Entrepreneurs and information technology have formed the backbone of the global economy. The growing use of information technology in society and most industries and businesses, as well as the economic convergence of entrepreneurship and innovation, highlights the need for cutting-edge research on entrepreneurship and innovation in the context of information technology. Futuristic Technology Perspectives on Entrepreneurship and Sustainable Innovation provides rigorous and research-based chapters on the formation and implementation of effective strategies and business plans as well as current studies on the nature, process, and practice of entrepreneurship and innovation in the development, implementation, and application of information technology worldwide. Covering topics such as green entrepreneurship, design thinking, and skill building, this premier reference source is an essential resource for entrepreneurs, business leaders and managers, IT managers, technologists, government officials, medical professionals, financial planners, students and educators of higher education, librarians, researchers, and academicians.

The role humans play in the field of information technology continues to hold relevance even with the industry's rapid growth. People contribute heavily to the physical, cognitive, and organizational domain of computing, yet there is a lack of exploration into this phenomenon. Humanoid aspects of technology require extensive research in order to avoid marginalization and insufficient data. The Handbook of Research on the Role of Human Factors in IT Project Management is a collection of innovative research on the methods and applications of the task of human characteristics in the design and development of new technology. While highlighting topics including digitalization, risk management, and task analysis, this book is ideally designed for IT professionals, managers, support executives, project managers, managing directors, academicians, researchers, and students seeking current research on the dynamics of human influence in technological projects.

This book addresses extensible and adaptable computing, a broad range of methods and techniques used to systematically tackle the future growth of systems and respond proactively and seamlessly to change. The book is divided into five main sections: Agile Software Development, Data Management, Web Intelligence, Machine Learning and Computing in Education. These sub-domains of computing work together in mutually complementary ways to build systems and applications that scale well, and which can successfully meet the demands of changing times and contexts. The topics under each track have been carefully selected to highlight certain qualitative aspects of applications and systems, such as scalability, flexibility, integration, efficiency and context awareness. The first section (Agile Software Development) includes six contributions that address related issues, including risk management, test case prioritization and tools, open source software reliability and predicting the change proneness of software. The second section (Data Management) includes discussions on myriad issues, such as extending database caches using solid-state devices, efficient data transmission, healthcare applications and data security. In turn, the third section (Machine Learning) gathers papers that investigate ML algorithms and present their specific applications such as portfolio optimization, disruption classification and outlier detection. The fourth section (Web Intelligence) covers emerging applications such as metaphor detection, language identification and sentiment analysis, and brings to the fore web security issues such as fraud detection and trust/reputation systems. In closing, the fifth section (Computing in Education) focuses on various aspects of computer-aided pedagogical methods.

Developing a knowledge model helps to formalize the difficult task of analyzing crime incidents in addition to preserving and presenting the digital evidence for legal processing. The use of data analytics techniques to collect evidence assists forensic investigators in following the standard set of forensic procedures, techniques, and methods used for evidence collection and extraction. Varieties of data sources and information can be uniquely identified, physically isolated from the crime scene, protected, stored, and transmitted for investigation using AI techniques. With such large volumes of forensic data being processed, different deep learning techniques may be employed. Confluence of AI, Machine, and Deep Learning in Cyber Forensics contains cutting-edge research on the latest AI techniques being used to design and build solutions that address prevailing issues in cyber forensics and that will support efficient and effective investigations. This book seeks to understand the value of the deep learning algorithm to handle evidence data as well as the usage of neural networks to analyze investigation data. Other themes that are explored include machine learning algorithms that allow machines to interact with the evidence, deep learning algorithms that can handle evidence acquisition and preservation, and techniques in both fields that allow for the analysis of huge amounts of data collected during a forensic investigation. This book is ideally intended for forensics experts, forensic investigators, cyber forensic practitioners, researchers, academicians, and students interested in cyber forensics, computer science and engineering, information technology, and electronics and communication.

This book provides an account of the latest developments in IoT and cloud computing, and their practical applications in various industrial, scientific, business, education, and government domains. The book covers the advanced research and state of the art review of the latest developments in IoT and cloud computing and how they might be employed post-COVID era. The book also identifies challenges and their solutions in this era, shaping the direction for future research and offering emerging topics to investigate further. The book serves as a reference for a broader audience such as researchers, application designers, solution architects, teachers, graduate students, enthusiasts, practitioners, IT managers, decision-makers and policymakers. The book editors are pioneers in the fields of IoT and Cloud computing. Provides an account of the latest developments in IoT and cloud computing and how it can aid in a COVID-19 Era in a variety of applications; Identifies IoT and cloud computing challenges and their solutions, shaping the direction for future research; Serves as a reference for researchers, application designers, solution architects, teachers, and graduate students.

This book provides stepwise discussion, exhaustive literature review, detailed analysis and discussion, rigorous experimentation results (using several analytics tools), and an application-oriented approach that can be demonstrated with respect to data analytics using artificial intelligence to make systems stronger (i.e., impossible to breach). We can see many serious cyber breaches on Government databases or public profiles at online social networking in the recent decade. Today artificial intelligence or machine learning is redefining every aspect of cyber security. From improving organizations' ability to anticipate and thwart breaches, protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to making passwords obsolete, AI and machine learning are essential to securing the perimeters of any business. The book is useful for researchers, academics, industry players, data engineers, data scientists, governmental organizations, and non-governmental organizations.

This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.

Software Engineering Approaches to Enable Digital Transformation Technologies features contributions reflecting ideas and research in enabling digital transformation technologies through software engineering. To date, multiple, different approaches have been adopted to develop software solutions for a variety of different problems. Of all the available approaches, the main approaches are level-oriented, data flow-oriented, data structure-oriented, and object-oriented design approaches. The other focus of the book is digital transformation, which can be defined as the adoption of digital technology to improve efficiency, value, and innovation Digitalization is more than just putting additional technological systems and services in place. Rather than improving conventional methods, a true digital transformation initiative includes radically rethinking company structures and procedures. There are four types of digital transformation: business process, business model, domain, and cultural and organizational. Companies are being challenged to develop new business models that consider and harness digitalization. From the standpoint of software engineering, digital transformation alters how software is built. Current trends include the development of mobile applications, cloud applications, and Internet of Things (IoT) applications. Emerging trends are the development of digital twins, robotics, artificial intelligence, machine learning, augmented reality, and additive manufacturing. This book examines the challenges that arise due to digitization in society and presents plausible solutions that could be applied to counter these challenges and convert them into opportunities. These solutions may further be improvised and worked out for the software companies from the technological perspective, organizational perspective, and management perspective.