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Convergence Strategies for Green Computing and Sustainable Development presents a comprehensive exploration of the potential of emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), fog computing, and cloud computing, to aid in fostering a sustainable future. It examines how these technologies can reduce the impact of unsustainability in societies, the environment, and natural resources, offering invaluable insights into harnessing their power for positive change. Convergence Strategies for Green Computing and Sustainable Development serves as a comprehensive strategy that holistically understands, transforms, and develops technological systems in society. This book caters to a diverse range of readers, including graduate students, researchers, working professionals seeking knowledge, and industry experts seeking information about new trends. With its recommended topics and comprehensive table of contents, readers can gain in-depth knowledge about sustainable cloud computing, artificial intelligence and machine learning for sustainable development, sustainable wireless systems and networks, and the crucial role of green IoT and Edge-AI in driving a sustainable digital transition.
This book comprehensively discusses the role of cloud computing in artificial intelligence‐based data‐driven systems and hybrid cloud computing for large data‐driven applications. It further explores new approaches, paradigms, and frameworks to meet societal challenges by providing solutions for critical insights into data. The text provides Internet of Things‐based frameworks and advanced computing techniques to deal with online/virtual systems. This book: • Covers the aspects of security, authentication, and prediction for data‐driven systems in heterogeneous environments. • Provides data‐driven frameworks in combination with the Internet of Things, artificial intelligence, and computing to provide critical insights and decision‐making for real‐time problems. • Showcases deep learning‐based computer vision algorithms for enhanced pattern detection in different domains based on data‐centric approaches. • Examines the role of the Internet of Things and machine learning algorithms for data‐driven systems. • Highlights the applications of data‐driven systems and cloud computing in enhancing network performance. This book is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, and computer science engineering.
As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.
As the world grapples with pressing environmental challenges, the need for sustainable solutions has never been more urgent. From climate change to resource depletion, our planet faces unprecedented threats that require immediate action. Revolutionizing Automated Waste Treatment Systems: IoT and Bioelectronics emerge as a beacon of hope, offering comprehensive insights and practical guidance to address these critical issues. By delving into the principles and applications of green technologies, this book presents a roadmap towards a greener, more sustainable future. Recognizing the essential role that green technologies play in mitigating environmental degradation, this book emphasizes concepts such as smart technologies, bioelectronics, and the internet of things. It also illustrates how these innovations can be leveraged to create a more sustainable world. Readers will be educated on the importance of adopting these technologies, and the book provides actionable strategies for implementation. Use this impressive resource to grasp a more holistic approach to environmental sustainability, from designing green infrastructure to managing water resources.
In an era overshadowed by pressing global challenges such as climate change, burgeoning populations, and the depletion of natural resources, the agricultural landscape is at a critical juncture. The need for sustainable practices has never been more urgent, with conventional methods struggling to meet the demands of a growing population while grappling with environmental degradation. Harnessing NanoOmics and Nanozymes for Sustainable Agriculture delves into the heart of the problem, navigating the intricate web of challenges facing agriculture today. From dwindling crop yields to the environmental repercussions of conventional farming practices, the urgency to find innovative, sustainable solutions is paramount. Harnessing NanoOmics and Nanozymes for Sustainable Agriculture offers a comprehensive exploration of nanotechnology's potential to revolutionize agriculture, presenting a promising pathway toward enhanced productivity, minimizing environmental impact, and optimal resource utilization.
In our world today, the pervasive threat of air, water, and soil contaminants has reached unprecedented levels, pushing ecosystems to the brink and causing harm to individuals worldwide. Despite numerous attempts by scholars to mitigate this crisis, we find ourselves in the infancy of understanding and combatting these pollutants. The lack of awareness among researchers regarding the types and extent of damage caused by contaminants further exacerbates the problem. This environmental dilemma calls for a transformative solution that not only identifies pollutants but also guides sustainable efforts to cleanse our vital ecosystems. Sensors for Environmental Monitoring, Identification, and Assessment is a groundbreaking book designed to revolutionize environmental research and provide a roadmap for tackling pollution head-on. This comprehensive guide is poised to make a significant impact on scholars, environmentalists, planners, researchers, industrialists, and academics globally. By delving into the diverse realms of environmental sensors, the book equips readers with the knowledge and tools necessary to identify pollutants in varied ecosystems and adopt sustainable approaches for cleanup. Its recommended topics cover critical areas such as indoor pollution, noise pollution, advancements in sensor technology, and the detection of pollutants in soil, water, air, and oceans.
In an era defined by relentless human activities and rapid ecological transformations, the world faces an escalating crisis – the precipitous loss of biodiversity. As we grapple with the consequences of industrialization, urbanization, and unchecked developmental pursuits, the very fabric of life on Earth is unraveling. Biodiversity, encompassing the myriad species, their genetic variations, and the intricate interplay within ecosystems, is diminishing at an unprecedented pace. This decline, termed biodiversity loss, extends beyond a mere statistical measure; it reflects the unraveling of the intricate tapestry that sustains life on our planet. In the face of climate change, pollution, habitat loss, overexploitation of species, and the invasion of non-native species, the urgency to address biodiversity loss has never been more critical. It is against this backdrop that this book emerges, titled Biodiversity Loss Assessment for Ecosystem Protection. This groundbreaking work not only unveils the theoretical frameworks surrounding biodiversity conservation but also presents the latest empirical research findings, making it an indispensable tool for professionals across diverse disciplines. From stress on biodiversity and impact assessment to innovative approaches for studying terrestrial, aquatic, and marine components, each chapter provides a deep dive into specific facets of biodiversity loss. The objective is clear: to equip scholars with the knowledge they need to contribute meaningfully to the preservation of our planet's rich biological heritage.
In the face of our planet's escalating environmental crisis and climate change, humanity stands at a crossroads, urgently requiring a transformative response. The task of averting environmental destruction necessitates not only a shift in our economy and technology but, more fundamentally, a profound cultural transformation. This imperative transformation involves a collective move from the self-centered "Ego" to an ecologically conscious "Eco." To unravel the complexities of this metamorphosis, scholars are turning to the potent tool of environmental education, recognized for its capacity to foster personal and social growth while promoting environmental conservation. Enter Fostering an Ecological Shift Through Effective Environmental Education, a groundbreaking exploration into the transformative power of education in the pursuit of sustainable change. As readers embark on this scholarly journey, the book reveals the profound psychological connection to nature achievable through environmental education. It scrutinizes the connection between heightened nature awareness and the adoption of sustainable practices, providing valuable insights for educators at various levels. The chapters traverse diverse topics, from the historical roots of environmental education to the role of indigenous knowledge, yoga, and eco-spirituality within nature education. The book's comprehensive approach extends to eco-therapy, forest school programs, and the influence of parents in environmental education. By scrutinizing case studies and global movements, this work illuminates the achievements and challenges of environmental education on both national and global scales.
In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI).