The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.

The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. It equips readers with the knowledge and practical insights needed to harness these technologies effectively, enhancing software development, testing, maintenance, and deployment processes. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics. Moreover, the forward-looking section discussing future trends and research directions will inspire readers to explore new avenues in software engineering. This book serves as a valuable resource for those seeking to leverage advanced technologies to improve software engineering processes, making it an essential addition to the library of anyone involved in software development.

In the rapidly evolving digital landscape, the importance of cybersecurity has never been more critical. With the increasing sophistication of cyber threats, traditional security measures often fall short in providing adequate protection. Cyber artificial intelligence (AI) offers advanced capabilities to detect, prevent, and respond to attacks in real time. As cyber threats continue to grow in complexity and frequency, the integration of AI into cybersecurity frameworks is not just advantageous but essential for maintaining robust and resilient defenses. Redefining Security With Cyber AI delves into the profound transformation of security paradigms brought about by the advent of AI. This book explores the intricate dance between the ever-expanding frontiers of digital technology and the AI-driven mechanisms that aim to safeguard them. Covering topics such as artificial neural networks, intrusion detection, and large language models, this book is an excellent resource for cybersecurity professionals, AI and machine learning researchers, IT executives and managers, policy makers and regulators, postgraduate students and educators, academicians, and more.

In the modern business landscape, the intersection of technology and operations management is driving efficiency and innovation. As organizations continue to rely on advanced technologies, such as artificial intelligence, data analytics, and automation, they are transforming their operational strategies to enhance productivity, streamline processes, and deliver valuable products. Aligning technological advancements with operational goals allows companies to achieve a competitive edge, improve customer satisfaction, and unlock new growth opportunities. Businesses must continue to explore this convergence to adapt their operations successfully and invest in necessary skills to connect technology with business processes. Convergence of Technology and Operations Management in Modern Businesses explores the intersection of technology and operations management in the modern business environment. It covers technological advancements for revolutionized operations and supply chain management for increased efficiency and competitiveness. This book covers topics such as smart banking, blockchain, and human capital, and is a useful resource for financial professionals, bankers, business owners, data scientists, computer engineers, academicians, scientists, and researchers.

Companies constantly strive to adopt the latest technological advancements in order to stay ahead in today's interconnected world. However, many organizations need guidance to fully leverage the potential of artificial intelligence (AI) and digital transformation. Without this direction, their ability to drive growth and efficiency is thwarted. Creating AI Synergy Through Business Technology Transformation offers a comprehensive guide to leveraging AI and digital transformation for strategic advantage. By combining insightful research, practical case studies, and innovative strategies, this book provides a roadmap for organizations to maximize the benefits of AI across their operations. From optimizing decision-making processes to enhancing customer experiences, the book demonstrates how AI can revolutionize business practices and drive sustainable growth.

Software engineering is a basic concept in the digital age. The seamless operation of a website is integral to the functioning of businesses, education, government services, and personal communications. As a foundation of our online interactions, a website must be meticulously crafted to provide an outstanding user experience supported by an innovative user interface. It is essential to explore core services required to host, manage, and access a secure modern website. Design and Implementation of Software Engineering for Modern Web Applications serves as a comprehensive guide to understanding the technologies and methodologies essential for designing, developing, and maintaining modern, secure websites. From domain structures and domain name systems to web protocols, database servers, and web browsers are introduced to the network concepts critical to server technologies. Covering topics such as requirements engineering, web applications, and website management, this book is an essential resource for postgraduate students, educators, web developers, researchers, academicians, and more.

In an increasingly data-centric world, scholars and practitioners grapple with the complexities of harnessing data analytics effectively across various industries. The challenge lies in navigating the rapid evolution of methodologies, identifying emerging trends, and understanding the nuanced applications of data analytics in real-world scenarios. This gap between theory and practice inhibits academic progress. It hampers industry innovation, leaving stakeholders needing help to leverage data to its full potential. Recent Trends and Future Direction for Data Analytics presents a compelling solution. By delving into real-world case studies spanning supply chain management, marketing, healthcare, and finance, this book bridges the gap between theory and practice, offering invaluable insights into the practical applications of data analytics. A systematic exploration of fundamental concepts, advanced techniques, and specialized topics equips scholars, researchers, and industry professionals with the knowledge and tools needed to navigate the complexities of data analytics with confidence.

In today's rapidly evolving educational landscape, traditional teaching methods often fail to equip students with the skills necessary for success in the 21st century. The siloed approach to education, where subjects are taught in isolation, must reflect the interconnected nature of modern challenges. This disconnect between traditional educational models and the needs of the future workforce is a serious concern among educators. They face the challenge of preparing students for professions that still need to be created using tools and technologies that are still emerging. Revolutionizing Curricula Through Computational Thinking, Logic, and Problem Solving offers a transformative solution to this challenge. By advocating for computational thinking as a fundamental skill set applicable across all academic disciplines, the book provides educators with the tools to bridge this gap. It introduces computational thinking not just as a technical skill but as a way of problem-solving and logical reasoning that enhances critical thinking across subjects. Through practical lesson plans, case studies, and strategies, educators can seamlessly integrate computational thinking into their classrooms, preparing students for the complexities of the modern world.

In contemporary engineering domains, optimization and decision-making issues are crucial. Given the vast amounts of available data, processing times and memory usage can be substantial. Developing and implementing novel heuristic algorithms is time-consuming, yet even minor improvements in solutions can significantly reduce computational costs. In such scenarios, the creation of heuristics and metaheuristic algorithms has proven advantageous. The convergence of machine learning and metaheuristic algorithms offers a promising approach to address these challenges. Metaheuristic and Machine Learning Optimization Strategies for Complex Systems covers all areas of comprehensive information about hyper-heuristic models, hybrid meta-heuristic models, nature-inspired computing models, and meta-heuristic models. The key contribution of this book is the construction of a hyper-heuristic approach for any general problem domain from a meta-heuristic algorithm. Covering topics such as cloud computing, internet of things, and performance evaluation, this book is an essential resource for researchers, postgraduate students, educators, data scientists, machine learning engineers, software developers and engineers, policy makers, and more.

In today's increasingly digital world, the interaction between humans and computers has become a fundamental aspect of our daily lives, affecting how we learn, work, and conduct business. However, as technology continues to evolve, the Human-Computer Interaction (HCI) field faces new challenges and opportunities. One pressing issue is the need to understand the social implications of HCI in education, management, and economics. This includes how HCI impacts teaching methodologies, business practices, and economic models and how it can be leveraged to enhance productivity, innovation, and entrepreneurship. Social Reflections of Human-Computer Interaction in Education, Management, and Economics addresses these challenges by comprehensively exploring HCI and its societal implications. This book is essential for scholars, practitioners, and policymakers looking to understand the complex interplay between humans and computers in today's digital age. It offers practical insights and theoretical frameworks that can inform the design of more effective educational programs, management practices, and economic policies.