Paper Title
ENHANCING INFORMATION RETRIVAL IN ACADEMIC LIBRARIES WITH ARTIFICIAL INTELLIGENCE: A COMPARATIVES STUDY OF NEURAL NETWORKS, GENETIC ALGORITHMS, AND LATENT SEMANTIC ANALYSIS

Abstract
This paper aims to ascertain AI application in improving information retrieval systems in academic libraries particularly Neural Networks, Genetic Algorithms and Latent Semantic Analysis. Consequently, the aim of the study is to look at how these algorithms enhance retrieval accuracy, relevance and response time in library information systems and overall user experience in academic research facilities. We used a collection of documents containing materials from academic texts, queries, and metadata from the different fields. All the algorithms were run separately allowing each to work on the result lists and rank them according to relevance. Neural Networks was applied to identify semantic features of the queries and documents, Genetic Algorithms were used to tune search ranking parameters, Latent Semantic Analysis was applied to determine the main terms and conceptions for improved relevance scores. Such quantitative measures as precision, recall and time taken for retrieval were employed. Based on the results, the greatest accuracy or using neural Networks to analyse complex semantically oriented queries but extensive computational power is needed. What was illustrated in this research was superiority of Genetic Algorithms in parameter optimization, that leads to faster search time but slightly less specific to the query. Given in table 1, Latent Semantic Analysis done a fairly well job in terms of assessing thematic relevance particularly where the search string was fairly limited and simple and this system also boasted an incredibly high speed of processing times while not being as flexible in terms of adapts to change in the way a query is entered. Three methods were identified to be very precise for complex queries with Neural Networks giving the most optimal results, followed closely by Genetic Algorithms and Latent Semantic Analysis which were equally faster but not as versatile. Applying the proposed algorithms may result in approaching a balanced solution for an academic library for which speed and relevance are crucial factors in information retrieval. This paper presents a modified design of Area-Efficient Low power Carry Select Adder (CSLA) Circuit. In digital adders, the speed of addition is limited by the time required to propagate a carry through the adder. The sum for each bit position in an elementary adder is generated sequentially only after the previous bit position, the speed of addition is limited by the time required to transmit a carry through the adder. Carry select adder processors and systems. Has been summed and a carry propagated into the next position. The major speed limitation in any adder is in the production of carries. Keywords - Artificial Intelligence, Information Retrieval, Neural Networks, Genetic Algorithms, Latent Semantic Analysis, Academic Libraries, Information Systems, Data Retrieval Optimization.