Model-Driven Integration of Deep Learning for Artifact Classification in Museum Information Systems

Ke Xu, Qiong Wu and Yujiao Hou
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Ke Xu: Hebei Minzu Normal University, China
Qiong Wu: Chifeng University, China
Yujiao Hou: Chifeng University, China

International Journal of Information Technology and Web Engineering (IJITWE), 2025, vol. 20, issue 1, 1-24

Abstract: Museum Information Systems (MIS) often rely on manual classification and keyword search, limiting accuracy and scalability. Deep learning offers a solution, but effective integration requires alignment with curatorial workflows. This study proposes a model-driven framework for integrating Convolutional Neural Networks (CNNs) into MIS to enhance artifact classification and retrieval. A prototype was built using ReactJS, Django, and TensorFlow, and it was trained on a curated subset of The Met's Open Access Images. The system employs a Hybrid-E Loss for improved classification accuracy. The model achieved 94.3% classification accuracy and real-time retrieval latency below 100 ms, with throughput exceeding 14 queries per second. The framework successfully bridges AI performance with curatorial logic, demonstrating a scalable and interpretable solution for digital heritage systems.

Date: 2025
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