT, edge and SDN scenarios and are used in heterogeneous networks for IoT-based management on the QoS required by each Software Defined Network (SDN) service. While DRL has shown great potential to solve emerging problems in complex wireless network virtualization, there are still domain-specific challenges that require further study, including the design of adequate DNN architectures with 5G network optimization issues, resource discovery and allocation, developing intelligent mechanisms that allow the automated and dynamic management of the virtual communications established in the SDNs which is considered as research perspective. Despite DRL, there are still domain-specific challenges in complex wireless network virtualization requiring further study covered in this book; DNN architectures design with 5G network optimization, developing intelligent mechanism for automated management of virtual communications in SDNs, etc Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781032426396
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