Pippet sky (56 Ergebnisse)
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Verlag: Pippet Sky, 2026
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Verlag: Pippet Sky, 2026
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Verlag: Pippet Sky, 2026
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Paperback. Zustand: new. Paperback. The rapid growth of online social networks has transformed the way individuals communicate, share information, and participate in digital communities. At the same time, the enormous volume of user-generated data has created significant opportunities for data mining while raising important conc…erns regarding privacy, security, and responsible data management. Privacy-Preserving Data Mining for Online Social Networks provides a comprehensive introduction to the principles, techniques, and challenges involved in extracting meaningful knowledge from social network data while protecting individual privacy and maintaining data confidentiality.The book examines the intersection of data mining, privacy preservation, social network analysis, and information security, presenting the engineering and computational concepts that support responsible data analysis in large-scale online environments. Readers are introduced to the fundamentals of knowledge discovery, data preprocessing, pattern recognition, classification, clustering, association analysis, anomaly detection, and predictive analytics as they relate to online social platforms. The discussion emphasizes the importance of balancing analytical utility with privacy protection to enable ethical and compliant data-driven decision-making.A central focus is placed on privacy-preserving methodologies that reduce the risk of exposing sensitive information during data collection, storage, sharing, and analysis. The text explores widely recognized approaches including data anonymization, data masking, pseudonymization, secure data publishing, differential privacy, privacy-aware data transformation, cryptographic techniques, and secure multi-party computation. These concepts are presented within the broader framework of privacy engineering, helping readers understand how technical safeguards contribute to secure and trustworthy data mining processes.The book further discusses the structure and dynamics of online social networks, highlighting how graph-based data models, user interactions, community detection, influence analysis, recommendation systems, sentiment analysis, and network evolution contribute to understanding social behavior. It also examines the ethical, legal, and security considerations associated with processing social network data, including data governance, access control, identity protection, risk management, and regulatory compliance. These discussions provide readers with a balanced understanding of both the opportunities and responsibilities associated with mining large-scale social datasets.Designed for undergraduate and graduate students, researchers, data scientists, computer engineers, cybersecurity professionals, educators, and information technology practitioners, this volume serves as both an academic reference and a practical introduction to privacy-aware data mining. The material emphasizes broadly applicable computational principles and established methodologies rather than proprietary software platforms or vendor-specific technologies, making it suitable for university coursework, research, and professional development.As organizations increasingly rely on social data to support business intelligence, public policy, cybersecurity, healthcare, marketing, and scientific research, preserving user privacy has become a critical requirement. Privacy-Preserving Data Mining for Online Social Networks provides readers with a structured understanding of the technologies, analytical methods, and privacy-enhancing techniques that enable responsible knowledge discovery from social network data. Optimized for engineering students, academic libraries, universities, researchers, and data professionals, this book serves as a valuable resource for exploring privacy-preserving analytics, social network mining, data Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Verlag: Pippet Sky, 2026
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Paperback. Zustand: new. Paperback. The rapid growth of online social networks has transformed the way individuals communicate, share information, and participate in digital communities. At the same time, the enormous volume of user-generated data has created significant opportunities for data mining while raising important conc…erns regarding privacy, security, and responsible data management. Privacy-Preserving Data Mining for Online Social Networks provides a comprehensive introduction to the principles, techniques, and challenges involved in extracting meaningful knowledge from social network data while protecting individual privacy and maintaining data confidentiality.The book examines the intersection of data mining, privacy preservation, social network analysis, and information security, presenting the engineering and computational concepts that support responsible data analysis in large-scale online environments. Readers are introduced to the fundamentals of knowledge discovery, data preprocessing, pattern recognition, classification, clustering, association analysis, anomaly detection, and predictive analytics as they relate to online social platforms. The discussion emphasizes the importance of balancing analytical utility with privacy protection to enable ethical and compliant data-driven decision-making.A central focus is placed on privacy-preserving methodologies that reduce the risk of exposing sensitive information during data