In the dynamic world of manufacturing, the industry has grappled with ongoing issues such as expensive machine maintenance, operational inefficiencies, and the production of defective products. The need for informed decision-making to maintain quality, meet deadlines, and prevent disruptions is more crucial than ever. Enter Using Real-Time Data and AI for Thrust Manufacturing, a groundbreaking book that addresses these challenges head-on. As Industry 4.0 transforms the manufacturing sector through the integration of the Internet of Things (IoT) and artificial intelligence (AI), this book serves as a beacon for academic scholars and industry professionals alike, offering profound insights into the world of AI-driven industry solutions. The objective of this book is clear—to revolutionize the manufacturing sector by leveraging human expertise and AI-driven data technologies. By delving into the realms of Industry 4.0, IoT, and AI, the book systematically tackles issues such as costly downtime, inefficient processes, and the production of substandard products. With a focus on turning raw data into meaningful insights, the book explores AI applications like machine learning and deep learning, natural language processing, and machine vision. From predictive maintenance to improved demand forecasting, quality assurance, inspection, and warehouse automation, the book positions AI as the linchpin of "Industry 4.0," ensuring not only cost savings but also safety improvements and supply-chain efficiencies.

In manufacturing, entrenched challenges like costly maintenance, operational inefficiencies, and product defects loom large, casting shadows over industry progress. Despite the promise of Industry 4.0 and the proliferation of data-driven technologies, many enterprises need help to effectively harness the transformative power of artificial intelligence (AI). The gap between AI's potential and its practical application persists, hindering manufacturing companies from achieving optimal efficiency, competitiveness, and sustainability. Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI is a groundbreaking book meticulously crafted to address the pressing needs of academic scholars and industry professionals. Offering a nuanced exploration of AI's role in revolutionizing manufacturing, this book serves as a beacon of clarity amidst the complexities of modern industrial landscapes. Whether seeking to optimize operational workflows, mitigate risks, or unlock untapped opportunities, this definitive guide offers invaluable insights and actionable strategies to propel manufacturing enterprises into a future of innovation, efficiency, and sustainable growth.

In the dynamic world of manufacturing, the industry has grappled with ongoing issues such as expensive machine maintenance, operational inefficiencies, and the production of defective products. The need for informed decision-making to maintain quality, meet deadlines, and prevent disruptions is more crucial than ever. Enter Using Real-Time Data and AI for Thrust Manufacturing, a groundbreaking book that addresses these challenges head-on. As Industry 4.0 transforms the manufacturing sector through the integration of the Internet of Things (IoT) and artificial intelligence (AI), this book serves as a beacon for academic scholars and industry professionals alike, offering profound insights into the world of AI-driven industry solutions. The objective of this book is clear-to revolutionize the manufacturing sector by leveraging human expertise and AI-driven data technologies. By delving into the realms of Industry 4.0, IoT, and AI, the book systematically tackles issues such as costly downtime, inefficient processes, and the production of substandard products. With a focus on turning raw data into meaningful insights, the book explores AI applications like machine learning and deep learning, natural language processing, and machine vision. From predictive maintenance to improved demand forecasting, quality assurance, inspection, and warehouse automation, the book positions AI as the linchpin of "Industry 4.0," ensuring not only cost savings but also safety improvements and supply-chain efficiencies.

Industries face the challenge of incorporating technological innovations while ensuring sustainable development in a constantly changing landscape. This struggle is complicated by the need to address societal and environmental concerns, and thus, there is a critical need for guidance and expertise to navigate these difficulties. Powering Industry 5.0 and Sustainable Development Through Innovation unveils a roadmap for industry stakeholders, offering insights into the role of technological innovations in driving Industry 5.0 while advancing sustainable development goals. By dissecting key elements such as artificial intelligence, big data analytics, and blockchain technology, readers understand how these tools can be harnessed to foster innovation and inclusivity. Moreover, the book explores emerging trends and challenges, equipping readers with the foresight needed to navigate the dynamic landscape of Industry 5.0.

In an era where data security and transparency are paramount, blockchain technology offers a groundbreaking solution for libraries. By enhancing security protocols, ensuring data integrity, and enabling transparent information sharing, blockchain has the potential to revolutionize library systems. Integrating blockchain into library operations is an essential step forward in strengthening cataloging processes, securing digital assets, and fostering a more efficient and trusted information management system. Enhancing Security and Regulations in Libraries With Blockchain Technology provides a comprehensive analysis of blockchain’s role in reshaping library operations. Through detailed case studies, readers will gain insight into the practical applications, benefits, and challenges of implementing blockchain in the library sector. This essential resource is aimed at librarians, IT professionals, library science students, and academics looking to bolster the future of information management with enhanced security and regulatory compliance.

