Ensemble machine learning for predicting academic performance in STEM education by Aklilu Mandefro Messele

“…To tackle these issues, our research focused on developing a predictive model for STEM students using advanced ensemble machine learning algorithms. We gathered secondary data from the University of Gondar, Addis Ababa University, and Bahir Dar University, employing techniques such as Random Forest, CatBoost, Extreme Gradient Boosting, Gradient Boosting, Decision Trees, Logistic Regression, and Support Vector Machines. …”
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Forecasting outbound student mobility: A machine learning approach. by Stephanie Yang, Hsueh-Chih Chen, Wen-Ching Chen, Cheng-Hong Yang

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Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…In this paper, an empirical comparison of the robustness of seven machine learning algorithms to credit risk, namely support vector machines (SVMs), naïve base, decision trees (DT), random forest (RF), gradient boosting (GB), K-nearest neighbour (K-NN), and logistic regression (LR), is carried out using the Lending Club credit data from Kaggle. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

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Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…In order to evaluate effects of artificial rainfall enhancement objectively and quantitatively, combing linear fitting, polynomial regression, spline regression and 3 other machine learning methods including decision tree, support vector machine and neural network, the relationship model between the rainfall in the target area and the contrast area is established based on rainfall data and operation information of recent 10 years in Fujian. …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…Additionally, the “AIR PEN 3” E-nose, equipped with 10 metal oxide sensors, was employed to identify volatile compounds in the pet food samples, categorized into 10 different groups. Machine learning algorithms, including liner regression, k-nearest neighbors, support vector machines, random forests, XGBoost, and multi-layer perceptron (MLP), were used to classify the samples based on their volatile profiles. …”
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The Evaluation of Music Teaching in Colleges and Universities Based on Machine Learning by Xia Xiongjun, Danmeng Lv

“…This article uses decision tree algorithms, support vector machines, Bayesian theory, and random forest four different classification techniques to evaluate the student curriculum evaluation dataset. …”
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Perbandingan Algoritme Machine Learning untuk Memprediksi Pengambil Matakuliah by Fitra A. Bachtiar, Indra K. Syahputra, Satrio A. Wicaksono

“…Classification method used in this study are k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithms to compare their performance for prediction cases. …”
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Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning by HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi

“…This paper reviews commonly used machine learning models, such as random forests, support vector machines, and deep learning, in antimicrobial activity screening. …”
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An Iterative Pixel-Based Dimensional Voting Model for High Spatial-Resolution Image Classification by Muhamad Asyraf Mohd Pouzi, Haza Nuzly Abdull Hamed, Muhammad Razib Othman, Hishammuddin Asmuni, Mohd Adham Isa, Husni Ruslai

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Quality assessment of chicken using machine learning and electronic nose by Hassan Anwar, Talha Anwar

“…Random Forest achieved 100 % accuracy with randomly split data and 69 % accuracy with non-randomly split data. Support Vector Machine, using the recursive feature elimination technique, attained 78.5 % accuracy without random splitting. …”
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Optimal Placement of Wind Power System Using Machine Learning by Abdul Karim, Muhammad Amir Raza, Darakhshan Ara, Muhammad Shahid, Shakir Ali Soomro

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Machine Learning Ship Classifiers for Signals from Passive Sonars by Allyson A. da Silva, Lisandro Lovisolo, Tadeu N. Ferreira

“…The classifiers included standard Support Vector Machines, K-Nearest Neighbors, Random Forests, Neural Networks and two less conventional approaches in this context: Linear Discriminant Analysis (LDA) and the XGBoost ensemble method. …”
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Assessment of Machine Learning Methods for Concrete Compressive Strength Prediction by Oluwafemi Omotayo, Chinwuba Arum, Catherine Ikumapayi

“…This research sought to forecast concrete compressive strength through six machine learning (ML) algorithms namely Linear Regression (LR), Random Forest (RF), Decision Trees (DT), Gradient Boost (GB), Support Vector Machine (SVM), and Categorical Gradient Boost (CatBoost), and to examine the significance of the input factors on the concrete compressive strength. …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

“…In this research, we employed various machine learning algorithms, both classification (supervised learning) and outlier detection (unsupervised learning) approaches, such as Naive Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM) for supervised learning, and Isolation Forest, Local Outlier Factor (LOF), and One-Class SVM for unsupervised learning. …”
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Predicting Forest Evapotranspiration using Remote Sensing and Machine Learning by B. Yadav, L. K. Sharma, B. Bijarniya

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A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data by Shahid Ahmad Wani, SMK Quadri, Mohammad Shuaib Mir, Yonis Gulzar

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Machine learning-based prediction of FeNi nanoparticle magnetization by Federico Williamson, Nadhir Naciff, Carlos Catania, Gonzalo dos Santos, Nicolás Amigo, Eduardo M. Bringa

“…More than 1600 NPs were created and split into training and testing sets (70%–30% split), with features including the number of Ni/Fe surface and core atoms, potential energy distributions, pair correlation functions, and coordination distributions. Several machine-learning algorithms, including Random Forest (RF), Elastic Net, Support Vector Regression (SVR), and Gradient Boosting Regression (CatBoost), were applied to predict the average magnetic moment per atom of these NPs. …”
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Automatic Selection of Machine Learning Models for Armed People Identification by Alonso Javier Amado-Garfias, Santiago Enrique Conant-Pablos, Jose Carlos Ortiz-Bayliss, Hugo Terashima-Marin

“…Thereby, we initially developed six-armed people detectors (APD) based on six machine learning models: Random Forest Classifier (RFC-APD), Multilayer Perceptron (MLP-APD), Support Vector Machine (SVM-APD), Logistic Regression (LR-APD), Naive Bayes (NB-APD), and Gradient Boosting Classifier (GBC-APD). …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…The two models include the support vector machine (SVM) and 1D convolutional neural network (1D-CNN), which use ZMs, compared with the other three classification models of 2D-CNN, ResNet50, and VGG16 that apply the features from original images. …”
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