Application of Artificial Intelligence-based Technology in College Archives Management

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Jianping Hu, Yongkang Yan, Zhengguang Xie


This study looks into the use of artificial intelligence (AI)-based technologies, notably Convolutional Neural Network (CNN) models and data augmentation approaches, in college archives management. With the rapid expansion of digital information, archive organizations have enormous issues in maintaining, organizing, and giving access to their vast collections of historical documents and scholarly resources. AI solutions present possible solutions to these difficulties by automating labor-intensive operations and improving the efficiency and accuracy of preservation workflows. In this study, they investigate the transformational potential of CNN models in digitization activities, utilizing their visual data processing skills to automate and enhance the digitization of visual resources within college archives. In addition, they look into how data augmentation techniques might improve the resilience and generalization capabilities of AI models, reducing biases and improving performance across a variety of archive collections. Using empirical research, theoretical analysis, and case studies, they present insights into the opportunities and obstacles of implementing AI-based technology in college archive management. Furthermore, they discuss ethical implications for the deployment of AI solutions, emphasizing the significance of transparency, accountability, and stakeholder participation. This study aims to inform archival practitioners, technologists, and stakeholders about the effective and responsible integration of AI technologies in cultural heritage preservation and promotion within academic institutions by elucidating the practical applications and implications of CNN models and data augmentation techniques in archival contexts.

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