Optimization techniques have developed into a modern-day solution for real-world problems in various industries. As a way to improve performance and handle issues of uncertainty, optimization research becomes a topic of special interest across disciplines. Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications presents the latest research trends and developments in the area of applied optimization methodologies and soft computing techniques for solving complex problems. Taking a multi-disciplinary approach, this critical publication is an essential reference source for engineers, managers, researchers, and post-graduate students.

Emerging scientific and industrial applications in today’s world require significant computing power. Modern software tools are available for such platforms but are relatively complex and require the use of innovative programming models. One promising area in modern software design is the development, analysis, and implementation of algorithms and adaptive methods. These advancements in programming are promising but lack relevant research and understanding. Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities is an essential reference source that solves the problem of the development of efficient models, methods, and tools for parallel programming automation based on the algebra of algorithms, term rewriting, and auto-tuning paradigms. The results of this book will help to further develop and improve existing research on design, synthesis, and optimization of sequential and parallel algorithms and programs. Featuring research on topics such as auto-tuning methods, graphics processing, and algorithmic language, this book is ideally designed for mathematicians, software engineers, data scientists, researchers, academicians, and students seeking coverage on developing tools for automated design and parallel programs.

The book focuses on soft computing and its applications to solve real-world problems occurring in different domains ranging from medicine and healthcare, and supply chain management to image processing and cryptanalysis. It includes high-quality papers presented in the International Conference on Soft Computing: Theories and Applications (SoCTA 2017), organized by Bundelkhand University, Jhansi, India. Offering significant insights into soft computing for teachers and researchers alike, the book inspires more researchers to work in the field of soft computing.

The results of computational model simulations allow researchers and clinicians to make predictions about what will happen in the biological systems that are being studied in response to changing conditions for a disease or disorder. With a well-developed computational model, researchers and clinicians can better understand the cause of a disease or a disorder and predict treatment results. Computational Models for Biomedical Reasoning and Problem Solving is a critical scholarly publication that provides insightful strategies to developing computational models that allow for the better understanding and treatment of various diseases and disorders. Featuring topics such as biomedicine, neuroscience, and artificial intelligence, this book is ideal for practitioners, clinicians, researchers, psychologists, and engineers.

This book reports on advanced theories and cutting-edge applications in the field of soft computing. The individual chapters, written by leading researchers, are based on contributions presented during the 4th World Conference on Soft Computing, held May 25-27, 2014, in Berkeley. The book covers a wealth of key topics in soft computing, focusing on both fundamental aspects and applications. The former include fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation and neural networks, while the latter include soft computing in data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling. By providing readers with a timely, authoritative view on the field, and by discussing thought-provoking developments and challenges, the book will foster new research directions in the diverse areas of soft computing.

Nature-inspired computation is an interdisciplinary topic area that connects the natural sciences to computer science. Since natural computing is utilized in a variety of disciplines, it is imperative to research its capabilities in solving optimization issues. The Handbook of Research on Natural Computing for Optimization Problems discusses nascent optimization procedures in nature-inspired computation and the innovative tools and techniques being utilized in the field. Highlighting empirical research and best practices concerning various optimization issues, this publication is a comprehensive reference for researchers, academicians, students, scientists, and technology developers interested in a multidisciplinary perspective on natural computational systems.

Low temperature plasma in medicine is a new field that rose from the research in the application of cold plasmas in bioengineering. Plasma medicine is an innovative and promising multidisciplinary novel field of research covering plasma physics, life sciences, and clinical medicine to apply physical plasma for therapeutic applications. Emerging Developments and Applications of Low Temperature Plasma explores all areas of experimental, computational, and theoretical study of low temperature and atmospheric plasmas and provides a collection of exciting new research on the fundamental aspects of low temperature and pressure plasmas and their applications. Covering topics such as carbon nanotubes, foodborne pathogens, and plasma formation, this book is an essential resource for research groups, plasma-based industries, plasma aerodynamics industries, metal and cutlery industries, medical institutions, researchers, and academicians.

In this technology-driven era, conventional manufacturing is increasingly at risk of reaching its limit, and a more design-driven manufacturing process, additive manufacturing, might just hold the key to innovation. Offering a higher degree of design freedom, the optimization and integration of functional features, and the manufacturing of small batch sizes, additive manufacturing is changing industry as we know it. Additive Manufacturing Technologies From an Optimization Perspective is a critical reference source that provides a unified platform for the dissemination of basic and applied knowledge about additive manufacturing. It carefully examines how additive manufacturing is increasingly being used in series production, giving those in the most varied sectors of industry the opportunity to create a distinctive profile for themselves based on new customer benefits, cost-saving potential, and the ability to meet sustainability goals. Highlighting topics such as bio-printing, tensile strength, and cell printing, this book is ideally designed for academicians, students, engineers, scientists, software developers, architects, entrepreneurs, and medical professionals interested in advancements in next-generation manufacturing.

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 recent years, substantial efforts are being made in the development of reliability theory including fuzzy reliability theories and their applications to various real-life problems. Fuzzy set theory is widely used in decision making and multi criteria such as management and engineering, as well as other important domains in order to evaluate the uncertainty of real-life systems. Fuzzy reliability has proven to have effective tools and techniques based on real set theory for proposed models within various engineering fields, and current research focuses on these applications. Advancements in Fuzzy Reliability Theory introduces the concept of reliability fuzzy set theory including various methods, techniques, and algorithms. The chapters present the latest findings and research in fuzzy reliability theory applications in engineering areas. While examining the implementation of fuzzy reliability theory among various industries such as mining, construction, automobile, engineering, and more, this book is ideal for engineers, practitioners, researchers, academicians, and students interested in fuzzy reliability theory applications in engineering areas.