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For: Cheng F, Yang C, Zhou C, Lan L, Zhu H, Li Y. Simultaneous Determination of Metal Ions in Zinc Sulfate Solution Using UV-Vis Spectrometry and SPSE-XGBoost Method. Sensors (Basel) 2020;20:E4936. [PMID: 32878223 DOI: 10.3390/s20174936] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Pinheiro Claro Gomes W, Gonçalves L, Barboza da Silva C, Melchert WR. Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee. Computers and Electronics in Agriculture 2022;198:107097. [DOI: 10.1016/j.compag.2022.107097] [Reference Citation Analysis]
2 Zheng L. Gesture recognition method for placement task in VR environment based on improved xgboost algorithm. 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) 2022. [DOI: 10.1109/cvidliccea56201.2022.9825097] [Reference Citation Analysis]
3 Zhao D, Wang J, Jiang X, Zhen J, Miao J, Wang J, Wu G. Reflectance spectroscopy for assessing heavy metal pollution indices in mangrove sediments using XGBoost method and physicochemical properties. CATENA 2022;211:105967. [DOI: 10.1016/j.catena.2021.105967] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
4 Younis SA, Ali TA, Serp P. Potential applicability of Zn0.05TiOxNy@MOF-5 nanocomposite for adsorption and electrochemical detection of Zn(II) in saline wastewater. Journal of Environmental Chemical Engineering 2021;9:106186. [DOI: 10.1016/j.jece.2021.106186] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
5 Byeon H. Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia. Applied Sciences 2021;11:2235. [DOI: 10.3390/app11052235] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Alajmi MS, Almeshal AM. Predicting the Tool Wear of a Drilling Process Using Novel Machine Learning XGBoost-SDA. Materials (Basel) 2020;13:E4952. [PMID: 33158099 DOI: 10.3390/ma13214952] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]