Thesis Abstract

Abstract
The integration of Artificial Intelligence (AI) in library cataloging and classification has the potential to revolutionize information organization and retrieval processes. This thesis explores the implementation of AI technologies in improving the efficiency and effectiveness of library cataloging and classification systems. The research investigates how AI algorithms can enhance the accuracy and speed of metadata creation, classification, and indexing of library resources. The study begins with an introduction to the significance of AI in the field of Library and Information Science, highlighting the growing importance of technology in managing and accessing vast amounts of information. The background of the study provides an overview of traditional library cataloging methods and the challenges associated with manual classification processes. The problem statement identifies the limitations of current cataloging systems in meeting the evolving information needs of users and highlights the potential benefits of incorporating AI technologies. The objectives of the study focus on evaluating the impact of AI on library cataloging efficiency, accuracy, and user satisfaction. The research methodology chapter outlines the approach taken to investigate the implementation of AI in library cataloging and classification. It includes details on data collection methods, AI algorithm selection, and evaluation criteria for measuring the performance of AI-enhanced cataloging systems. The literature review section explores existing studies and projects that have implemented AI in library settings. It discusses the various AI techniques used for automated metadata creation, subject classification, and indexing. The chapter also examines the challenges and opportunities associated with AI adoption in library cataloging processes. The findings chapter presents a detailed analysis of the results obtained from implementing AI in library cataloging and classification. It evaluates the impact of AI on metadata accuracy, classification consistency, and user satisfaction. The discussion delves into the implications of these findings for library professionals, users, and information retrieval practices. The conclusion and summary chapter consolidate the key findings of the study and provide recommendations for future research and implementation of AI in library cataloging and classification. It emphasizes the potential of AI technologies to enhance information organization and access in libraries, paving the way for more efficient and user-centric services. In conclusion, this thesis contributes to the growing body of research on the application of AI in Library and Information Science. By exploring the implementation of AI in library cataloging and classification, this study offers insights into the benefits and challenges of integrating AI technologies into traditional library practices.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Library Cataloging and Classification" aims to explore the integration of artificial intelligence (AI) technologies within library cataloging and classification processes. In recent years, AI has gained significant traction across various industries, offering advanced capabilities in data processing, analysis, and decision-making. This research seeks to investigate how AI can be effectively utilized to enhance the efficiency and accuracy of library cataloging and classification practices. The proliferation of digital resources and the increasing volume of information available pose significant challenges for librarians in organizing and categorizing materials. Traditional cataloging methods often require manual input and extensive human intervention, leading to potential errors and inconsistencies. By leveraging AI technologies such as machine learning and natural language processing, libraries can streamline cataloging processes, automate repetitive tasks, and improve the overall quality of metadata management. The research overview will delve into the current landscape of library cataloging and classification, highlighting the limitations and challenges faced by librarians in the digital age. It will provide a comprehensive review of existing literature on AI applications in library science, showcasing successful case studies and best practices from the field. The overview will also address the potential benefits and implications of implementing AI in library settings, including improved search capabilities, enhanced user experience, and resource optimization. Furthermore, the research will outline the methodology employed to investigate the feasibility and effectiveness of AI integration in library cataloging and classification. This will involve data collection, analysis, and experimentation to evaluate the impact of AI algorithms on cataloging accuracy, efficiency, and scalability. The research overview will also discuss the ethical considerations and implications of AI adoption in library settings, emphasizing the importance of transparency, accountability, and privacy protection. Overall, the project aims to contribute to the growing body of knowledge on AI applications in library science and provide practical insights for librarians and information professionals seeking to enhance their cataloging and classification processes. By embracing AI technologies, libraries can adapt to the evolving information landscape, improve access to resources, and deliver more personalized and efficient services to patrons.