This contributed volume comprises of detailed chapters covering the biotechnological approaches employed for the removal of toxic recalcitrant xenobiotics such as petroleum hydrocarbons, textile dyes, microplastics and synthetic polymers that pose serious threat to the environment. It also includes the waste to energy conversion strategies that provides a deep insight on the need for waste circular economy for different developing countries and its implication on sustainable development goals such as SDG 12 (responsible consumption and production) SDG 14 (Life below water); and SDG 15 (Life on land). Emerging pollutants sourced from both industries and anthropogenic activity have created havoc in recent years for public health and destruction of biodiversity at multiple levels. The alarming increase in the global population and rapid industrialization might aggravate the problems associated with these hazardous pollutants in near future. Effluent from different industries may contain high amounts of xenobiotic hazardous contaminants such as dyes, hydrocarbons, synthetic surfactants, microplastics, etc. Industries and public sewers handling such waste streams are facing a plethora of challenges in the effluent treatment and solid waste disposal due to various factors that start from production to adoption of appropriate technologies. Therefore, there is an immediate circumvention of bottlenecks through sustainable mitigation strategies. This book is of interest to teachers, researchers, climate change scientists, capacity builders and policymakers. Also, the book serves as additional reading material for undergraduate and graduate students of agriculture, forestry, ecology, soil science, and environmental sciences. National and international agricultural scientists, policy makers also find this to be a useful read.

Recently, the internet of things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. With the outbreak of the COVID-19 pandemic, artificial intelligence (AI) has gained significant attention by utilizing its machine learning algorithms for quality patient care. The integration of IoT with AI may open new possibilities for both technologies and can play a big part in smart healthcare by providing improved insight into healthcare data and allowing for more inexpensive personalized care. The Handbook of Research on Advancements in AI and IoT Convergence Technologies considers recent advancements in AI and IoT convergence technologies with a focus on state-of-the-art approaches, methodologies, and systems for the design, development, deployment, and innovative use of those convergence technologies. It also provides insight into how to develop AI and IoT convergence techniques to meet industrial demands and covers the emerging research topics that are going to define the future of AI and IoT convergence technology development. Covering key topics such as diseases, smart healthcare, social distance monitoring, and security, this major reference work is ideal for industry professionals, nurses, healthcare workers, computer scientists, policymakers, researchers, scholars, practitioners, instructors, and students.

Recently, artificial intelligence (AI), the internet of things (IoT), and cognitive technologies have successfully been applied to various research domains, including computer vision, natural language processing, voice recognition, and more. In addition, AI with IoT has made a significant breakthrough and a shift in technical direction to achieve high efficiency and adaptability in a variety of new applications. On the other hand, network design and optimization for AI applications addresses a complementary topic, namely the support of AI-based systems through novel networking techniques, including new architectures, as well as performance models for IoT systems. IoT has paved the way to a plethora of new application domains, at the same time posing several challenges as a multitude of devices, protocols, communication channels, architectures, and middleware exist. Big data generated by these devices calls for advanced learning and data mining techniques to effectively understand, learn, and reason with this volume of information, such as cognitive technologies. Cognitive technologies play a major role in developing successful cognitive systems which mimic ""cognitive"" functions associated with human intelligence, such as ""learning"" and ""problem solving."" Thus, there is a continuing demand for recent research in these two linked fields. Innovations and Applications of AI, IoT, and Cognitive Technologies discusses the latest innovations and applications of AI, IoT, and cognitive-based smart systems. The chapters cover the intersection of these three fields in emerging and developed economies in terms of their respective development situation, public policies, technologies and intellectual capital, innovation systems, competition and strategies, marketing and growth capability, and governance and relegation models. These applications span areas such as healthcare, security and privacy, industrial systems, multidisciplinary sciences, and more. This book is ideal for technologists, IT specialists, policymakers, government officials, academics, students, and practitioners interested in the experiences of innovations and applications of AI, IoT, and cognitive technologies.

Necessity is the mother of invention; challenging times can provide new opportunities that must be detected and exploited at the right moments. The COVID-19 pandemic has demonstrated that it is not only an issue of healthcare but also a challenge for the global economy, business, and society. Organizations have rapidly deployed technology solutions that enable them to work and service remotely and continue most of their normal operations. Technologies and Systems for E-Collaboration During Global Crises focuses on emerging technologies and systems, strategies, and solutions for e-collaboration. This book assesses the importance of technologies and systems for e-collaboration in dealing with emerging crises such as pandemics. Covering topics such as deep learning processes, machine vision, and profit-sharing models, it is an essential resource for computer scientists, public officials, engineers, students and professors of higher education, healthcare administration, programmers, researchers, and academicians.