Artificial Intelligence and Machine Learning Approaches for Understanding Food and Nutrients-Reference-Cited by-同舟云学术

Artificial Intelligence and Machine Learning Approaches for Understanding Food and Nutrients

Published:2023 Issue: Volume: Page:59-66
ISSN:
Container-title:Artificial Intelligence to Analyze Psychophysical and Human Lifestyle
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Author:

Chhabra Gunjan,Kumar Sunil,Gupta Sunil,Nagpal Pooja

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-3039-5_5

Reference20 articles.

1. Jia, W., Zhao, R., Yao, N., Fernstrom, J. D., Fernstrom, M. H., Sclabassi, R. J., & Sun, M. (2009). A food portion size measurement system for image-based dietary assessment. In IEEE 35th Annual Northeast conference on Bioengineering.

2. Pouladzadeh, P., Kuhad, P., Peddi, S. V. B., Yassine, A., & Shirmohammadi, S. (2016). Food calorie measurement using deep learning neural network. In Conference Record - IEEE Instrumentation and Measurement Technology Conference.

3. Spector, R. (2009). Science and pseudoscience in adult nutrition research and practice. In: Skeptical Inquirer.

4. Yang, S., Chen, M., Pomerleau, D., & Sukthankar, R. (2010). Food recognition using statistics of pairwise local features. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 2249–2256).

5. Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009). Pfifid: Pittsburgh fast-food image dataset. In IEEE International Conference on Image Processing (pp. 289–292).

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