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

Ke Xu et al.

International Journal of Information Technology and Web Engineering2025https://doi.org/10.4018/ijitwe.387650article
ABDC C
Weight
0.50

What the paper says

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.

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https://doi.org/https://doi.org/10.4018/ijitwe.387650

Or copy a formatted citation

@article{ke2025,
  title        = {{Model-Driven Integration of Deep Learning for Artifact Classification in Museum Information Systems}},
  author       = {Ke Xu et al.},
  journal      = {International Journal of Information Technology and Web Engineering},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/ijitwe.387650},
}

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

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.