Technological tools and computational techniques have enhanced the healthcare industry. These advancements have led to significant progress and novel opportunities for biomedical engineering. Nature-Inspired Intelligent Techniques for Solving Biomedical Engineering Problems is a pivotal reference source for emerging scholarly research on trends and techniques in the utilization of nature-inspired approaches in biomedical engineering. Featuring extensive coverage on relevant areas such as artificial intelligence, clinical decision support systems, and swarm intelligence, this publication is an ideal resource for medical practitioners, professionals, students, engineers, and researchers interested in the latest developments in biomedical technologies.

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.

In the present era, technological developments are increasing the efficiency and potential of each stakeholder in a business. Robotic process automation is one of the key areas that can be applied in business organizations and corporate sectors to enhance productivity and show a path to success. Application and Adoption of Robotic Process Automation for Smart Cities provides relevant theoretical frameworks and various developments in the area of robotic process automation. Covering topics such as banking and financial services, public engagement, and smart cities, this premier reference source is a valuable resource for business leaders, IT managers, government officials, engineers, students and educators of higher education, researchers, and academicians.

Researchers working on natural resource management issues in human-influenced landscapes need to be able to elicit both biophysical and socio-economic information and explore the interactions between these realms to identify appropriate management options. Biophysical scientists are increasingly interested in researching natural resource management. This, however, requires a sensitivity to the socio-ecological dimensions in a landscape and at least a basic understanding of how to incorporate such in the research designing phase. Natural Resource Management Issues in Human-Influenced Landscapes documents the firsthand research designing experience of prolific researchers for the knowledge of budding researchers, which is not usually shared in journal publications. The chapters showcase cases narrated by the authors about their field experiences and cross-country comparisons. Covering topics such as biodiversity, plant genetic resources, and sustainable production, this premier reference source is an essential resource for ecologists, government officials, students and educators of higher education, librarians, researchers, and academicians.

Metal-organic frameworks (MOFs) are some of the most discussed materials of the last decade. Their extraordinary porosity and functionality from metals and organic linkers make them one of the most promising materials for a vast array of applications. The easy tunability of their pore size and shape from the micro- to meso-scale by changing the connectivity of the inorganic moiety and the nature of the organic linkers makes these materials special. Moreover, by combining with other suitable materials, the properties of MOFs can be improved further for enhanced functionality/stability, ease of preparation, and selectivity of operation. Emerging Applications and Implementations of Metal-Organic Frameworks combines the latest empirical research findings with relevant theoretical frameworks in this area in order to improve the reader’s understanding of MOFs and their different applications in areas that include drug delivery, heavy metal removal from water, and gas storage. The design and synthesis of MOFs are also investigated along with the preparation of composites of MOFs. While covering applications that include water defluoridation, rechargeable batteries, and pharmaceutically adapted drug delivery systems, the book’s target audience is comprised of professionals, researchers, academicians, and students working in the field of physical and polymer chemistry, physics, engineering science, and environmental science.

Understanding the recent developments in renewable energy is crucial for a range of fields in today’s society. As environmental awareness and the need for a more sustainable future continues to grow, the uses of renewable energy, particularly in areas such as smart grid, must be considered and studied thoroughly to be implemented successfully and move society toward a more sustainable future. Optimal Planning of Smart Grid With Renewable Energy Resources offers a detailed guide to the new problems and opportunities for sustainable growth in engineering by focusing on modeling diverse problems occurring in science and engineering as well as novel effective theoretical methods and robust optimization theories, which can be used to analyze and solve multiple types of problems. Covering topics such as electric drives and energy systems, this publication is ideal for researchers, academicians, industry professionals, engineers, scholars, instructors, and students.

In the age of immediate technical expansion, our world faces a multifaceted challenge: ensuring the sustainability of our digital transformation. Governments and organizations have wholeheartedly embraced innovative technologies such as artificial intelligence, blockchain, and e-governance, but in doing so, they have encountered a complex web of issues. These range from cybersecurity concerns in an increasingly digitalized world to the need for intelligent systems capable of managing automation infrastructure and interconnected environments. Sustainable Development in AI, Blockchain, and E-Governance Applications offers a forward-thinking approach that harnesses the synergy between intelligent systems, machine learning, deep learning, and blockchain methods. It explores data-driven decision-making, automation infrastructure, autonomous transportation, and the creation of connected buildings, all aimed at crafting a sustainable digital future. By delving into topics like machine learning for smart parking, disease classification through neural networks, and the Internet of Things (IoT) for smarter cities, this book equips academic scholars with the tools they need to navigate the complex terrain of technology and governance. Academic scholars and researchers in technology, governance, and sustainability will find this book to be an indispensable resource. It caters to those seeking a comprehensive understanding of current and future trends in the integration of intelligent systems with cybersecurity applications.