On Classification of the Human Emotions from Facial Thermal Images: A Case Study Based on Machine Learning by Marius Sorin Pavel, Simona Moldovanu, Dorel Aiordachioaie

“…An augmentation process was applied to the initial raw images that led to the development of the two databases with added noise, as well as the subsequent augmentation of all images, i.e., rotation, reflection, translation and scaling. (2) Methods: The multiclass classification process was implemented through two subsets of methods, i.e., machine learning with random forest (RF), support vector machines (SVM) and k-nearest neighbor (KNN) algorithms and deep learning with the convolutional neural network (CNN) algorithm. (3) Results: The results obtained in this paper with the two subsets of methods belonging to the field of artificial intelligence (AI), together with the two categories of facial thermal images with added noise used as input, were very good, showing a classification accuracy of over 99% for the two categories of images, and the three corresponding classes for each. (4) Discussion: The augmented databases and the additional configurations of the implemented algorithms seems to have had a positive effect on the final classification results.…”
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Combining machine learning with UAV derived multispectral aerial images for wheat yield prediction, in southern Brazil by Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Viana, Adão Robson Elias, Giovani Benin, Lucas Scolari, Arthur Armadori, Diandra Ganascini Donato

“…The tested supervised machine learning algorithms included Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), combined with vegetation indices from the visible spectrum (RGB), multispectral indices, and bands. …”
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Evaluation of Different Machine Learning Models for Predicting Soil Erosion in Tropical Sloping Lands of Northeast Vietnam by Tuan Vu Dinh, Nhat-Duc Hoang, Xuan-Linh Tran

“…This study evaluates possibility of predicting erosion status by machine learning models, including fuzzy k-nearest neighbor (FKNN), artificial neural network (ANN), support vector machine (SVM), least squares support vector machine (LSSVM), and relevance vector machine (RVM). …”
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A new machine learning approach based on spatial fuzzy data correlation for recognizing sports activities by Xu Pengru, Zhou Junhui, Kausar Nasreen, Lin Chunlei, Lu Qianqian, Ghaderpour Ebrahim, Pamucar Dragan, Zadeh Ardashir M.

“…The existing research difficulties are overcome by applying the proposed spatial data correlation with a support vector machine (SDC-SVM). The algorithm uses the hyperplane function that recognizes sportsperson activities and improves overall activity recognition efficiency. …”
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Applying Canny edge detection and Hough transform algorithms to identify irrigation channel boundaries in irrigation districts by LU Hongfei, MAO Hanyu, ZHOU Hao, ZHEN Bo, ZHONG Yao, YANG Bo

“…【Objective】Airborne technologies have been increasingly used in agricultural sectors for various purposes. In this paper, we developed a fast algorithm for accurately detecting irrigation channel boundaries to support intelligent water resource management in irrigation districts. …”
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A novel method to predict white blood cells after kidney transplantation based on machine learning by Songping He, Xiangxi Li, Zunyuan Zhao, Bin Li, Xin Tan, Hui Guo, Yanyan Chen, Xia Lu

“…Eight machine learning algorithms, including Logistic-L1, Logistic-L2, support vector machine, decision tree, random forest, multilayer perceptron, extreme gradient boosting and light gradient boosting machine, were used for the five-fold cross-validation on all data sets, and the algorithm with the best performance was selected as the final prediction algorithm based on the average area under the curve. …”
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Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques by Rami Mustafa A. Mohammad, Malak Aljabri, Menna Aboulnour, Samiha Mirza, Ahmad Alshobaiki

“…The dataset was analysed using seven ML classifiers, namely, Bagging, J48, logistic regression (LR), random forest (RF), support vector machine (SVM), naïve Bayes (NB), and threshold selector. …”
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Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method by Suha Falih Mahdi Alazawy, Mohammed Ali Ahmed, Saja Hadi Raheem, Hamza Imran, Luís Filipe Almeida Bernardo, Hugo Alexandre Silva Pinto

“…The RF algorithm was contrasted with single-learner machine learning models: Support Vector Regression (SVR) and k-Nearest Neighbors (KNN). …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…The study adopted multiple machine learning (ML) algorithms, including random forest (RF), K-nearest neighbors (KNN), decision tree (DT), naïve Bayes (NB), support vector machines (SVM), and logistic regression (LR). …”
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Applications of Machine Learning in Human Factors and Ergonomics: A Comprehensive Review of Research From the Past Decade by Erman Cakit, Waldemar Karwowski

“…ML techniques examined include Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Random Forest (RF), Decision Trees (DTs), K-Nearest Neighbors (KNN), Naive Bayes (NB), boosting algorithms, and k-means clustering. …”
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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

“…Subsequently, the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms were adopted to screen key ARGs related to DN. …”
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Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data. by Shayla Naznin, Md Jamal Uddin, Ahmad Kabir

“…<h4>Methods</h4>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. …”
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Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study by Natthanaphop Isaradech, Wachiranun Sirikul, Nida Buawangpong, Penprapa Siviroj, Amornphat Kitro

“…The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. …”
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Optimal design of high‐performance rare‐earth‐free wrought magnesium alloys using machine learning by Shaojie Li, Zaixing Dong, Jianfeng Jin, Hucheng Pan, Zongqing Hu, Rui Hou, Gaowu Qin

“…The ML algorithms, including support vector machine regression (SVR), artificial neural network, and other three methods, are employed, and the SVR has the best performance in predicting mechanical properties based on the components, and process parameters, with the mean absolute percentage error of YS, UTS, and EL being 6.34%, 4.19%, and 13.64% in the test set, respectively. …”
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BREAST-CAD: A Computer-Aided Diagnosis System for Breast Cancer Detection Using Machine Learning by Riyam M. Masoud, Ramadan Madi Ali Bakir, M. Sabry Saraya, Sarah M. Ayyad

“…This research presents a novel Computer-Aided Diagnosis (CAD) system called BREAST-CAD, developed to support clinicians in breast cancer detection. Our approach follows a three-phase methodology: Initially, a comprehensive literature review between 2000 and 2024 informed the choice of a suitable dataset and the selection of Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees (DT) Machine Learning (ML) algorithms. …”
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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

“…Four machine learning algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and naive Bayes (NB), were used to develop models for predicting the risk of lower extremity DVT occurrence in GC patients. …”
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Machine learning model for diagnosing salivary gland adenoid cystic carcinoma based on clinical and ultrasound features by Huan-Zhong Su, Zhi-Yong Li, Long-Cheng Hong, Yu-Hui Wu, Feng Zhang, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The support vector machine model performed robustly and accurately. …”
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Efficient Task Scheduling in Cloud Computing: A Multiobjective Strategy Using Horse Herd–Squirrel Search Algorithm by V. Parthasaradi, A. Karunamurthy, C. H. Hussaian Basha, S. Senthilkumar

“…The major aim of the research work is to reduce the cost and the execution time as well as to improve the resource utilization of the task scheduling problem using the improved support vector machine (ISVM) and the optimization concept. …”
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