Exploration of Deep Learning Evaluation from the Perspective of Multimodal Data Analysis

Haonan Cui

doi:10.23977/jaip.2024.070110

Exploration of Deep Learning Evaluation from the Perspective of Multimodal Data Analysis

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DOI: 10.23977/jaip.2024.070110 | Downloads: 32 | Views: 460

Author(s)

Haonan Cui ¹

Affiliation(s)

¹ Department of Information and Control Engineering, Shenyang Institute of Science and Technology, Shenyang, Liaoning, 110000, China

Corresponding Author

Haonan Cui

ABSTRACT

Deep learning evaluation is a new direction formed by intersection in multiple fields. By constructing a deep learning database and constructing a deep learning evaluation model, it plays a role in optimizing educational evaluation. Based on the current situation, the main purpose of deep learning evaluation is to follow educational laws, optimize educational reality, and promote educational development. Consequently, various aspects such as data collection automation, deepening education, and enhancing decision-making intelligence should be integrated into deep learning evaluation. Therefore, this article mainly explores the deep learning evaluation under the multi-modal data analysis perspective for reference.

KEYWORDS

Multi-modal Data; Analysis; Deep Learning Evaluation

CITE THIS PAPER

Haonan Cui, Exploration of Deep Learning Evaluation from the Perspective of Multimodal Data Analysis. Journal of Artificial Intelligence Practice (2024) Vol. 7: 58-63. DOI: http://dx.doi.org/10.23977/jaip.2024.070110.

REFERENCES

[1] Zhang Kang. Research on feature fusion of multimodal data based on deep learning [D]. Qilu University of technology, 2021.
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[3] Tong Wei. Research and application of large scale examination quality assessment method based on deep learning [D]. Anhui: University of science and technology of China, 2021.
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[5] Zhang Tingting, Zhang Xihai, Wang Dongchen. Design of seismic comprehensive data analysis system based on heterogeneous data fusion [J]. Electronic design engineering, 2022, 30 (17): 132-136.

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Exploration of Deep Learning Evaluation from the Perspective of Multimodal Data Analysis

Author(s)

Affiliation(s)

Corresponding Author

ABSTRACT

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CITE THIS PAPER

REFERENCES

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