collection, storage, sharing, and analysis. The text explores widely recognized approaches including data anonymization, data masking, pseudonymization, secure data publishing, differential privacy, privacy-aware data transformation, cryptographic techniques, and secure multi-party computation. These concepts are presented within the broader framework of privacy engineering, helping readers understand how technical safeguards contribute to secure and trustworthy data mining processes.The book further discusses the structure and dynamics of online social networks, highlighting how graph-based data models, user interactions, community detection, influence analysis, recommendation systems, sentiment analysis, and network evolution contribute to understanding social behavior. It also examines the ethical, legal, and security considerations associated with processing social network data, including data governance, access control, identity protection, risk management, and regulatory compliance. These discussions provide readers with a balanced understanding of both the opportunities and responsibilities associated with mining large-scale social datasets.Designed for undergraduate and graduate students, researchers, data scientists, computer engineers, cybersecurity professionals, educators, and information technology practitioners, this volume serves as both an academic reference and a practical introduction to privacy-aware data mining. The material emphasizes broadly applicable computational principles and established methodologies rather than proprietary software platforms or vendor-specific technologies, making it suitable for university coursework, research, and professional development.As organizations increasingly rely on social data to support business intelligence, public policy, cybersecurity, healthcare, marketing, and scientific research, preserving user privacy has become a critical requirement. Privacy-Preserving Data Mining for Online Social Networks provides readers with a structured understanding of the technologies, analytical methods, and privacy-enhancing techniques that enable responsible knowledge discovery from social network data. Optimized for engineering students, academic libraries, universities, researchers, and data professionals, this book serves as a valuable resource for exploring privacy-preserving analytics, social network Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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- Softcover
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Paperback. Zustand: new. Paperback. Human-centered robotics has become an essential area of research and development as autonomous systems are increasingly deployed in environments where they interact directly with people. Human-Centered Tracking for Autonomous Robots provides a comprehensive introduction to the principles, tech…nologies, and engineering methods that enable robots to detect, identify, monitor, and safely track human movement in dynamic environments. By integrating robotics, artificial intelligence, computer vision, sensor technologies, and autonomous navigation, the book presents the core concepts that support reliable human-aware robotic operation across industrial, commercial, healthcare, and service applications.The book explores the foundations of human-centered tracking by examining how autonomous robots perceive and interpret human behavior using a combination of cameras, LiDAR, radar, depth sensors, inertial measurement systems, and other sensing technologies. Readers are introduced to the principles of perception, environmental awareness, object detection, feature extraction, target localization, and motion estimation that form the basis of intelligent tracking systems. The discussion emphasizes the importance of accurate perception in enabling robots to operate safely while maintaining effective interaction with people in complex and continuously changing environments.A significant focus is placed on tracking algorithms and autonomous decision-making techniques that allow robots to maintain awareness of human position, movement, and activity. The text introduces concepts related to computer vision, machine learning, sensor fusion, probabilistic estimation, multi-object tracking, trajectory prediction, and adaptive navigation. It also examines how perception and tracking systems contribute to obstacle avoidance, path planning, motion control, and collaborative robotics, helping readers understand the integration of multiple engineering disciplines within intelligent robotic platforms.The book further discusses human-robot interaction from both technical and operational perspectives. Topics such as safety-aware navigation, collaborative workspaces, real-time localization, environmental mapping, simultaneous localization and mapping (SLAM), behavioral prediction, autonomous decision support, and robotic situational awareness are presented within the broader context of intelligent automation. These discussions demonstrate how effective human tracking improves navigation efficiency, operational safety, task coordination, and user acceptance across a wide range of robotic applications.Designed for undergraduate and graduate students, researchers, robotics engineers, computer vision specialists, automation professionals, educators, and technology practitioners, this volume serves as both an academic reference and a practical introduction to modern human-aware robotics. The material emphasizes established engineering principles and broadly applicable methodologies rather than proprietary software platforms or vendor-specific technologies, making it relevant across research, education, and industrial environments.As autonomous robots continue to expand into healthcare, logistics, manufacturing, hospitality, security, smart cities, and public service applications, the ability to accurately detect and track human activity has become a critical requirement for safe and effective deployment. Human-Centered Tracking for Autonomous Robots provides readers with a structured understanding of the engineering principles that support reliable human tracking, intelligent perception, and autonomous navigation. Human-Centered Tracking for Autonomous Robots explores the technologies and engineering principles that enable autonomous robots to detect, track, and in Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: Pippet Sky, 2026
