Application of stacking ensemble learning model in quantitative analysis of biomaterial activity

Cao, Hao; Gu, Youlin; Fang, Jiajie; Hu, Yihua; Ding, Wanying; He, Haihao; Chen, Guolong

doi:10.1016/j.microc.2022.108075

Cited by 16 publications

(10 citation statements)

References 33 publications

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“…This method is of great significance for improving the IR extinction performance of biomaterials and distinguishing the activities of biomaterials. Cao Hao et al [8] proposed a stacked ensemble learning model for accurately detect the activity ratio of the biomaterials, and overcame the problems of time-consuming and low accuracy of traditional methods in the detection of biomaterial activity.…”

Section: The Extinction Characteristics Of Monomer Particlesmentioning

confidence: 99%

“…The relationships between actual extinction ability of biological extinction materials and bioactivity, aggregation state, mass concentration and particle number concentration in the atmosphere were extensively discussed. The combination of modern methods such as spectral analysis and artificial intelligence has further improved the efficiency of research on biological extinction materials [8] .…”

Section: Introductionmentioning

confidence: 99%

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Research progress of biological extinction materials

Chen¹,

Gu²,

Hu³

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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Section: The Extinction Characteristics Of Monomer Particlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research progress of biological extinction materials

Chen¹,

Gu²,

Hu³

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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“…3000-2800 cm -1 may be the spectral band corresponding to C-H of aliphatic or cyclic groups, and 2921 cm -1 contains aliphatic methylene bands, which are asymmetric stretches of CH2 groups (Ribeiro et al, 2001;Rosa et al, 2015;Sisouane et al, 2017). The chemical composition corresponding to the characteristic peak near 3276 cm -1 is the hydroxyl group and the N-H structure in the amide, and the absorption peak is related to the N-H and O-H stretching (Cao et al, 2022;Hell et al, 2016).…”

Section: Chemical Composition Analysis Based On Ft-mir Spectral Imagesmentioning

confidence: 99%

FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

Liu

et al. 2023

Preprint

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Wild Gastrodia elata resources are in short supply and the market is dominated by cultivated Gastrodia elata resources and the medicinal value of both is very different. It is significant to find a highly accurate and stable technique to identify wild and cultivated Gastrodia elata. It could prevent market fraud and protect the rights of consumers. In this context, this study is the first to combine three-dimensional correlated spectral (3DCOS) images with deep learning to identify wild and cultivated Gastrodia elata. Also, partial least squares discrimination analysis (PLS-DA) and support vector machine (SVM) models are compared with this model. The PLS-DA and SVM models are built based on Fourier transform mid-infrared (FT-MIR) spectral data after nine different preprocessing. The PLS-DA model with second-order derivatives (2D) gives the best results when comparing the effects of the models with different preprocessing. the SVM model with parameters c, g in a reasonable range also gives satisfactory model results. The advantage of the deep learning model over them is that no processing of the original spectral data is required. With only 46 iterations, the accuracy of the model is stable at 100% for the training set, test set and external validation set. The excellent performance of the model allows it to be used as a technical reference to solve studies on the qualitative aspects of Gastrodia elata.

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“…The Stacking ensemble learning framework can fuse several different types of algorithms to make the prediction results more accurate than the performance of a single algorithm [5][6] . As shown in Figure 1, the model framework is designed with a two-layer structure, and the first layer consists of one or more base learners; since the more learners are fused, the more complex the model is and the longer the training time is, only two primary learners are used in this paper; the second layer uses a simple algorithm as a meta-learner, which not only strengthens the learning effect but also does not cause the prediction model to be too redundant and complex and prevents the overfitting phenomenon.…”

Section: Stacking Ensemble Learning Modelmentioning

confidence: 99%

Multi-feature short-term load forecasting based on stacking ensemble learning

He,

Yu,

Wang

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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Short-term power load forecasting plays an important role in power system dispatching. To improve forecasting accuracy, a short-term load forecasting model based on stacking ensemble learning was proposed. Firstly, add effective multi-feature variables, and establishes a Stacking ensemble learning model for the load data and feature, which was ensembles by Light Gradient Boosting Machine (abbr. LightGBM) and eXtreme Gradient Boosting (abbr. XGBoost) for prediction. Finally, the comparison and experimental results show that the forecasting error of the proposed model is less than that of the comparative model.

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Application of stacking ensemble learning model in quantitative analysis of biomaterial activity

Cited by 16 publications

References 33 publications

Research progress of biological extinction materials

Research progress of biological extinction materials

FT-MIR combined with 3DCOS-ResNet model for rapid identification of wild and cultivated Gastrodia elata

Multi-feature short-term load forecasting based on stacking ensemble learning

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