Machine Learning And Internet Of Things In Fire Ecology
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Machine Learning and Internet of Things in Fire Ecology
The destruction of millions of acres of forest land through wildfires is a global cause of concern. Artificial intelligence (AI), transformative in nature, has the potential to transcend and significantly mitigate risk factors of wildfires. AI-driven monitoring systems can detect early signs of wildfire activity, allowing for faster, more targeted responses that can minimize damage and save lives. Machine Learning and Internet of Things in Fire Ecology elucidates and explores the interface of fire ecology with AI, machine learning, and internet of things, as these technologies emerged as a pivotal domain with transformative potential. It will assist environmental-related industries in understanding the paraphernalia and dynamics of the fire ecology ecosystem. Covering topics such as AI, unmanned aerial vehicles (UAVs), and wildlife conservation, this book is an excellent resource for government officials, ecologists, academicians, policymakers, researchers, environmental specialists, industry experts, graduate and postgraduate students, and more.
Sustainable Futures With Life Cycle Assessment in Industry 5.0
As Industry 5.0 prioritizes human-centric, sustainable, and resilient production, life cycle assessment plays a crucial role in evaluating environmental impacts across industries. By integrating life cycle assessment, businesses can make data-driven decisions to reduce waste, optimize resource use, and minimize carbon footprints. This approach supports the transition toward circular economies, ensuring that technological advancements align with ecological responsibility. As sustainability becomes a global priority, life cycle assessment empowers industries, policymakers, and consumers to drive meaningful change toward a more sustainable future. Sustainable Futures With Life Cycle Assessment in Industry 5.0 explores life cycle assessment in Industry 5.0, emphasizing sustainable production, resource optimization, and environmental impact reduction. Through expert insights and case studies, it provides a comprehensive guide for integrating life cycle assessment into next-generation industrial practices to drive sustainability and innovation. Covering topics such as recommendation systems, community product marketing, and currency exchange rates, this book is an excellent resource for economists, business leaders, computer scientists, professionals, researchers, scholars, academicians, and more.
Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution
The convergence of Internet of Things (IoT), fog computing, and blockchain technology can be used to revolutionize energy efficiency and sustainability. The implementation of deep learning (DL) techniques may optimize the energy consumption of these interconnected systems. Thus, they can be used to create green, energy-efficient solutions for various industries, including smart cities, healthcare, finance, and industrial IoT (IIoT). Focusing on the energy efficiency and environmental impact of these technologies, they provide valuable insights into creating sustainable and scalable systems. Energy-Efficient Deep Learning Approaches in IoT, Fog, and Green Blockchain Revolution bridges the knowledge gap between traditional IoT and blockchain research and the emerging need for energy-efficient and green technologies. It influences future research directions, encourages collaboration across disciplines, and inspires innovations that prioritize sustainability. Covering topics such as software-defined networking (SDN), ecosystem conservation, and monitoring systems, this book is an excellent resource for computer scientists, policymakers, technologists, industry practitioners, engineers, environmentalists, sustainability advocates, professionals, researchers, scholars, academicians, and more.