As libraries transition into the digital age, they encounter a pressing challenge: outdated information systems hinder their ability to meet the diverse needs of patrons. Traditional library management systems struggle to cope with the demands of modern users, resulting in inefficient resource allocation, limited accessibility, and disjointed user experiences. This disconnect between antiquated systems and evolving user expectations poses a significant barrier to libraries striving to remain relevant in an increasingly digital world. Improving Library Systems with AI: Applications, Approaches, and Bibliometric Insights presents a comprehensive solution to this pressing problem. By integrating modern digital tools and technologies, libraries can revolutionize their information systems, enhancing accessibility, efficiency, and user satisfaction. This book offers practical insights and strategies for modernizing library services and operations, from digitizing physical resources to implementing advanced search algorithms and data analytics. Librarians, administrators, and technology providers will find invaluable guidance on navigating the complexities of digital transformation and maximizing the impact of their efforts.

Academic scholars, entrenched in the complexities of various domains, face the daunting task of navigating intricate decision-making scenarios. The prevailing need for efficient and effective decision-making tools becomes increasingly apparent as traditional methodologies struggle to keep pace with the demands of modern research and industry. This pivotal issue necessitates a shift, urging scholars to explore unconventional approaches that can transcend disciplinary boundaries and unlock new dimensions of problem-solving. In response to these pressing challenges, Intelligent Decision Making Through Bio-Inspired Optimization emerges as a beacon of ingenuity. This groundbreaking book transcends usual disciplinary boundaries, seamlessly integrating computer science, artificial intelligence, optimization, and decision science. Its multidisciplinary approach addresses the inherent complexities faced by scholars, offering a comprehensive exploration of nature-inspired algorithms such as genetic algorithms, swarm intelligence, and evolutionary strategies. The book's core mission is to empower academic scholars with the tools to overcome contemporary decision-making hurdles, providing a holistic understanding of these bio-inspired approaches and their potential to revolutionize the scholarly landscape.

Generative artificial intelligence (GAI) and large language models (LLM) are machine learning algorithms that operate in an unsupervised or semi-supervised manner. These algorithms leverage pre-existing content, such as text, photos, audio, video, and code, to generate novel content. The primary objective is to produce authentic and novel material. In addition, there exists an absence of constraints on the quantity of novel material that they are capable of generating. New material can be generated through the utilization of Application Programming Interfaces (APIs) or natural language interfaces, such as the ChatGPT developed by Open AI and Bard developed by Google. The field of generative artificial intelligence (AI) stands out due to its unique characteristic of undergoing development and maturation in a highly transparent manner, with its progress being observed by the public at large. The current era of artificial intelligence is being influenced by the imperative to effectively utilise its capabilities in order to enhance corporate operations. Specifically, the use of large language model (LLM) capabilities, which fall under the category of Generative AI, holds the potential to redefine the limits of innovation and productivity. However, as firms strive to include new technologies, there is a potential for compromising data privacy, long-term competitiveness, and environmental sustainability. This book delves into the exploration of generative artificial intelligence (GAI) and LLM. It examines the historical and evolutionary development of generative AI models, as well as the challenges and issues that have emerged from these models and LLM. This book also discusses the necessity of generative AI-based systems and explores the various training methods that have been developed for generative AI models, including LLM pretraining, LLM fine-tuning, and reinforcement learning from human feedback. Additionally, it explores the potential use cases, applications, and ethical considerations associated with these models. This book concludes by discussing future directions in generative AI and presenting various case studies that highlight the applications of generative AI and LLM.

The business world today is changing at a breakneck pace. Traditional management practices need help keeping up with the uncertainties and complexities of the digital age. Leaders face a lot of pressure to innovate, adapt, and drive transformative change within their organizations. However, they need more than just conventional wisdom to navigate this terrain. A deep understanding of emerging technologies like artificial intelligence (AI) and their practical applications in management is essential. Generative AI for Transformational Management offers a compelling solution to these challenges. This book provides a roadmap for leveraging AI to drive organizational transformation by exploring the intersection of generative AI and visionary leadership. By examining real-world case studies and practical applications, readers can learn how AI can be integrated into leadership practices to promote innovation and proactive decision-making and effectively navigate the complexities of the digital age.

In the age of artificial intelligence (AI), hotel and travel management are undergoing transformations to revolutionize guest experiences, make operations efficient, and improve industry standards. AI technologies redefine how hotels and travel companies personalize customer interactions, streamline operations, and optimize revenue management. From tools like chatbots and virtual assistants to predictive analytics, AI enables increased efficiency and customization. As AI continues to evolve, questions must be raised about data privacy, ethical use or algorithms, and the roles of hospitality workers as technology becomes pivotal. Hotel and Travel Management in the AI Era explores the intersection of AI and hotel and travel management, showcasing its potential for innovation and the challenges it presents for workers in the hospitality industry. It posits effective solutions for managing technology integration in an industry where the human aspect of management is pivotal. This book covers topics such as virtual and augmented reality, smart technology, and risk management, and is a useful resource for hospitality and tourism professionals, security workers, computer engineers, business owners, sociologists, researchers, and academicians.