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AGILE SOFTWARE DEVELOPMENT A unique title that introduces the whole range of agile software development processes from the fundamental concepts to the highest levels of applications such as requirement analysis, software testing, quality assurance, and risk management. Agile Software Development (ASD) has become a popular technology because its methods apply to any programming paradigm. It is important in the software development process because it emphasizes incremental delivery, team collaboration, continuous planning, and learning over delivering everything at once near the end. Agile has gained popularity as a result of its use of various frameworks, methods, and techniques to improve software quality. Scrum is a major agile framework that has been widely adopted by the software development community. Metaheuristic techniques have been used in the agile software development process to improve software quality and reliability. These techniques not only improve quality and reliability but also test cases, resulting in cost-effective and time-effective software. However, many significant research challenges must be addressed to put such ASD capabilities into practice. With the use of diverse techniques, guiding principles, artificial intelligence, soft computing, and machine learning, this book seeks to study theoretical and technological research findings on all facets of ASD. Also, it sheds light on the latest trends, challenges, and applications in the area of ASD. This book explores the theoretical as well as the technical research outcomes on all the aspects of Agile Software Development by using various methods, principles, artificial intelligence, soft computing, and machine learning. Audience The book is designed for computer scientists and software engineers both in research and industry. Graduate and postgraduate students will find the book accessible as well.

This book promotes the idea of creative thinking to those working on qualitative and quantitative analysis for reinforcing engineering designs relating to industrial and manufacturing domains. The book focuses on the development of the theoretical foundations and discusses the utility of integration of critical components and systems engineering for effective systems design. The book presents research directions and can be used as a tool to understand in-depth possible ways related to the management of industrial and manufacturing activities, processes, actions, benchmarks, and so on. The new ideas, theories, and methods related to qualitative and quantitative research will improve manufacturing knowledge linked to the manufacturing process. Readers will find that the book: Explains qualitative and quantitative research for exploiting system characteristics and attaining system efficiency, as well as what decision-making tools are available for attaining sustainability in industrial fields; Discusses the utility of qualitative and quantitative models and analytical frameworks for enduring sustainability and the exploration of new ideas, critical theories, and methods related to qualitative and quantitative research; Disseminates industrial and manufacturing knowledge and allied boundaries, including case studies and sustainable ways to retain excellence in industrial and manufacturing domains based on qualitative and quantitative analysis; Presents critical aspects related to lean manufacturing and lean management tools, and demonstrates pure research and practical solutions to manufacturing problems; Offers theoretical content and demonstrations of manufacturing applications, as well as critical thinking and methodological support for optimizing resources and consumption; Reviews the development of supply chain network designs based on qualitative and quantitative aspects.

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.