Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applicationsGives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languagesCovers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languagesPresents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.- Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applications- Gives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languages- Covers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languages- Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

This book constitutes the refereed proceedings of the 11th International Conference on Ad Hoc Networks, ADHOCNETS 2019, held in Queenstown, New Zealand, in November 2019. The 28 full papers were selected from 64 submissions and cover a variety of network paradigms including mobile ad hoc networks, sensor networks, vehicular networks, underwater networks, airborne networks, underground networks, personal area networks, device-to-device (D2D) communications in 5G cellular networks, and home networks. The papers present a wide range of applications in civilian, commercial, and military areas.

This book constitutes the refereed proceedings of the 11th International Conference on Ad Hoc Networks, ADHOCNETS 2019, held in Queenstown, New Zealand, in November 2019. The 28 full papers were selected from 64 submissions and cover a variety of network paradigms including mobile ad hoc networks, sensor networks, vehicular networks, underwater networks, airborne networks, underground networks, personal area networks, device-to-device (D2D) communications in 5G cellular networks, and home networks. The papers present a wide range of applications in civilian, commercial, and military areas.

This book constitutes the proceedings of the Third International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT, held in Auckland, New Zealand, in April 2018. The 28 revised full papers presented were carefully reviewed and selected from 44 submissions. They focus on smart grid as the next generation of electrical grid, which will enable the smart integration of conventional, renewable and distributed power generation, energy storage, transmission and distribution, and demand management. The benefits of smart grid include enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality.

This book constitutes the proceedings of the Third International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT, held in Auckland, New Zealand, in April 2018. The 28 revised full papers presented were carefully reviewed and selected from 44 submissions. They focus on smart grid as the next generation of electrical grid, which will enable the smart integration of conventional, renewable and distributed power generation, energy storage, transmission and distribution, and demand management. The benefits of smart grid include enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality.

This book constitutes peer-reviewed proceedings of the International Conference on Emerging Electronics and Automation (E2A) 2021. The book presents new ideas, research findings, and novel techniques in the fields of sensors and instrumentation, automation and control, artificial intelligence, MEMS sensors, soft computing, signal processing, and communication. It includes contributions received from both academia and industry. The proceedings will be helpful for beginners as well as advanced researchers in the area of automation and other allied fields.

This book constitutes peer-reviewed proceedings of the International Conference on Emerging Electronics and Automation (E2A) 2021. The book presents new ideas, research findings, and novel techniques in the fields of sensors and instrumentation, automation and control, artificial intelligence, MEMS sensors, soft computing, signal processing, and communication. It includes contributions received from both academia and industry. The proceedings will be helpful for beginners as well as advanced researchers in the area of automation and other allied fields.

This volume comprises selected peer-reviewed proceedings of the 9th International Conference on Signal Processing and Integrated Networks (SPIN 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in signal processing, IoT sensors, systems and technologies, cloud computing, wireless communication, and wireless sensor networks. This volume will provide a valuable resource for those in academia and industry.

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applicationsGives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languagesCovers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languagesPresents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.- Provides comprehensive coverage of the emerging paradigm of graph data science and its real-world applications- Gives readers practical guidance on how to approach and solve complex data analysis problems using graph data science, with an emphasis on deep analysis techniques including graph neural networks (GNNs), machine learning, algorithms, graph databases, and graph query languages- Covers extended graph models such as bipartite directed graphs of place-transition nets, graphs with dynamical processes defined on them - Petri and Sleptsov nets, and graphs as programming languages- Presents all the key tools and techniques as well as the foundations of graph theory, including mathematical concepts, research, and graph analytics

This book constitutes the refereed proceedings of the 11th International Conference on Ad Hoc Networks, ADHOCNETS 2019, held in Queenstown, New Zealand, in November 2019. The 28 full papers were selected from 64 submissions and cover a variety of network paradigms including mobile ad hoc networks, sensor networks, vehicular networks, underwater networks, airborne networks, underground networks, personal area networks, device-to-device (D2D) communications in 5G cellular networks, and home networks. The papers present a wide range of applications in civilian, commercial, and military areas.

This book constitutes the refereed proceedings of the 11th International Conference on Ad Hoc Networks, ADHOCNETS 2019, held in Queenstown, New Zealand, in November 2019. The 28 full papers were selected from 64 submissions and cover a variety of network paradigms including mobile ad hoc networks, sensor networks, vehicular networks, underwater networks, airborne networks, underground networks, personal area networks, device-to-device (D2D) communications in 5G cellular networks, and home networks. The papers present a wide range of applications in civilian, commercial, and military areas.

This book constitutes the proceedings of the Third International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT, held in Auckland, New Zealand, in April 2018. The 28 revised full papers presented were carefully reviewed and selected from 44 submissions. They focus on smart grid as the next generation of electrical grid, which will enable the smart integration of conventional, renewable and distributed power generation, energy storage, transmission and distribution, and demand management. The benefits of smart grid include enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality.

This book constitutes the proceedings of the Third International Conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT, held in Auckland, New Zealand, in April 2018. The 28 revised full papers presented were carefully reviewed and selected from 44 submissions. They focus on smart grid as the next generation of electrical grid, which will enable the smart integration of conventional, renewable and distributed power generation, energy storage, transmission and distribution, and demand management. The benefits of smart grid include enhanced reliability and resilience, higher intelligence and optimized control, decentralized operation, higher operational efficiency, more efficient demand management, and better power quality.

This book constitutes peer-reviewed proceedings of the International Conference on Emerging Electronics and Automation (E2A) 2021. The book presents new ideas, research findings, and novel techniques in the fields of sensors and instrumentation, automation and control, artificial intelligence, MEMS sensors, soft computing, signal processing, and communication. It includes contributions received from both academia and industry. The proceedings will be helpful for beginners as well as advanced researchers in the area of automation and other allied fields.

This book constitutes peer-reviewed proceedings of the International Conference on Emerging Electronics and Automation (E2A) 2021. The book presents new ideas, research findings, and novel techniques in the fields of sensors and instrumentation, automation and control, artificial intelligence, MEMS sensors, soft computing, signal processing, and communication. It includes contributions received from both academia and industry. The proceedings will be helpful for beginners as well as advanced researchers in the area of automation and other allied fields.

This volume comprises selected peer-reviewed proceedings of the 9th International Conference on Signal Processing and Integrated Networks (SPIN 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in signal processing, IoT sensors, systems and technologies, cloud computing, wireless communication, and wireless sensor networks. This volume will provide a valuable resource for those in academia and industry.