2022
DOI: 10.1016/j.arabjc.2022.104062
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Predictive modeling and computational machine learning simulation of adsorption separation using advanced nanocomposite materials

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Cited by 20 publications
(1 citation statement)
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“…In biology and medicine, i.e., predict metal oxide nanoparticle toxicity in immune cells [67] and detection for COVID-19 [68]. In the aspect of nanocomposite materials, Gradient Boosted Trees is applied [69]. Applying bagging, majority voting to fault detection in the electronics, Catboost, XGBoost in electrical [70,71], In computer science, various ensemble learning methods, like a machine learning-based intrusion detection system (ML-IDS) [72], are also working to solve problems on local explanation [73], Cyber-Attacks [72,74].…”
Section: Application In M Categorymentioning
confidence: 99%
“…In biology and medicine, i.e., predict metal oxide nanoparticle toxicity in immune cells [67] and detection for COVID-19 [68]. In the aspect of nanocomposite materials, Gradient Boosted Trees is applied [69]. Applying bagging, majority voting to fault detection in the electronics, Catboost, XGBoost in electrical [70,71], In computer science, various ensemble learning methods, like a machine learning-based intrusion detection system (ML-IDS) [72], are also working to solve problems on local explanation [73], Cyber-Attacks [72,74].…”
Section: Application In M Categorymentioning
confidence: 99%