Gupta Punit Pradeep Kumar (53 results)

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Paperback. Condition: new. Paperback. A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:The existing fog and edge architecture is used to provide solutions to future challenges.A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare.An optimization framework helps in cloud resource management.Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production.Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers.The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients. The book examines the enhancement of the performance of fog networks for real-world applications using nature-based optimization techniques. It looks at the challenges in cloud datacenter nodes and fog computing networks and provides solutions. It covers such issues as security, privacy, and power consumption. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. Biomedical engineering is undergoing a transformation because of AI, which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:Genome sequence and visualizationThe role of AI and cloud in detection of diseasesNature-inspired algorithms for disease detectionFrameworks for disease classificationWith a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how machine learning (ML) and deep learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring. Biomedical engineering is undergoing a transformation due to AI and cloud computing, which are allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. This work examines these two computing paradigms in biomedical engineering, highlighting practical applications and new directions. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Condition: new. Paperback. A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:The existing fog and edge architecture is used to provide solutions to future challenges.A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare.An optimization framework helps in cloud resource management.Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production.Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers.The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients. The book examines the enhancement of the performance of fog networks for real-world applications using nature-based optimization techniques. It looks at the challenges in cloud datacenter nodes and fog computing networks and provides solutions. It covers such issues as security, privacy, and power consumption. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Paperback. Condition: Brand New. 268 pages. 9.18x6.12x9.21 inches. In Stock.

Paperback. Condition: new. Paperback. A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:The existing fog and edge architecture is used to provide solutions to future challenges.A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare.An optimization framework helps in cloud resource management.Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production.Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers.The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients. The book examines the enhancement of the performance of fog networks for real-world applications using nature-based optimization techniques. It looks at the challenges in cloud datacenter nodes and fog computing networks and provides solutions. It covers such issues as security, privacy, and power consumption. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: new. Paperback. Biomedical engineering is undergoing a transformation because of AI, which is allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. Artificial Intelligence and Cloud Computing Applications in Biomedical Engineering examines the salient characteristics of AI in biomedical engineering, highlighting its practical applications and new directions. Highlights of the book include:Genome sequence and visualizationThe role of AI and cloud in detection of diseasesNature-inspired algorithms for disease detectionFrameworks for disease classificationWith a focus on designing AI techniques for disease detection, the book explores the role of AI in biomedical engineering. It discusses how machine learning (ML) and deep learning (DL) are at the heart of AI applications in biomedical engineering. ML algorithms, particularly those based on neural networks, enable computers to learn from large datasets, identify patterns, and make predictions or decisions without explicit programming, and implementing ML algorithms is a focus of the book. Another focus is on DL, a subset of ML, and how it uses multi-layered neural networks to achieve high accuracy in such complex tasks as image and speech recognition. Biomedical engineering generates massive amounts of data from medical imaging, genomic sequencing, wearable devices, electronic health records (EHR), and other sources. This book also discusses AI-driven big data analytics, which allows researchers and clinicians to derive from data meaningful insights, aiding in early disease detection, personalized treatment plans, and patient monitoring. Biomedical engineering is undergoing a transformation due to AI and cloud computing, which are allowing creative solutions that enhance patient outcomes, diagnosis, treatment planning, and healthcare delivery. This work examines these two computing paradigms in biomedical engineering, highlighting practical applications and new directions. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

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Condition: New. 1st edition NO-PA16APR2015-KAP.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days. 185.

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