Thesis Abstract

Abstract
This thesis explores the utilization of artificial intelligence (AI) for enhancing information retrieval in digital libraries. In an era where vast amounts of digital information are generated and stored, efficient retrieval mechanisms are essential to ensure users can access relevant resources effectively. Traditional search algorithms often fall short in providing accurate and personalized results due to the complexities of modern information systems. AI technologies offer promising solutions to address these challenges by enabling intelligent search capabilities that adapt to user preferences and context. The study begins with an examination of the background of digital libraries and the evolution of information retrieval systems. It identifies the limitations of existing approaches and highlights the need for more sophisticated methods to enhance user experience and satisfaction. The research problem centers on the inefficiencies and inaccuracies in current information retrieval systems, which hinder users from accessing the most relevant resources efficiently. The primary objective of this thesis is to investigate how AI techniques, such as machine learning, natural language processing, and recommendation systems, can be leveraged to improve information retrieval in digital libraries. By developing intelligent algorithms that can understand user queries, analyze content, and deliver personalized results, the aim is to enhance the overall search experience and increase the relevance of retrieved information. The scope of the study encompasses the design and implementation of AI-based information retrieval models within a digital library context. Real-world datasets and user interactions will be used to evaluate the effectiveness of the proposed solutions in comparison to traditional search methods. The significance of this research lies in its potential to revolutionize how users interact with digital libraries, leading to more accurate, efficient, and personalized information retrieval experiences. The methodology chapter outlines the research design, data collection methods, and evaluation metrics employed to assess the performance of the AI-based information retrieval models. Various experiments will be conducted to measure the accuracy, speed, and user satisfaction levels of the proposed systems. The findings chapter presents a detailed analysis of the results obtained from the experiments, highlighting the strengths and limitations of the AI models in comparison to traditional search algorithms. In conclusion, this thesis demonstrates the potential of artificial intelligence to significantly enhance information retrieval in digital libraries. By harnessing the power of AI technologies, users can access relevant resources more efficiently, leading to improved overall satisfaction and engagement. The study contributes valuable insights to the field of library and information science, paving the way for future research and development in intelligent information retrieval systems.

Thesis Overview