Corrigendum to “Classification of oestrogen receptor selective compounds with benzopyranskeleton using counterpropagation artificial neural networks optimised by genetic algorithms” [Chemom. Intell. Lab. Syst. (2015) 385–395]

Cui, Ying; Shen, Jibin; Li, Yaxiao; Li, Jianyu; Zhang, Ting

doi:10.1016/j.chemolab.2015.07.011

Cited by 4 publications

(5 citation statements)

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“…74,[105][106][107] Moreover, in recent years, more and more deep learning algorithms have been reported not only for image classification but also for multiparameter regression purposes. 108 For more details on recent advances in chemometric calibration methods in modern spectroscopy, the interested reader is referred to the respective review by Wang et al 79…”

Section: Chemometric Analysismentioning

confidence: 99%

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Kalinichev,

Zieger,

Koren

2024

Analyst

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Section: Chemometric Analysismentioning

confidence: 99%

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Kalinichev,

Zieger,

Koren

2024

Analyst

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“…The second neural network is a CNN [19]. This type of model is common in other spectroscopy domains [20][21][22][23][24][25][26][27][28] but not in XRF. Convolution layers incorporate some general domain knowledge about spectral data.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Auto‐encoder neural network incorporating x‐ray fluorescence fundamental parameters with machine learning

Dirks

Poole

2023

X-Ray Spectrometry

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We consider energy‐dispersive x‐ray fluorescence (XRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (like dust). Neural networks do not require instrument and fundamental parameters but training neural networks requires XRF spectra labeled with elemental composition, which is often limited because of its expense. We develop a neural network model that learns from limited labeled data and also benefits from domain knowledge by learning to invert a forward model. The forward model uses transition energies and probabilities of all elements and parameterized distributions to approximate other fundamental and instrument parameters. We evaluate the model and baseline models on a rock dataset from a lithium mineral exploration project. Our model works particularly well for some low‐Z elements (Li, Mg, Al, and K) as well as some high‐Z elements (Sn and Pb) despite these elements being outside the suitable range for common spectrometers to directly measure, likely owing to the ability of neural networks to learn correlations and nonlinear relationships.

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“…19 Thus, the CNN algorithm become acceptable recently. 20,21 The network layer structure involved in the study is shown in Table 1 and Fig. 5.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Age estimation of bloodstains based on convolutional neural network algorithm and hyperspectral imaging technology

Qifu,

Xinyu,

Yueying

et al. 2023

Anal. Methods

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Corrigendum to “Classification of oestrogen receptor selective compounds with benzopyranskeleton using counterpropagation artificial neural networks optimised by genetic algorithms” [Chemom. Intell. Lab. Syst. (2015) 385–395]

Cited by 4 publications

References 0 publications

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Auto‐encoder neural network incorporating x‐ray fluorescence fundamental parameters with machine learning

Age estimation of bloodstains based on convolutional neural network algorithm and hyperspectral imaging technology

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