Artificial Intelligence and Archive Management on Malaysia's National Archive's Uncaptioned Photos Collection: Accuracy findings comparison based on clustering algorithms

• Published in: ENVIRONMENT-BEHAVIOUR PROCEEDINGS JOURNAL
• Main Authors: Malek, W. A.; Jalil, A.; Rahman, Safawi A.; Kamarudin, Irwan; Abu, Roziya; Ismail, Saidatul Akmar; Mansoor, Mazlifah; Mokhtarudin; Norsuriati; Safuan, N.; Roslan, R. N. Hakim
• Format: Proceedings Paper
• Language: English
• Published: E-IPH LTD UK 2024
• Subjects: Environmental Sciences & Ecology
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001276023400010

In the realm of Artificial Intelligence (AI) and archive management, the central objective revolves around the autonomous extraction of valuable insights, patterns, and actionable information from extensive datasets. The AI technologies play a pivotal role in this context, leveraging advanced algorithms and computational capabilities to efficiently analyze and interpret archived data. The integration of AI within archive management systems enhances the organization, retrieval, and preservation of historical records, while also offering the capability to uncover hidden knowledge and trends. These advancements underscore the vital synergy between AI and archive management, revolutionising how National archive of Malaysia could harness their uncaptioned photos to provide some insight out of the photos for improved decision-making and historical preservation. The findings show that that the accuracy of adopted algorithms K-Means at 83.3%, Mean Shift at 78.0%, and Gaussian Mixture stood at 80.3% accuracy rate respectively.