An assessment of AI in detecting duplicate books in Delta State Polytechnic Library, Ogwashi-Uku, Delta State
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- NGN 5000
Background of the study
Duplicate books in library collections can lead to inefficiencies in resource management and hinder optimal space utilization. AI is increasingly being employed to detect and eliminate duplicate entries in library catalogs by comparing bibliographic data and metadata. At Delta State Polytechnic Library, Ogwashi-Uku, AI-driven systems utilize advanced pattern recognition and machine learning techniques to identify duplicate records, streamline catalog management, and improve resource allocation (Okeke, 2023). This automation reduces manual sorting errors and ensures that the library’s collection remains accurate and current. Furthermore, the continuous learning capability of AI allows the system to adapt to evolving classification standards and new book acquisitions. However, challenges such as data inconsistencies, integration with legacy catalog systems, and initial system calibration issues may affect the accuracy of duplicate detection. This study assesses the effectiveness of AI in detecting duplicate books, examining its impact on catalog integrity and overall library management efficiency (Ibrahim, 2024).
Statement of the problem
Although AI shows promise in automating the detection of duplicate books, Delta State Polytechnic Library faces challenges such as data inconsistencies and integration issues with existing catalog systems. These problems can lead to errors in duplicate identification, resulting in inefficient use of library space and resources. This study aims to identify the specific challenges affecting the performance of AI-based duplicate detection and propose strategies to enhance system accuracy and operational efficiency (Adeniran, 2024).
Objectives of the study
To evaluate the effectiveness of AI in detecting duplicate books.
To identify challenges related to data integration and system accuracy.
To propose strategies for improving duplicate detection processes.
Research questions
How effective is AI in identifying duplicate books?
What challenges affect the accuracy of AI-based duplicate detection?
What measures can improve the performance of these systems?
Significance of the study
This study is significant as it provides insights into how AI can improve catalog management by eliminating duplicates, thereby enhancing resource allocation and operational efficiency. The findings will guide Delta State Polytechnic Library in optimizing AI tools to maintain an accurate and efficient collection (Chinwe, 2024).
Scope and limitations of the study
Limited to the topic only.
Definitions of terms
Duplicate Books: Multiple copies or redundant entries of the same book in a library catalog.
Catalog Integrity: The accuracy and consistency of library catalog records.
Pattern Recognition: The process by which AI identifies regularities in data.
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