Ravi Shankar Singh – författare

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization.

The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand.�

Provides state-of-art methods and current trends in data analytics for the healthcare industry Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques Discusses several potential AI techniques developed using IoT for the healthcare industry Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization.The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand.- Provides state-of-art methods and current trends in data analytics for the healthcare industry- Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques- Discusses several potential AI techniques developed using IoT for the healthcare industry- Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.• Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations• Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching• Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach• Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems• Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)

This book presents a comprehensive exploration of structural pattern recognition with a clear understanding of graph representation and manipulation. It explains graph matching techniques, unearthing the core principles of graph similarity measures, subgraph isomorphism, and advanced algorithms tailored to various pattern recognition tasks. It bridges the gap between theory and application by providing case studies, hands-on examples, and applications. It is a reference book for academicians, researchers, and students working in the fields of structural pattern recognition, computer vision, artificial intelligence, and data science.* Begins with the fundamentals of graph theory, graph matching algorithms, and structural pattern recognition concepts and explains the principles, methodologies, and practical implementations* Presents relevant case studies and hands-on examples across chapters to guide making informed decisions by graph matching* Discusses various graph-matching algorithms, including exact and approximate methods, geometric methods, spectral techniques, graph kernels, and graph neural networks, including practical examples to illustrate the strengths and limitations of each approach* Showcases the versatility of graph matching in real-world applications, such as image analysis, biological molecule identification, object recognition, social network clustering, and recommendation systems* Describes deep learning models for graph matching, including graph convolutional networks (GCNs) and graph neural networks (GNNs)