Vania V. Estrela – författare

The book sheds light on medical cyber-physical systems while addressing image processing, microscopy, security, biomedical imaging, automation, robotics, network layers’ issues, software design, and biometrics, among other areas. Hence, solving the dimensionality conundrum caused by the necessity to balance data acquisition, image modalities, different resolutions, dissimilar picture representations, subspace decompositions, compressed sensing, and communications constraints. Lighter computational implementations can circumvent the heavy computational burden of healthcare processing applications. Soft computing, metaheuristic, and deep learning ascend as potential solutions to efficient super-resolution deployment. The amount of multi-resolution and multi-modal images has been augmenting the need for more efficient and intelligent analyses, e.g., computer-aided diagnosis via computational intelligence techniques. This book consolidates the work on artificial intelligence methods and clever design paradigms for healthcare to foster research and implementations in many domains. It will serve researchers, technology professionals, academia, and students working in the area of the latest advances and upcoming technologies employing smart systems’ design practices and computational intelligence tactics for medical usage. The book explores deep learning practices within particularly difficult computational types of health problems. It aspires to provide an assortment of novel research works that focuses on the broad challenges of designing better healthcare services.

The book sheds light on medical cyber-physical systems while addressing image processing, microscopy, security, biomedical imaging, automation, robotics, network layers’ issues, software design, and biometrics, among other areas. Hence, solving the dimensionality conundrum caused by the necessity to balance data acquisition, image modalities, different resolutions, dissimilar picture representations, subspace decompositions, compressed sensing, and communications constraints. Lighter computational implementations can circumvent the heavy computational burden of healthcare processing applications. Soft computing, metaheuristic, and deep learning ascend as potential solutions to efficient super-resolution deployment. The amount of multi-resolution and multi-modal images has been augmenting the need for more efficient and intelligent analyses, e.g., computer-aided diagnosis via computational intelligence techniques. This book consolidates the work on artificial intelligence methods and clever design paradigms for healthcare to foster research and implementations in many domains. It will serve researchers, technology professionals, academia, and students working in the area of the latest advances and upcoming technologies employing smart systems’ design practices and computational intelligence tactics for medical usage. The book explores deep learning practices within particularly difficult computational types of health problems. It aspires to provide an assortment of novel research works that focuses on the broad challenges of designing better healthcare services.

Feeding an expanding population in light of environmental preservation and keeping production lean is a considerable challenge. Sustainable and Precise Agriculture relying on Industry 4.0 (Agri4) points toward creating strategic collaborations, innovations, and alternative approaches to old concerns, and incorporating solutions from other expertise domains. It requires intensive efforts from governments, investors, investigators, and developers. These initiatives address many problems while connecting more agrarians and their communities. Agri4 entails key agribusiness developments, viz. precision agriculture, biotech, IoT, and Big Data, to augment efficiency in dealing with growing population and climate change. Agri4 is likely to yield improvements such as sustainable practices education, environmental residue reduction, seed improvement, and traditional agricultural knowledge usage, among others. It promotes sustainable food production, community development, and scientific literacy by fostering effective natural resource administration.

This book explores the transformative impact of Agriculture 4.0, guiding readers through the integration of digital technologies and sustainable practices into modern farming. It discusses how innovations like AI and the IoT are revolutionizing agriculture, enhancing connectivity, precision, and ecological awareness. The intended readers of the book include agricultural professionals, researchers, tech innovators, policymakers, and students. It caters to those interested in the convergence of modern technology and farming, particularly in advancing sustainable practices and understanding the future of agriculture in the digital age.

Feeding an expanding population in light of environmental preservation and keeping production lean is a considerable challenge. Sustainable and Precise Agriculture relying on Industry 4.0 (Agri4) points toward creating strategic collaborations, innovations, and alternative approaches to old concerns, and incorporating solutions from other expertise domains. It requires intensive efforts from governments, investors, investigators, and developers. These initiatives address many problems while connecting more agrarians and their communities. Agri4 entails key agribusiness developments, viz. precision agriculture, biotech, IoT, and Big Data, to augment efficiency in dealing with growing population and climate change. Agri4 is likely to yield improvements such as sustainable practices education, environmental residue reduction, seed improvement, and traditional agricultural knowledge usage, among others. It promotes sustainable food production, community development, and scientific literacy by fostering effective natural resource administration.

This book explores the transformative impact of Agriculture 4.0, guiding readers through the integration of digital technologies and sustainable practices into modern farming. It discusses how innovations like AI and the IoT are revolutionizing agriculture, enhancing connectivity, precision, and ecological awareness. The intended readers of the book include agricultural professionals, researchers, tech innovators, policymakers, and students. It caters to those interested in the convergence of modern technology and farming, particularly in advancing sustainable practices and understanding the future of agriculture in the digital age.

This book explores the application of deep learning techniques within a particularly difficult computational type of computer vision (CV) problem ─ super-resolution (SR). The authors present and discuss ways to apply computational intelligence (CI) methods to SR. The volume also explores the possibility of using different kinds of CV techniques to develop and enhance the tools/processes related to SR. The application areas covered include biomedical engineering, healthcare applications, medicine, histology, and material science. The book will be a valuable reference for anyone concerned with multiple multimodal images, especially professionals working in remote sensing, nanotechnology and immunology at research institutes, healthcare facilities, biotechnology institutions, agribusiness services, veterinary facilities, and universities.