Adarsh Garg – författare

The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and technology for forecasting and predicting healthcare disasters as well as for helping to restrict the subsequent spread and fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information for designing healthcare disaster management systems that can be dynamically configurable with implementation of today’s modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning. These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks, geographic information system (GIS), and global positioning System (GPS), to name a few.

This book discusses mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for applications in epidemiology. The authors also address the use of mobile applications for communication efforts and remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in areas of conflict and crisis.

This important volume, Global Healthcare Disasters: Predicting the Unpredictable with Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers, governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with emerging technologies.

The recent COVID-19 global pandemic exemplifies the need for efficient, reliable, and real-time tools and technology for forecasting and predicting healthcare disasters as well as for helping to restrict the subsequent spread and fatality of deadly diseases. This new book discusses many of the innovative and state-of-the-art tools and technology that can help meet the challenges of predicting such disasters. The chapters offer a plethora of useful information for designing healthcare disaster management systems that can be dynamically configurable with implementation of today’s modern technology, such as cloud computing, artificial intelligence, IoT, data analytics, and machine learning. These can increase effectiveness in remote sensing technologies, data analytics, data storage, communication networks, geographic information system (GIS), and global positioning System (GPS), to name a few.

This book discusses mathematical models using graph-based approaches for analyzing dynamic, heterogeneous, and unstructured data for applications in epidemiology. The authors also address the use of mobile applications for communication efforts and remote monitoring for gauging health and the effectiveness of preventive healthcare measures. The chapters discuss influencing factors that directly or indirectly target public health infrastructure that can lead to or exacerbate global health crises, such as extreme climate changes, refugee health crises, terrorism and cyberterrorism, and technology-related incidents. The book further looks at efficient methods to analyze disasters and how to deliver healthcare in areas of conflict and crisis.

This important volume, Global Healthcare Disasters: Predicting the Unpredictable with Emerging Technologies, provides a bounty of useful information for health professionals, academicians, researchers, governmental agencies, and policymakers across the world to predict, mitigate, and manage global health disaster with emerging technologies.

The evolution of technology in education can no longer be comprehended simply by looking at the use of computers and networks. Technology is not just a supplementary tool to the conventional method of education. Education has to undergo a complete transformation with technological innovations for the sustainability of quality education as a system and not in silos. Sustainability in education also necessitates a more workable strategy to realize socially viable educational policies and practices which can focus more on personalized learning. Due to various factors like emerging technologies; changing needs of the learners; policy reforms for enhancing employability; and emphasis on uninterrupted education as in the case of the pandemic scenario of COVID-19, there is a need to steer a major transition in the education system. The education system has to be real and proficient for it to be instrumental to nurture an informed and knowledgeable society.

This book on technological innovations in higher education is organized, largely, based on the diversity of higher education ecosystems that are supported by technological innovations. Various author viewpoints give insights into the full potential of technology as well as its risks in interrelated areas of higher education to work towards sustainability of value-based quality education across the globe.

The evolution of technology in education can no longer be comprehended simply by looking at the use of computers and networks. Technology is not just a supplementary tool to the conventional method of education. Education has to undergo a complete transformation with technological innovations for the sustainability of quality education as a system and not in silos. Sustainability in education also necessitates a more workable strategy to realize socially viable educational policies and practices which can focus more on personalized learning. Due to various factors like emerging technologies; changing needs of the learners; policy reforms for enhancing employability; and emphasis on uninterrupted education as in the case of the pandemic scenario of COVID-19, there is a need to steer a major transition in the education system. The education system has to be real and proficient for it to be instrumental to nurture an informed and knowledgeable society.

This book on technological innovations in higher education is organized, largely, based on the diversity of higher education ecosystems that are supported by technological innovations. Various author viewpoints give insights into the full potential of technology as well as its risks in interrelated areas of higher education to work towards sustainability of value-based quality education across the globe.

Innovation is a critical facilitator in today’s fast-changing global value chain landscape. This book describes the interplay of technological breakthroughs enabling efficiency and intelligence within value chains, making them smart and sustainable. From service models and smart technologies to the application of smart global value chains across sectors, this book offers a unique insight into the transformational role of smart global value chains in bringing agility and sustainability to the global value chain ecosystem.

This book is an essential guide for academics, industry leaders, and policymakers to navigate the future where smart technologies like artificial intelligence, machine learning, blockchain, Internet of Things, and beyond reshape the global economic landscape.

Innovation is a critical facilitator in today’s fast-changing global value chain landscape. This book describes the interplay of technological breakthroughs enabling efficiency and intelligence within value chains, making them smart and sustainable. From service models and smart technologies to the application of smart global value chains across sectors, this book offers a unique insight into the transformational role of smart global value chains in bringing agility and sustainability to the global value chain ecosystem.

This book is an essential guide for academics, industry leaders, and policymakers to navigate the future where smart technologies like artificial intelligence, machine learning, blockchain, Internet of Things, and beyond reshape the global economic landscape.

Data analytics and generative AI (GenAI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.

Generative AI 2.0 and Data Analytics explores the intersection between GenAI and data analytics and addresses its profound effects on industries and organizations across the globe. Highlights of the book include:

Not only covering the fundamental concepts and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve the quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges, and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.

Data analytics and generative AI (GenAI) are transformative technologies that play a critical role in modern decision-making and innovation. Data analytics enables organizations to extract actionable insights from vast amounts of structured and unstructured data, driving efficiency, improving customer experiences, and identifying trends. Generative AI, on the other hand, enhances creativity and problem-solving by producing new content, such as text, images, and designs, based on learned patterns. Together, they empower people and organizations to make data-driven decisions, automate complex processes, and unlock new opportunities for growth and innovation.

Generative AI 2.0 and Data Analytics explores the intersection between GenAI and data analytics and addresses its profound effects on industries and organizations across the globe. Highlights of the book include:

Not only covering the fundamental concepts and techniques of generative AI and their practical application, the book also investigates how these techniques foster innovation and improve the quality of data in various business domains. It examines a broad range of topics from artificial data generation, security analytics, anomaly detection, reinforcement management, ethical consideration, challenges, and future scenarios. The book also features expert opinions and case studies to provide practical direction and valuable insight.