Xin Yuan – författare

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9 produkter

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9 produkter

Graph Learning Techniques
AvBaoling Shan,Xin Yuanm. fl.
Häftad, Engelska, 2025
783 kr
Skickas inom 10-15 vardagar
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
Graph Learning Techniques
AvBaoling Shan,Xin Yuanm. fl.
Inbunden, Engelska, 2025
1 474 kr
Skickas inom 10-15 vardagar
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation. It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning. This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
Graph Learning Techniques
AvEryk Dutkiewicz,Ren Ping Lium. fl.
E-bok
PDF, Engelska, 2025
925 kr
Läs direkt efter köp
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.
It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.
This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
Graph Learning Techniques
AvEryk Dutkiewicz,Ren Ping Lium. fl.
E-bok
Engelska, 2025
922 kr
Läs direkt efter köp
This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.
It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.
This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.
Artificial Intelligence for Unmanned Aerial Vehicles
Sensing, Communication, and Computing
AvShuyan Hu,Xin Yuanm. fl.
Inbunden, Engelska, 2026
1 463 kr
Skickas inom 5-8 vardagar
In-depth exploration of machine learning techniques applied to UAV operations and communications, highlighting areas of potential growth and research gaps Artificial Intelligence for Unmanned Aerial Vehicles provides a comprehensive overview of machine learning (ML) techniques used in unmanned aerial vehicle (UAV) operations, communications, sensing, and computing. It emphasizes key components of UAV activity to which ML can significantly contribute including perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. The book considers the notion of security in the UAV network primarily in terms of its underlying rationale. This book also includes a detailed analysis of UAV behavior with respect to time and explores online machine learning-based solutions for UAV-assisted IoT networks. Additional topics include: Joint cruise control and data collectionResilience in an AI-aided UAV network against multiple attacks, introducing a flexible and adaptive threshold to alleviate malicious conductQuantification of influencing attributes, quantification of weights affiliated with these attributes, and movement tracking of malicious UAVsIntegration of contextual information, threshold definitions, and time-variant behavior analysisArtificial Intelligence for Unmanned Aerial Vehicles is an essential up-to-date reference on the subject for researchers, professors, graduate and senior undergraduate students, and industry professionals in the field.
Artificial Intelligence for Unmanned Aerial Vehicles
Sensing, Communication, and Computing
AvXin Wang,Wei Nim. fl.
E-bok
Engelska, 2026
1 704 kr
Läs direkt efter köp
In-depth exploration of machine learning techniques applied to UAV operations and communications, highlighting areas of potential growth and research gaps Artificial Intelligence for Unmanned Aerial Vehicles provides a comprehensive overview of machine learning (ML) techniques used in unmanned aerial vehicle (UAV) operations, communications, sensing, and computing. It emphasizes key components of UAV activity to which ML can significantly contribute including perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. The book considers the notion of security in the UAV network primarily in terms of its underlying rationale. This book also includes a detailed analysis of UAV behavior with respect to time and explores online machine learning-based solutions for UAV-assisted IoT networks. Additional topics include: Joint cruise control and data collectionResilience in an AI-aided UAV network against multiple attacks, introducing a flexible and adaptive threshold to alleviate malicious conductQuantification of influencing attributes, quantification of weights affiliated with these attributes, and movement tracking of malicious UAVsIntegration of contextual information, threshold definitions, and time-variant behavior analysis Artificial Intelligence for Unmanned Aerial Vehicles is an essential up-to-date reference on the subject for researchers, professors, graduate and senior undergraduate students, and industry professionals in the field.
Artificial Intelligence for Unmanned Aerial Vehicles
Sensing, Communication, and Computing
AvXin Wang,Wei Nim. fl.
E-bok
PDF, Engelska, 2026
1 704 kr
Läs direkt efter köp
In-depth exploration of machine learning techniques applied to UAV operations and communications, highlighting areas of potential growth and research gaps Artificial Intelligence for Unmanned Aerial Vehicles provides a comprehensive overview of machine learning (ML) techniques used in unmanned aerial vehicle (UAV) operations, communications, sensing, and computing. It emphasizes key components of UAV activity to which ML can significantly contribute including perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. The book considers the notion of security in the UAV network primarily in terms of its underlying rationale. This book also includes a detailed analysis of UAV behavior with respect to time and explores online machine learning-based solutions for UAV-assisted IoT networks. Additional topics include: Joint cruise control and data collectionResilience in an AI-aided UAV network against multiple attacks, introducing a flexible and adaptive threshold to alleviate malicious conductQuantification of influencing attributes, quantification of weights affiliated with these attributes, and movement tracking of malicious UAVsIntegration of contextual information, threshold definitions, and time-variant behavior analysis Artificial Intelligence for Unmanned Aerial Vehicles is an essential up-to-date reference on the subject for researchers, professors, graduate and senior undergraduate students, and industry professionals in the field.
Security and Resilience in Distributed Machine Learning
Challenges, Techniques, and Future Directions
AvKai Li,Xin Yuanm. fl.
Inbunden, Engelska, 2026
2 018 kr
Skickas inom 10-15 vardagar
This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrity. Through cutting-edge research and practical insights, the book introduces defense mechanisms like representational similarity analysis and visual explanation techniques, which safeguard FL models while ensuring performance and scalability. It also explores the evolving landscape of FL, including the integration of graph neural networks, explainable AI, and energy-efficient designs that drive sustainability in distributed systems. As FL becomes increasingly vital across industries—from healthcare and finance to IoT and smart cities—this book addresses the critical balance between security, functionality, and compliance with global data privacy regulations. It is an essential resource for researchers, industry professionals, and policymakers aiming to navigate and contribute to the rapidly growing domain of FL. By bridging theory and practice, this book contributes to advancing secure and resilient FL technologies.
Security and Resilience in Distributed Machine Learning
Challenges, Techniques, and Future Directions
AvWei Ni,Xin Yuanm. fl.
E-bok
Engelska, 2026
2 592 kr
Läs direkt efter köp
This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrity. Through cutting-edge research and practical insights, the book introduces defense mechanisms like representational similarity analysis and visual explanation techniques, which safeguard FL models while ensuring performance and scalability. It also explores the evolving landscape of FL, including the integration of graph neural networks, explainable AI, and energy-efficient designs that drive sustainability in distributed systems. As FL becomes increasingly vital across industries—from healthcare and finance to IoT and smart cities—this book addresses the critical balance between security, functionality, and compliance with global data privacy regulations. It is an essential resource for researchers, industry professionals, and policymakers aiming to navigate and contribute to the rapidly growing domain of FL. By bridging theory and practice, this book contributes to advancing secure and resilient FL technologies.

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Xin Yuan – författare

Graph Learning Techniques

Graph Learning Techniques

Graph Learning Techniques

Graph Learning Techniques

Artificial Intelligence for Unmanned Aerial Vehicles

Artificial Intelligence for Unmanned Aerial Vehicles

Artificial Intelligence for Unmanned Aerial Vehicles

Security and Resilience in Distributed Machine Learning

Security and Resilience in Distributed Machine Learning

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