A Review of Data Science Applications in the Camera Industry

Xien Yu

doi:10.54254/2755-2721/2025.LD29185

Applied and Computational EngineeringOpen access

A Review of Data Science Applications in the Camera Industry

Research Article

Open Access

A Review of Data Science Applications in the Camera Industry

Xien Yu ^1*

¹ Newton’s Grove School

^*Corresponding author: Johannayu0331@gmail.com

Published on 5 November 2025

ACE Vol.204

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-517-2

ISBN (Online): 978-1-80590-518-9

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Abstract

As the information society enters the era of big data, data has become a crucial production factor driving industrial innovation and upgrading. As a key component of traditional manufacturing and consumer electronics, the camera industry has faced multiple challenges in recent years, including the widespread adoption of smartphones, diversified consumer demand, and global supply chain fluctuations. Against this backdrop, leveraging data science to realize accurate user insights, efficient product iteration, and flexible operational management has become a core concern for both academia and industry. Research indicates that the application of data science in the imaging and camera fields encompasses multiple areas, including analyzing user profiles, upgrading products, and improving resource allocation efficiency in the face of fierce competition. This paper systematically reviews existing research in the camera field using case studies and literature reviews, exploring the value realization mechanisms of data science in the camera industry and providing insights for the industry's intelligent transformation.

Keywords:

Camera Industry, Data Science, Consumer Behavior

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References

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[5]. Zhang, Y. (2024). A study on marketing strategies of Canon digital cameras in Southwest China (Master's thesis). University of Electronic Science and Technology of China.

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References

[3]. Wang, J. (2022). Data-driven Supply Chain Monitoring and Optimization (Doctoral dissertation).

[4]. Choi, Y., & Lee, C. (2024). Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. PloS one, 19(12), e0315540.

[5]. Zhang, Y. (2024). A study on marketing strategies of Canon digital cameras in Southwest China (Master's thesis). University of Electronic Science and Technology of China.

[6]. Joshi, R., Gupte, R., & Saravanan, P. (2018). A random forest approach for predicting online buying behavior of Indian customers. Theoretical Economics Letters, 8(03), 448.

[7]. Ilesanmi, A. E., & Ilesanmi, T. O. (2021b). Methods for image denoising using convolutional neural network: A review. Complex & Intelligent Systems, 7(5), 2179–2198.

Cite this article

Yu,X. (2025). A Review of Data Science Applications in the Camera Industry. Applied and Computational Engineering,204,8-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-517-2(Print) / 978-1-80590-518-9(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.204

ISSN: 2755-2721(Print) / 2755-273X(Online)

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