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    Reverse migration prediction model based on machine learning / Azreen Anuar by Anuar, Azreen

    Published 2024
    “…For this purpose, three (3) algorithms have been assessed, namely, the Random Forest, Decision Tree, and Gradient Boosted Tree. …”
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    Development of electronic nose for classification of aromatic herbs using Artificial Intelligent techniques by Che Soh, Azura, Mohamad Radzi, Nur Fadzilah, Mohamad Yusof, Umi Kalsom, Ishak, Asnor Juraiza, Hassan, Mohd Khair

    Published 2018
    “…Two classification methods, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were used in order to investigate the performance of classification accuracy for this E-nose system. …”
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    Enhanced Adaptive Neuro-Fuzzy Inference System Classification Method for Intrusion Detection by Jia, Liu

    Published 2024
    “…On the KDDTest+ dataset, the proposed method also outperforms single CART and ANFIS in terms of various metrics other than precision. Since the CART tree is a binary tree, it can only represent the relationship between data through a split based on a single attribute at a single tree node. …”
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    A quality of experience approach in smartphone video selection framework for energy efficiency by Jofri, Muhamad Hanif

    Published 2021
    “…A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. …”
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    State of charge estimation for electric vehicles using random forest by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2024
    “…By leveraging decision trees and ensemble learning, the RF model forms resilient relationships between input parameters, such as voltage, current, ambient temperature, and battery temperatures, and SOC values. …”
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    Simultaneous measurement of multiple soil properties through proximal sensor data fusion: a case study by Wenjun, Ji, Adamchuk, Viacheslav I., Song, Chao Chen, Mat Su, Ahmad S., Ismail, Ashraf, Qianjun, Gan, Zhou, Shi, Biswas, Asim

    Published 2019
    “…After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). …”
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