jeganathan joseph (19 Ergebnisse)

Zustand: New. pp. 624.

Zustand: New. pp. 624 3rd Edition NO-PA16APR2015-KAP.

Zustand: New. pp. 624.

Soft cover. Zustand: New. 1st Edition. Contents: Preface to the Third Edition. Preface to the Second Edition. Preface to the First Edition.1. Introduction. 2. Electromagnetic Radiation. 3. Fundamentals of Radiometry. 4. Physical Basis of Signatures. 5. Remote Sensors An Overview. 6. Optical Infrared Sensors. 7. Microwave Sensors. 8. Platforms. 9. Data Reception and Data Products. 10. Data Analysis. 11. Applications of Remote Sensing for Earth Resources Management. 12. Geographical Information System (GIS). References. Index. The Third Edition of this book retains the basic principles of remote sensing, introduced in the earlier editions. It covers all aspects of the subject from electromagnetic radiation, its interaction with objects, various sensors, platforms, data processing, data product generation and end utilisation for earth resource monitoring and management. Apart from material that has retained value since the previous edition, this revised and updated edition presents additional information to keep the readers abreast of the emerging trends. The newer developments in sensor technology, supplementary information on image processing, data product generation, applications of remote sensing in disciplines such as archaeology, desertification and drought assessment are included. A relatively newer theme in remote sensing GNSS remote sensing has been introduced. Since remote sensing is used by professionals from varied disciplines, the book is designed to cater to readers from various backgrounds. For those intending to pursue graduate studies in remote sensing, this book serves as an overview and introduction, so that the basic concepts of all topics science, technology and applications of remote sensing are clear. This directs them to delve deeper into their specific field of interest. The book serves as a source of information for professionals who come across remote sensing in their work and would like to learn more about its principles and practical uses to support their professional/research activity. For faculty who want to widen their horizons, the comprehensive bibliography and relevant websites will be extremely helpful. Overall the book serves as a single window source to comprehend the basics of the subject. (jacket).

Hardcover. Zustand: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. Pioneering AI and data technologies redefines next-generation security, Internet of Things (IoT), and smart ecosystems. By integrating intelligent algorithms with real-time data, these technologies enable more responsive, adaptive, and predictive systems across urban infrastructure, industrial environments, and connected devices. In security, AI strengthens threat detection and response, while in IoT, it enhances smarter automation and efficient resource management. Together with advanced data analytics, these innovations drive interconnected ecosystems that are more intelligent and secure, as well as more sustainable and resilient when facing global challenges. Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems explores the integration of intelligent technologies into security, automation, and smart business sectors. It presents solutions for enhanced threat detection, data analysis, and predictive maintenance. This book covers topics such as digital technology, cloud computing, and deep learning, and is a useful resource for computer engineers, security professionals, business owners, academicians, researchers, and data scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Paperback. Zustand: new. Paperback. Pioneering AI and data technologies redefines next-generation security, Internet of Things (IoT), and smart ecosystems. By integrating intelligent algorithms with real-time data, these technologies enable more responsive, adaptive, and predictive systems across urban infrastructure, industrial environments, and connected devices. In security, AI strengthens threat detection and response, while in IoT, it enhances smarter automation and efficient resource management. Together with advanced data analytics, these innovations drive interconnected ecosystems that are more intelligent and secure, as well as more sustainable and resilient when facing global challenges. Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems explores the integration of intelligent technologies into security, automation, and smart business sectors. It presents solutions for enhanced threat detection, data analysis, and predictive maintenance. This book covers topics such as digital technology, cloud computing, and deep learning, and is a useful resource for computer engineers, security professionals, business owners, academicians, researchers, and data scientists. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Zustand: new. Hardcover. Pioneering AI and data technologies redefines next-generation security, Internet of Things (IoT), and smart ecosystems. By integrating intelligent algorithms with real-time data, these technologies enable more responsive, adaptive, and predictive systems across urban infrastructure, industrial environments, and connected devices. In security, AI strengthens threat detection and response, while in IoT, it enhances smarter automation and efficient resource management. Together with advanced data analytics, these innovations drive interconnected ecosystems that are more intelligent and secure, as well as more sustainable and resilient when facing global challenges. Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems explores the integration of intelligent technologies into security, automation, and smart business sectors. It presents solutions for enhanced threat detection, data analysis, and predictive maintenance. This book covers topics such as digital technology, cloud computing, and deep learning, and is a useful resource for computer engineers, security professionals, business owners, academicians, researchers, and data scientists. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Zustand: Neu. Neuware.

