Govind Singh Patel – författare

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm's location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm''s location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers'' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

This book endeavours to highlight the untapped potential of Smart Agriculture for the innovation and expansion of the agriculture sector. The sector shall make incremental progress as it learns from associations between data over time through Artificial Intelligence, deep learning and Internet of Things applications. The farming industry and Smart agriculture develop from the stringent limits imposed by a farm''s location, which in turn has a series of related effects with respect to supply chain management, food availability, biodiversity, farmers'' decision-making and insurance, and environmental concerns among others. All of the above-mentioned aspects will derive substantial benefits from the implementation of a data-driven approach under the condition that the systems, tools and techniques to be used have been designed to handle the volume and variety of the data to be gathered. Contributions to this book have been solicited with the goal of uncovering the possibilities of engaging agriculture with equipped and effective profound learning algorithms. Most agricultural research centres are already adopting Internet of Things for the monitoring of a wide range of farm services, and there are significant opportunities for agriculture administration through the effective implementation of Machine Learning, Deep Learning, Big Data and IoT structures.

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.

This book offers comprehensive coverage of the most essential topics, including:

Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.

This book offers comprehensive coverage of the most essential topics, including:

Introduction to e-monitoring for healthcare Case studies based on big data and healthcare Intelligent learning analytics in healthcare sectors using machine learning and IoT Identifying diseases and diagnosis using machine learning and IoT Deep learning architecture and framework for healthcare using IoT Knowledge discovery from big data of healthcare-related processing Big data and IoT in healthcare Role of IoT in sustainable healthcare A heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.This book offers comprehensive coverage of the most essential topics, including:Introduction to e-monitoring for healthcareCase studies based on big data and healthcareIntelligent learning analytics in healthcare sectors using machine learning and IoTIdentifying diseases and diagnosis using machine learning and IoTDeep learning architecture and framework for healthcare using IoTKnowledge discovery from big data of healthcare-related processingBig data and IoT in healthcareRole of IoT in sustainable healthcareA heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

This book reviews that narrate the development of current technologies under the theme of the emerging concept of healthcare, specifically in terms of what makes healthcare more efficient and effective with the help of high-precision algorithms. The mechanism that drives it is machine learning, deep learning, big data, and Internet of Things (IoT)—the scientific field that gives machines the ability to learn without being strictly programmed. It has emerged together with big data technologies and high-performance computing to create new opportunities to unravel, quantify, and understand data-intensive processes in healthcare operational environments.This book offers comprehensive coverage of the most essential topics, including:Introduction to e-monitoring for healthcareCase studies based on big data and healthcareIntelligent learning analytics in healthcare sectors using machine learning and IoTIdentifying diseases and diagnosis using machine learning and IoTDeep learning architecture and framework for healthcare using IoTKnowledge discovery from big data of healthcare-related processingBig data and IoT in healthcareRole of IoT in sustainable healthcareA heterogeneous IoT-based application for remote monitoring of physiological and environmental parameters

The two-volume set CCIS 2053 and 2054 constitutes the refereed post-conference proceedings of the 13th International Advanced Computing Conference, IACC 2023, held in Kolhapur, India, during December 15–16, 2023.The 66 full papers and 6 short papers presented in these proceedings were carefully reviewed and selected from 425 submissions. The papers are organized in the following topical sections:Volume I:The AI renaissance: a new era of human-machine collaboration; application of recurrent neural network in natural language processing, AI content detection and time series data analysis; unveiling the next frontier of AI advancement.Volume II:Agricultural resilience and disaster management for sustainable harvest; disease and abnormalities detection using ML and IOT; application of deep learning in healthcare; cancer detection using AI.

The two-volume set CCIS 2053 and 2054 constitutes the refereed post-conference proceedings of the 13th International Advanced Computing Conference, IACC 2023, held in Kolhapur, India, during December 15–16, 2023.The 66 full papers and 6 short papers presented in these proceedings were carefully reviewed and selected from 425 submissions. The papers are organized in the following topical sections:Volume I:The AI renaissance: a new era of human-machine collaboration; application of recurrent neural network in natural language processing, AI content detection and time series data analysis; unveiling the next frontier of AI advancement.Volume II:Agricultural resilience and disaster management for sustainable harvest; disease and abnormalities detection using ML and IOT; application of deep learning in healthcare; cancer detection using AI.

The two-volume set CCIS 2053 and 2054 constitutes the refereed post-conference proceedings of the 13th International Advanced Computing Conference, IACC 2023, held in Kolhapur, India, during December 15–16, 2023.The 66 full papers and 6 short papers presented in these proceedings were carefully reviewed and selected from 425 submissions. The papers are organized in the following topical sections:Volume I:The AI renaissance: a new era of human-machine collaboration; application of recurrent neural network in natural language processing, AI content detection and time series data analysis; unveiling the next frontier of AI advancement.Volume II:Agricultural resilience and disaster management for sustainable harvest; disease and abnormalities detection using ML and IOT; application of deep learning in healthcare; cancer detection using AI.