Artificial intelligence edge computing (41 Ergebnisse)
Sprache: Englisch
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Taschenbuch. Zustand: Neu. Edge AI | Convergence of Edge Computing and Artificial Intelligence | Xiaofei Wang (u. a.) | Taschenbuch | xvii | Englisch | 2021 | Springer | EAN 9789811561887 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anb…ieter: preigu.
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Taschenbuch. Zustand: Neu. Artificial Intelligence for Cloud and Edge Computing | Sanjay Misra (u. a.) | Taschenbuch | Internet of Things | xiv | Englisch | 2023 | Springer | EAN 9783030808235 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com…| Anbieter: preigu.
Sprache: Englisch
Verlag: CRC Press, 2023
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Taschenbuch. Zustand: Neu. Neuware - Artificial Intelligence and Machine Learning for Predictive and Analytical Rendering in Edge Computing focuses on the role of AI and machine learning as it impacts and works alongside Edge Computing. Sections cover the growing number of devices and applications in diversified domains of indus…try, including gaming, speech recognition, medical diagnostics, robotics and computer vision and how they are being driven by Big Data, Artificial Intelligence, Machine Learning and distributed computing, may it be Cloud Computing or the evolving Fog and Edge Computing paradigms. Challenges covered include remote storage and computing, bandwidth overload due to transportation of data from End nodes to Cloud leading in latency issues, security issues in transporting sensitive medical and financial information across larger gaps in points of data generation and computing, as well as design features of Edge nodes to store and run AI/ML algorithms for effective rendering.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - As an important enabler for changing people's lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative t…echnique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e)using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
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Zustand: New. InhaltsverzeichnisPart 1: AI and Machine Learning 1. Artificial Intelligence 2. Machine Learning 3. Regression Analysis 4. Bayesian Statistics 5. Learning Theory 6. Supervised Learning 7. Unsupervised Learning 8. Reinforcement .
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - As an important enabler for changing people's lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most represent…ative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e)using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning… and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cl…oud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.
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Taschenbuch. Zustand: Neu. Artificial Intelligence for Edge Computing | Mudhakar Srivatsa (u. a.) | Taschenbuch | xiv | Englisch | 2024 | Springer | EAN 9783031407895 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. Artificial Intelligence and Edge Computing for Sustainable Ocean Health | Debashis De (u. a.) | Taschenbuch | The Springer Series in Applied Machine Learning | xiv | Englisch | 2025 | Springer | EAN 9783031646447 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidel…berg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - It is undeniable that the recent revival of artificial intelligence (AI) has significantly changed the landscape of science in many application domains, ranging from health to defense and from conversational interfaces to autonomous cars. With terms…such as 'Google Home', 'Alexa', and 'ChatGPT' becoming household names, the pervasive societal impact of AI is clear. Advances in AI promise a revolution in our interaction with the physical world, a domain where computational intelligence has always been envisioned as a transformative force toward a better tomorrow. Depending on the application family, this domain is often referred to as Ubiquitous Computing, Cyber-Physical Computing, or the Internet of Things. The underlying vision is driven by the proliferation of cheap embedded computing hardware that can be integrated easily into myriads of everyday devices from consumer electronics, such as personal wearables and smart household appliances, to city infrastructure and industrial process control systems. One common trait across these applications is that the data that the application operates on come directly (typically via sensors) from the physical world. Thus, from the perspective of communication network infrastructure, the data originate at the network edge. From a performance standpoint, there is an argument to be made that such data should be processed at the point of collection. Hence, a need arises for Edge AI -- a genre of AI where the inference, and sometimes even the training, are performed at the point of need, meaning at the edge where the data originate.The book is broken down into three parts: core problems, distributed problems, and other cross-cutting issues. It explores the challenges arising in Edge AI contexts. Some of these challenges (such as neural network model reduction to fit resource-constrained hardware) are unique to the edge environment. They need a novel category of solutions that do not parallel more typical concerns in mainstream AI. Others are adaptations of mainstream AI challenges to the edge space. An example is overcoming the cost of data labeling. The labeling problem is pervasive, but its solution in the IoT application context is different from other contexts. This book is not a survey of the state of the art. With thousands of publications appearing in AI every year, such a survey is doomed to be incomplete on arrival. It is also not a comprehensive coverage of all the problems in the space of Edge AI. Different applications pose different challenges, and a more comprehensive coverage should be more application specific. Instead, this book covers some of the more endemic challenges across the range of IoT/CPS applications. To offer coverage in some depth, we opt to cover mainly one or a few representative solutions for each of these endemic challenges in sufficient detail, rather that broadly touching on all relevant prior work. The underlying philosophy is one of illustrating by example. The solutions are curated to offer insight into a way of thinking that characterizes Edge AI research and distinguishes its solutions from their more mainstream counterparts.
