Machine learning-based monitoring of land cover and reclamation plantations on coal-mined landscape using Sentinel 2 data by Mayank Pandey, Alka Mishra, Singam L. Swamy, James T. Anderson, Tarun Kumar Thakur

“…This study examines the reclamation of coal mine overburdens through reforestation, using high-resolution Sentinel 2 satellite data classified by various Machine Learning (ML) algorithms. Support Vector Machine has been identified as a more accurate and effective ML algorithm compared to Random Forest and Maximum Likelihood Classifier in delineating land use and vegetation classes, particularly forests, and in distinguishing reclamation plantations into three age classes: young (4 ± 3 years), middle-aged (10 ± 2 years), and mature (15 ± 2 years). …”
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Quantum key distribution through quantum machine learning: a research review by Krupa Purohit, Ajay Kumar Vyas

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Predicting Diabetes Mellitus with Machine Learning Techniques by Heba Ahmed Jassim, Omar R. Kadhim, Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong

“…The Random Forest and Decision Tree models also perform well in terms of their ability to deliver strong performance, and the outcome shows some incremental differences, suggesting their ability to manage the dataset is quite high. However, the Support Vector Machine (SVM) model performs worse than all the above models at 96.36% and seems to struggle with the correct classification of less frequent instances. …”
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Prediction model of water inrush risk level of coal seam floor based on KPCA-DBO-SVM by Wei Wang, Huangrui Wang, Xuping Li, Yun Qi, Xinchao Cui, Chenhao Bai

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Prediction of Shear Strength of Steel Fiber-Reinforced Concrete Beams with Stirrups Using Hybrid Machine Learning and Deep Learning Models by B. R. Kavya, A. S. Shrikanth, K. S. Sreekeshava

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Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…The methods employed included multi-linear regression (MLR), multi-nonlinear regression (MNLR), and several machine learning techniques: backpropagation artificial neural networks (ANNs), gradient boosting (GB), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN). …”
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Multi-class fault diagnosis of BF based on global optimization LS-SVM by ZHANG Hai-gang, ZHANG Sen, YIN Yi-xin

“…Aiming at the requirement of high speed and precision in blast furnace fault diagnosis systems, a new strategy based on global optimization least-squares support vector machines (LS-SVM) was proposed to solve this problem. …”
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Title not available by Bouali, Fakhreddine, Fedala, Semchedine, André, Hugo, Felkaoui, Ahmed

“…Then, the selection phase is performed by the Minimum Redundancy Maximum Relevance (MRMR) algorithm to select the most relevant features. Finally, the classification is carried out by a cubic support vector machine (SVM) for the detection and identification stages of various bearings fault conditions. …”
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Estimating latent heat flux of subtropical forests using machine learning algorithms by Harekrushna Sahu, Pramit Kumar Deb Burman, Palingamoorthy Gnanamoorthy, Qinghai Song, Yiping Zhang, Huimin Wang, Yaoliang Chen, Shusen Wang

“…By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. …”
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Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques by Dina Wulan Yekti rahayu, Khothibul Umam, Maya Rini Handayani

“…This study explores the performance of five sentiment classification algorithms—Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest—on an imbalanced sentiment dataset, with the SMOTE technique applied as a comparison. …”
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Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings by Harshal Aher, Nilesh Ghuge

“…Kurtosis was extracted as the sole feature from the vibration signals for fault classification. Several machine learning models, including artificial neural network (ANN), decision tree, support vector machine (SVM), random forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), gradient boosting and categorical boosting (CatBoost), were employed to predict fault severity. …”
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia by Cheng-Wen Lee, Mao-Wen Fu, Chin-Chuan Wang, Muh. Irfandy Azis

“…The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. …”
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Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms by Abdurrahman Burak Guher, Sakir Tasdemir, Bulent Yaniktepe

“…In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K-Nearest Neighbors (K-NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. …”
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Advanced Ai Tools for Predicting Mechanical Properties of Self-Compacting Concrete by AGRAWAL Achal, CHANDAK Narayan

“…The present study utilizes advanced numerical evaluation techniques like Artificial Intelligence (AI), including Support Vector Machines (SVM), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems with Genetic Algorithms (ANFIS-GA), Gene Expression Programming (GEP), and Multiple Linear Regression (MLR) to develop and compare the predictive models for determination of compressive and tensile strength. …”
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Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms by Walaa H. Elashmawi, Adel Djellal, Alaa Sheta, Salim Surani, Sultan Aljahdali

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Prediction models based on machine learning algorithms for COVID-19 severity risk by Hansong Zhang, Ying Wang, Yan Xie, Cuihan Wang, Yuqi Ma, Xin Jin

“…Methods Prediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. …”
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Predicting diabetic retinopathy based on routine laboratory tests by machine learning algorithms by Xiaohua Wan, Ruihuan Zhang, Yanan Wang, Wei Wei, Biao Song, Lin Zhang, Yanwei Hu

“…Using 39 optimal variables, a prediction model was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm and compared with four other algorithms: support vector machine (SVM), gradient boosting decision tree (GBDT), neural network (NN), and logistic regression (LR). …”
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Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms by Haobo Kong, Yong Li, Ya Shen, Jingjing Pan, Min Liang, Zhi Geng, Yanbei Zhang

“…Using the development cohort, candidate variables were selected via the Recursive Feature Elimination (RFE) method. Five machine learning algorithms, logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and support vector machine (SVM), were utilized to construct the predictive models. …”
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IMPROVING AGRICULTURAL YIELDS IN THE DEMOCRATIC REPUBLIC OF CONGO USING MACHINE LEARNING ALGORITHMS by Rodolphe Nsimba Malumba, Mardochee Longo Kayembe, Fiston Chrisnovic Balanganayi Kabutakapua, Bopatriciat Boluma Mangata, Trésor MAZAMBI KILONGO, Rufin Tabiaki Tandele, Emmanuel Ntanyungu Ndizieye, Parfum Bukanga Christian

“…The data comes from a variety of sources, including METTELSAT, the World Meteorological Organization (WMO) and WorldClim for climate data, and the DRC Ministry of Agriculture and the FAO for soil and agricultural data. The algorithms evaluated include linear regression, random forest regression, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). …”
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Letter and Person Recognition in Freeform Air-Writing Using Machine Learning Algorithms by Huseyin Kunt, Zeki Yetgin, Furkan Gozukara, Turgay Celik

“…Fourier and wavelet transforms are used to extract features and the performances of various machine learning algorithms, namely Decision Tree, Random-Forest, K-Nearest Neighbors, Support Vector Machine, Artificial Neural Networks, and SubSpace KNN, are comparatively studied. …”
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