- Softcover
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Paperback. Zustand: new. Paperback. The rapid growth of online social networks has transformed the way individuals communicate, share information, and participate in digital communities. At the same time, the enormous volume of user-generated data has created significant opportunities for data mining while raising important conc…erns regarding privacy, security, and responsible data management. Privacy-Preserving Data Mining for Online Social Networks provides a comprehensive introduction to the principles, techniques, and challenges involved in extracting meaningful knowledge from social network data while protecting individual privacy and maintaining data confidentiality.The book examines the intersection of data mining, privacy preservation, social network analysis, and information security, presenting the engineering and computational concepts that support responsible data analysis in large-scale online environments. Readers are introduced to the fundamentals of knowledge discovery, data preprocessing, pattern recognition, classification, clustering, association analysis, anomaly detection, and predictive analytics as they relate to online social platforms. The discussion emphasizes the importance of balancing analytical utility with privacy protection to enable ethical and compliant data-driven decision-making.A central focus is placed on privacy-preserving methodologies that reduce the risk of exposing sensitive information during data collection, storage, sharing, and analysis. The text explores widely recognized approaches including data anonymization, data masking, pseudonymization, secure data publishing, differential privacy, privacy-aware data transformation, cryptographic techniques, and secure multi-party computation. These concepts are presented within the broader framework of privacy engineering, helping readers understand how technical safeguards contribute to secure and trustworthy data mining processes.The book further discusses the structure and dynamics of online social networks, highlighting how graph-based data models, user interactions, community detection, influence analysis, recommendation systems, sentiment analysis, and network evolution contribute to understanding social behavior. It also examines the ethical, legal, and security considerations associated with processing social network data, including data governance, access control, identity protection, risk management, and regulatory compliance. These discussions provide readers with a balanced understanding of both the opportunities and responsibilities associated with mining large-scale social datasets.Designed for undergraduate and graduate students, researchers, data scientists, computer engineers, cybersecurity professionals, educators, and information technology practitioners, this volume serves as both an academic reference and a practical introduction to privacy-aware data mining. The material emphasizes broadly applicable computational principles and established methodologies rather than proprietary software platforms or vendor-specific technologies, making it suitable for university coursework, research, and professional development.As organizations increasingly rely on social data to support business intelligence, public policy, cybersecurity, healthcare, marketing, and scientific research, preserving user privacy has become a critical requirement. Privacy-Preserving Data Mining for Online Social Networks provides readers with a structured understanding of the technologies, analytical methods, and privacy-enhancing techniques that enable responsible knowledge discovery from social network data. Optimized for engineering students, academic libraries, universities, researchers, and data professionals, this book serves as a valuable resource for exploring privacy-preserving analytics, social network Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Verlag: Pippet Sky, 2026
- Softcover
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Paperback. Zustand: new. Paperback. Human-centered robotics has become an essential area of research and development as autonomous systems are increasingly deployed in environments where they interact directly with people. Human-Centered Tracking for Autonomous Robots provides a comprehensive introduction to the principles, tech…nologies, and engineering methods that enable robots to detect, identify, monitor, and safely track human movement in dynamic environments. By integrating robotics, artificial intelligence, computer vision, sensor technologies, and autonomous navigation, the book presents the core concepts that support reliable human-aware robotic operation across industrial, commercial, healthcare, and service applications.The book explores the foundations of human-centered tracking by examining how autonomous robots perceive and interpret human behavior using a combination of cameras, LiDAR, radar, depth sensors, inertial measurement systems, and other sensing technologies. Readers are introduced to the principles