Vishal Jain

Sarika Jain

Ankur Choudhary

Ankita Bansal

Abha Jain

Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book: Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

Computational Intelligence Techniques and Their Applications to Software Engineering Problems

Rajendra Kumar

Ahmed A. Elngar

Ahed Al-Haraizah

This book focuses on the design, development, and analysis of augmented and virtual reality (AR/VR)-based systems, along with the technological impacts and challenges in social learning. Social Learning provides a comprehensive approach to researching methods in the emerging fields of AR/VR. The contributors of this book outline the state-of-the-art implementation of AR/VR for the Internet of Things, Blockchains, Big Data, and 5G within AR/VR systems.

Technological Impacts and Challenges

Augmented and Virtual Reality in Social Learning

Aboul Ella Hassanien

Jyotir Moy Chatterjee

Artificial Intelligence and Industry 4.0 explores recent advancements in blockchain technology and artificial intelligence (AI) as well as their crucial impacts on realizing Industry 4.0 goals. The book explores AI applications in industry including Internet of Things (IoT) and Industrial Internet of Things (IIoT) technology. Chapters explore how AI (machine learning, smart cities, healthcare, Society 5.0, etc.) have numerous potential applications in the Industry 4.0 era. This book is a useful resource for researchers and graduate students in computer science researching and developing AI and the IIoT. Explores artificial intelligence applications within the industrial manufacturing and communications sectors Presents a wide range of machine learning, computer vision, and digital twin applications across the IoT sector Explores how deep learning and cognitive computing tools enable processing vast data sets, precise and comprehensive forecast of risks, and delivering recommended actions

Artificial Intelligence and Industry 4.0

Vishal Kumar

Bharti Sharma

Rakesh Shrestha

SMART CITY INFRASTRUCTURE The wide range of topics presented in this book have been chosen to provide the reader with a better understanding of smart cities integrated with AI and blockchain and related security issues. The goal of this book is to provide detailed, in-depth information on the state-of-the-art architecture and infrastructure used to develop smart cities using the Internet of Things (IoT), artificial intelligence (AI), and blockchain security—the key technologies of the fourth industrial revolution. The book outlines the theoretical concepts, experimental studies, and various smart city applications that create value for inhabitants of urban areas. Several issues that have arisen with the advent of smart cities and novel solutions to resolve these issues are presented. The IoT along with the integration of blockchain and AI provides efficient, safe, secure, and transparent ways to solve different types of social, governmental, and demographic issues in the dynamic urban environment. A top-down strategy is adopted to introduce the architecture, infrastructure, features, and security. Audience The core audience is researchers in artificial intelligence, information technology, electronic and electrical engineering, systems engineering, industrial engineering as well as government and city planners.

The Blockchain Perspective

Smart City Infrastructure

Salah-ddine Krit

Uzzal Sharma

Parmanand Astya

Anupam Baliyan

Mohammad Zubair Khan

Security systems have become an integral part of the building and large complex setups, and intervention of the computational intelligence (CI) paradigm plays an important role in security system architecture. This book covers both theoretical contributions and practical applications in security system design by applying the Internet of Things (IoT) and CI. It further explains the application of IoT in the design of modern security systems and how IoT blended with computational intel- ligence can make any security system improved and realizable. Key features: Focuses on the computational intelligence techniques of security system design Covers applications and algorithms of discussed computational intelligence techniques Includes convergence-based and enterprise integrated security systems with their applications Explains emerging laws, policies, and tools affecting the landscape of cyber security Discusses application of sensors toward the design of security systems This book will be useful for graduate students and researchers in electrical, computer engineering, security system design and engineering.

Advancing Computational Intelligence Techniques for Security Systems Design

Sapna Juneja

Abhinav Juneja

Ramani Kannan

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach, which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications, and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers, professionals, and application designers.

Applications and Case Studies

Handbook of Machine Learning for Computational Optimization

Recently, the Semantic Web has gained huge popularity to address these challenges. Semantic web technologies have the opportunity to transform the way healthcare providers utilize technology to gain insights and knowledge from their data and make decisions. Both big data and semantic web technologies can complement each other to address the challenges and add intelligence to healthcare management systems. The aim of this book is to analyze the current status on how Semantic Web is used to solve the health data integration and interoperability problem, how it provides advanced data linking capabilities that can improve search and retrieval of medical data. There are chapters in the book which analyze the tools and approaches to semantic health data analysis and knowledge discovery. The book discusses the role of semantic technologies in extracting and transforming healthcare data before storing it in repositories. It also discusses different approaches for integrating heterogeneous healthcare data. To summarize, the book will help readers understand key concepts in semantic web applications for biomedical engineering and healthcare.

Semantic Web for Effective Healthcare Systems

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Machine Learning and Healthcare

Machine Learning with Health Care Perspective

Valentina Emilia Balas

Web Semantics strengthen the description of web resources to exploit them better and make them more meaningful for both humans and machines, thereby contributing to the development of a knowledgeintensive data web. The world is experiencing the movement of concept from data to knowledge and the movement of web from document model to data model. The underlying idea is making the data machine understandable and processable. In the light of these trends, conciliation of Semantic and the Web is of paramount importance for further progress in the area. Web Semantics: Cutting Edge and Future Directions in Healthcare describes the three major components of the study of Semantic Web, namely Representation, Reasoning, and Security with a special focus on the healthcare domain. This book summarizes the trends and current research advances in web semantics, emphasizing the existing tools and techniques, methodologies, and research solutions. It provides easily comprehensible information on Web Semantics including semantics for data and semantics for services. Presents a comprehensive examination of the emerging research in areas of the semantic web, including ontological engineering, semantic annotation, reasoning and intelligent processing, semantic search paradigms, semantic web mining, and semantic sentiment analysis Helps readers understand key concepts in semantic web applications for biomedical engineering and healthcare, including mapping disparate knowledge bases, security issues, multilingual semantic web, and integrating databases with knowledge bases Includes coverage of key application areas of the semantic web, including clinical decision-making, biodiversity science, interactive healthcare, intelligent agent systems, decision support systems, and clinical natural language processing

Cutting Edge and Future Directions in Healthcare

Web Semantics

Vishal Goyal

Poonam Tanwar

Chuan-Ming Liu

Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

A Perspective for Health Care

Big Data Analytics and Intelligence

Vishal Jain

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