Editorial.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3391
- By:
- Publication type:
- Article
Works matching IS 08869383 AND DT 2022 AND VI 36 AND IP 2
Results: 7
1
2
Combining computer vision and deep learning to classify varieties of Prunus dulcis for the nursery plant industry.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3388
- By:
- Publication type:
- Article
3
On the possible benefits of deep learning for spectral preprocessing.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3374
- By:
- Publication type:
- Article
4
Issue Information.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3352
- Publication type:
- Article
5
Predicting pectin performance strength using near‐infrared spectroscopic data: A comparative evaluation of 1‐D convolutional neural network, partial least squares, and ridge regression modeling.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3348
- By:
- Publication type:
- Article
6
Predicting molecular activity on nuclear receptors by multitask neural networks.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3325
- By:
- Publication type:
- Article
7
Varietal quality control in the nursery plant industry using computer vision and deep learning techniques.
- Published in:
- Journal of Chemometrics, 2022, v. 36, n. 2, p. 1, doi. 10.1002/cem.3320
- By:
- Publication type:
- Article