Artificial intelligence in smart library: Developing a machine learning prediction model
By: Daimari, Dersin, et al
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Material type: 
BookPublisher: IASLIC Bulletin Description: 68(1), Mar, 2023: p.47-54.
In:
IASLIC BulletinSummary: Purpose: Recent developments in artificial intelligence (AI) can transform the working practices of the modern library. However, modern libraries are one of the slowest institutions to adopt AI.This study explores how smart libraries might take advantage of machine learning (ML), a subfield of artificial intelligence (AI), to improve the quality of their services to the users. Methodology: Three cutting-edge machine learning algorithms are presented in this study for the task of identifying and classifying favorite books based on the data of Art Garfunkel's Library. Findings: The findings of this research work are drawn based on experiment results.The experiment results reveal that the proposed method obtained a test accuracy of 86.07% in identifying Favorite books. Originality: This is an empirical research work that looks at AI adoption in libraries using a machine learning model. To the best of our knowledge, this research is the first AI-based application for the modern library to automatically identify and classify Favorite books. – Reproduced
http://www.iaslic1955.org.in/fckeditor/userfiles/file/Abstract,%20%20References%20&%20Author.pdf
| Item type | Current location | Call number | Vol info | Status | Date due | Barcode |
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Articles
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Indian Institute of Public Administration | 68(1), Mar, 2023: p.47-54 | Available | AR130480 |
Purpose: Recent developments in artificial intelligence (AI) can transform the working practices of the modern library. However, modern libraries are one of the slowest institutions to adopt AI.This study explores how smart libraries might take advantage of machine learning (ML), a subfield of artificial intelligence (AI), to improve the quality of their services to the users. Methodology: Three cutting-edge machine learning algorithms are presented in this study for the task of identifying and classifying favorite books based on the data of Art Garfunkel's Library. Findings: The findings of this research work are drawn based on experiment results.The experiment results reveal that the proposed method obtained a test accuracy of 86.07% in identifying Favorite books. Originality: This is an empirical research work that looks at AI adoption in libraries using a machine learning model. To the best of our knowledge, this research is the first AI-based application for the modern library to automatically identify and classify Favorite books. – Reproduced
http://www.iaslic1955.org.in/fckeditor/userfiles/file/Abstract,%20%20References%20&%20Author.pdf
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