Paperback. Zustand: new. Paperback. Pioneering AI and data technologies redefines next-generation security, Internet of Things (IoT), and smart ecosystems. By integrating intelligent algorithms with real-time data, these technologies enable more responsive, adaptive, and predictive systems across urban infrastructure, industrial environments, and connected devices. In security, AI strengthens threat detection and response, while in IoT, it enhances smarter automation and efficient resource management. Together with advanced data analytics, these innovations drive interconnected ecosystems that are more intelligent and secure, as well as more sustainable and resilient when facing global challenges. Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems explores the integration of intelligent technologies into security, automation, and smart business sectors. It presents solutions for enhanced threat detection, data analysis, and predictive maintenance. This book covers topics such as digital technology, cloud computing, and deep learning, and is a useful resource for computer engineers, security professionals, business owners, academicians, researchers, and data scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Zustand: new. Hardcover. Pioneering AI and data technologies redefines next-generation security, Internet of Things (IoT), and smart ecosystems. By integrating intelligent algorithms with real-time data, these technologies enable more responsive, adaptive, and predictive systems across urban infrastructure, industrial environments, and connected devices. In security, AI strengthens threat detection and response, while in IoT, it enhances smarter automation and efficient resource management. Together with advanced data analytics, these innovations drive interconnected ecosystems that are more intelligent and secure, as well as more sustainable and resilient when facing global challenges. Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems explores the integration of intelligent technologies into security, automation, and smart business sectors. It presents solutions for enhanced threat detection, data analysis, and predictive maintenance. This book covers topics such as digital technology, cloud computing, and deep learning, and is a useful resource for computer engineers, security professionals, business owners, academicians, researchers, and data scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Zustand: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Zustand: new. Hardcover. The integration of machine learning, predictive analytics, and optimization techniques revolutionizes the understanding and management of complex systems. From supply chains and energy grids to healthcare and financial markets, these systems are characterized by dynamic interactions, uncertainty, and large data amounts. Machine learning enables insights into data patterns, analytics predict future behaviors, and optimization methods guide decision-making. When combined, these tools offer solutions for enhancing system performance, resilience, and adaptability. As complexity grows, their collaboration becomes vital for creating intelligent, responsive, and sustainable systems. Machine Learning, Predictive Analytics, and Optimization in Complex Systems examines the integration of intelligent technologies into system design and management, and data analysis. It explores strategies for data-informed decisions, intelligent technology utilization, and security optimization. This book covers topics such as computer engineering, smart ecosystems, and system design, and is a useful resource for computer engineers, data analysts, academicians, researchers, and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Zustand: new. Hardcover. The landscape of healthcare is transformed by the integration of advanced data analytics, especially in the realm of multi-source data analysis. By combining diverse datasets, such as electronic health records (EHRs), genetic information, wearable device data, and patient-reported outcomes, healthcare providers can gain a comprehensive understanding of a patient's health status. This approach creates more personalized treatment plans, enhances diagnostic accuracy, and supports early detection of potential health issues. Communication between various data sources allows for the identification of hidden trends and patterns, improving predictive capabilities and optimizing patient outcomes. As healthcare systems adopt this data-driven process, it is crucial to address challenges related to data privacy, integration, and the interpretation of complex datasets, ensuring the potential benefits of multi-source data analysis are realized in ethical and effective ways. Optimizing Patient Outcomes Through Multi-Source Data Analysis in Healthcare explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets, analyzed through AI algorithms to enhance patient outcomes and operational efficiency. This book covers topics such as automation, machine learning, and neural networks, and is a useful resource for healthcare professionals, computer engineers, business owners, academicians, researchers, and data scientists. "This book explores the transformative potential of big data and AI in healthcare, focusing on informed decision-making. It delves into the integration of vast, diverse datasets such as EHRs, medical imaging, and genomic data, analyzed through AI algorithms to enhance patient outcomes and operational efficiency"-- Provided by publisher. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.