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial Intelligence and Edge Computing for Sustainable Ocean Health explores the transformative role of AI and edge computing in preserving and enhancing ocean health. The growing influence of Artificial Intelligence (AI), along with the Internet… of Things (IoT) in generating wide coverage of sensor networks, and Edge Computing (EC) has paved the way for investigation of underwater as well as massive marine data, thereby generating huge potential for credible research opportunities for these domains. This book's journey begins with a broad overview of Artificial Intelligence for Sustainable Ocean Health, setting the foundation for understanding AI's potential in marine conservation. The subsequent chapter, Role of Artificial Intelligence and Technologies in Improving Ocean Health in Promoting Tourism, illustrates the synergy between technological advancements and sustainable tourism practices, demonstrating how AI can enhance the attractiveness and preservation of marine destinations. The identification, restoration, and monitoring of marine resources along with the utilization of technology continues in Utilization of Underwater Wireless Sensor Network through Supervising a Random Network Environment in the Ocean Environment has been extensively dealt with. The technical challenges of underwater imaging, essential for accurate data collection and analysis has been discussed.The importance of Explainable AI is discussed in chapters like Sustainable Development Goal 14: Explainable AI (XAI) for Ocean Health, Explainable AI (XAI) for Ocean Health: Exploring the Role of Explainable AI in Enhancing Ocean Health, and A Comprehensive Study of AI (XAI) for Ocean Health Monitoring, which emphasize transparency and trust in AI systems. Further, Revolutionizing Internet of Underwater Things with Federated Learning, Underwater Drone, Underwater Imagery with AI/ML and IoT in ROV Technology and Ocean Cleanup has been demonstrated using innovative approaches to addressing underwater challenges. The book also includes a Review on the Optics and Photonics in Environmental Sustainability, focusing on the role of optics in marine conservation. Security issues are tackled in Intelligent Hash Function Based Key-Exchange Scheme for Ocean Underwater Data Transmission, and the overarching potential of AI in marine resource management is discussed in Artificial Intelligence as Key-enabler for Safeguarding the Marine Resources.
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - It is undeniable that the recent revival of artificial intelligence (AI) has significantly changed the landscape of science in many application domains, ranging from health to defense and from conversational interfaces to autonomous cars. With terms such as… 'Google Home', 'Alexa', and 'ChatGPT' becoming household names, the pervasive societal impact of AI is clear. Advances in AI promise a revolution in our interaction with the physical world, a domain where computational intelligence has always been envisioned as a transformative force toward a better tomorrow. Depending on the application family, this domain is often referred to as Ubiquitous Computing, Cyber-Physical Computing, or the Internet of Things. The underlying vision is driven by the proliferation of cheap embedded computing hardware that can be integrated easily into myriads of everyday devices from consumer electronics, such as personal wearables and smart household appliances, to city infrastructure and industrial process control systems. One common trait across these applications is that the data that the application operates on come directly (typically via sensors) from the physical world. Thus, from the perspective of communication network infrastructure, the data originate at the network edge. From a performance standpoint, there is an argument to be made that such data should be processed at the point of collection. Hence, a need arises for Edge AI -- a genre of AI where the inference, and sometimes even the training, are performed at the point of need, meaning at the edge where the data originate.The book is broken down into three parts: core problems, distributed problems, and other cross-cutting issues. It explores the challenges arising in Edge AI contexts. Some of these challenges (such as neural network model reduction to fit resource-constrained hardware) are unique to the edge environment. They need a novel category of solutions that do not parallel more typical concerns in mainstream AI. Others are adaptations of mainstream AI challenges to the edge space. An example is overcoming the cost of data labeling. The labeling problem is pervasive, but its solution in the IoT application context is different from other contexts. This book is not a survey of the state of the art. With thousands of publications appearing in AI every year, such a survey is doomed to be incomplete on arrival. It is also not a comprehensive coverage of all the problems in the space of Edge AI. Different applications pose different challenges, and a more comprehensive coverage should be more application specific. Instead, this book covers some of the more endemic challenges across the range of IoT/CPS applications. To offer coverage in some depth, we opt to cover mainly one or a few representative solutions for each of these endemic challenges in sufficient detail, rather that broadly touching on all relevant prior work. The underlying philosophy is one of illustrating by example. The solutions are curated to offer insight into a way of thinking that characterizes Edge AI research and distinguishes its solutions from their more mainstream counterparts.