of perception, environmental awareness, object detection, feature extraction, target localization, and motion estimation that form the basis of intelligent tracking systems. The discussion emphasizes the importance of accurate perception in enabling robots to operate safely while maintaining effective interaction with people in complex and continuously changing environments.A significant focus is placed on tracking algorithms and autonomous decision-making techniques that allow robots to maintain awareness of human position, movement, and activity. The text introduces concepts related to computer vision, machine learning, sensor fusion, probabilistic estimation, multi-object tracking, trajectory prediction, and adaptive navigation. It also examines how perception and tracking systems contribute to obstacle avoidance, path planning, motion control, and collaborative robotics, helping readers understand the integration of multiple engineering disciplines within intelligent robotic platforms.The book further discusses human-robot interaction from both technical and operational perspectives. Topics such as safety-aware navigation, collaborative workspaces, real-time localization, environmental mapping, simultaneous localization and mapping (SLAM), behavioral prediction, autonomous decision support, and robotic situational awareness are presented within the broader context of intelligent automation. These discussions demonstrate how effective human tracking improves navigation efficiency, operational safety, task coordination, and user acceptance across a wide range of robotic applications.Designed for undergraduate and graduate students, researchers, robotics engineers, computer vision specialists, automation professionals, educators, and technology practitioners, this volume serves as both an academic reference and a practical introduction to modern human-aware robotics. The material emphasizes established engineering principles and broadly applicable methodologies rather than proprietary software platforms or vendor-specific technologies, making it relevant across research, education, and industrial environments.As autonomous robots continue to expand into healthcare, logistics, manufacturing, hospitality, security, smart cities, and public service applications, the ability to accurately detect and track human activity has become a critical requirement for safe and effective deployment. Human-Centered Tracking for Autonomous Robots provides readers with a structured understanding of the engineering principles that support reliable human tracking, intelligent perception, and autonomous navigation. Human-Centered Tracking for Autonomous Robots explores the technologies and engineering principles that enable autonomous robots to detect, track, and in Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Paperback. Zustand: new. Paperback. Autonomous mobile robots are becoming an essential component of modern computing, industrial automation, intelligent transportation, warehouse management, environmental monitoring, and space exploration. As multi-robot systems continue to evolve, efficient algorithms for robot dispersion, grap…h exploration, and distributed coordination have become increasingly important. Dispersion-the process of relocating robots so that each occupies a distinct position in a graph-is closely related to graph exploration, load balancing, distributed computing, and cooperative robotics, making it a fundamental problem in algorithm design and autonomous systems.Robot Computational Dispersion and Exploration on Graph Topologies and Performance Indices presents a comprehensive study of computational techniques that enable autonomous robots to efficiently disperse and explore graph-based environments. The book introduces readers to the mathematical foundations of graph theory, distributed algorithms, computational models, and robotic coordination before examining how different graph topologies influence robot movement, communication, and task execution. It provides a detailed understanding of anonymous graphs, trees, grids, rings, meshes, and complex network structures that commonly appear in distributed robotic applications.The book explains the principles of robot dispersion, graph exploration, navigation strategies, and cooperative decision-making in both synchronous and asynchronous computational environments. Readers learn how autonomous robots coordinate their movements, exchange information, avoid conflicts, minimize redundant exploration, and achieve efficient coverage of unknown environments. Various deterministic and distributed algorithms are discussed to illustrate how computational efficiency can be improved while reducing communication overhead, memory usage, and execution time.Special emphasis is placed on the analysis of performance indices that evaluate the effectiveness of robotic algorithms. The book examines important evaluation metrics including computational complexity, execution time, memory requirements, communication cost, energy consumption, scalability, fault tolerance, path efficiency, exploration coverage, and load balancing. Readers gain practical insight into selecting appropriate algorithms for different robotic applications based on performance objectives and operational constraints. Robot Computational Dispersion and Exploration on Graph Topologies and Performance Indices explores distributed algorithms, graph theory, and autonomous robot coordination. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 160 pp. Englisch.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 158 pp. Englisch.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 146 pp. Englisch.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 154 pp. Englisch.
