Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context by Ana Gavrovska, Goran Zajić, Vesna Bogdanović, Irini Reljin, Branimir Reljin

“…The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). …”
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Drunk Driver Detection Using Thermal Facial Images by Chin-Heng Chai, Siti Fatimah Abdul Razak, Sumendra Yogarayan, Ramesh Shanmugam

“…Convolutional Neural Networks (CNNs) and YOLO (You Only Look Once) algorithms were employed to extract facial features, while classifiers such as Support Vector Machines (SVMs), Multi-Layer Perceptron (MLP), and K-Nearest Neighbors (KNN), as well as Random Forest and linear regression, classify individuals as sober or intoxicated based on their thermal images. …”
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A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM by Wei-Li Qin, Wen-Jin Zhang, Chen Lu

“…This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA), grid search (GS), 10-fold cross validation (CV), and one-versus-one support vector machine (SVM). This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. …”
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Prediction of formation pressure in underground gas storage based on data-driven method by SUI Gulei, FU Yujiang, ZHU Hongxiang, LI Zunzhao, WANG Xiaolin

“…The supervised learning model of formation pressure forecasting is established by three kinds of machine learning algorithms including extreme gradient boosting (XGBoost), support vector regression (SVR), and long short-term memory network (LSTM). …”
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Dimensions management of traffic big data for short-term traffic prediction on suburban roadways by Arash Rasaizadi, Fateme Hafizi, Seyedehsan Seyedabrishami

“…The models used include long short-term memory, support vector machine, and random forest. The results show that using cyclic features can increase traffic state prediction's accuracy than the model, including all the initial features (base model). …”
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An Analysis of Intelligent Turkish Text Classification Models for Routing Calls in Call Centers: A Case Study on the Republic of Turkiye Ministry of Trade Call Center by Muammer Özdemir, Yasin Ortakcı

“…Using a specific dataset of 20,000 phone call texts collected from the MTCC, the study employs TF-IDF, Word2Vec, and GloVe text vectorization techniques and applies various machine learning algorithms such as K-Nearest Neighbours, Naive Bayes, Support Vector Machines, Adaptive Boosting, Decision Tree and Random Forest for text classification. …”
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Determination of Covid-19 Possible Cases by Using Deep Learning Techniques by Mete Yağanoğlu, Çinare Oğuz

“…Their performance was measured using Support Vector Machines (SVM), Nearest neighbor algorithm (KNN), Linear Discrimination Analysis (LDA), Decision trees, Random forest (RF) and Naive Bayes methods as the methods of classification. …”
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Automatic Construction and Extraction of Sports Moment Feature Variables Using Artificial Intelligence by Zhao Zhang, Wang Li, Yuyang Zhang

“…In this paper, we study the automatic construction and extraction of feature variables of sports moments and construct the extraction of the specific variables by artificial intelligence. In this paper, support vector machines, which have better performance in the case of small samples, are selected as classifiers, and multiclass classifiers are constructed in a one-to-one manner to achieve the classification and recognition of human sports postures. …”
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Forecast Model of TV Show Rating Based on Convolutional Neural Network by Lingfeng Wang

“…First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…Rubberized cementitious composites have emerged as a sustainable alternative in the construction sector by promoting circular economy principles. However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. …”
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Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions by Maria Sigopi, Cletah Shoko, Timothy Dube

“…Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…MethodsThe first step relied on designing a predictive model based on clinical data (ie, risk factors identified in the literature) extracted from the clinical data warehouse of the Rennes Hospital and machine learning algorithms (logistic regression, random forest, and support vector machine). …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…For instance, in the energy and power sector, intelligent gas meters and automated fire suppression systems manage gas-related risks effectively, while in the health sector, AI-powered health monitoring devices and mental health support applications improve patient and worker safety. …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

“…Results A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). …”
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Rapid identification of foodborne pathogenic bacteria using hyperspectral imaging combined with convolutional neural networks(高光谱结合卷积神经网络对食源性致病菌的快速识别)... by 周贯旭(ZHOU Guanxu), 姜红(JIANG Hong), 徐雪芳(XU Xuefang)

“…At the same time, establish random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classification models for the reduced spectral data. …”
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How Predictable Is Electric Vehicle Adoption? Exploring the Broader Role of Renewables in Transportation Using a Data-Driven Approach by Simona-Vasilica Oprea, Adela Bara

“…Several classifiers are tested as baseline (Logistic Regression) or as cutting-edge algorithms (Random Forest-RF, eXtreme Gradient Boost-XGB, Light Gradient Boosting Machine-LGBM, Support Vector Classifier). …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…To address this issue, this study employs a One-Class Support Vector Machine (OCSVM) trained exclusively on features extracted from healthy brain MRI images, using both deep learning architectures—such as DenseNet121, VGG16, MobileNetV2, InceptionV3, and ResNet50—and classical feature extraction techniques. …”
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Local Transmissibility-Based Identification of Structural Damage Utilizing Positive Learning Strategies by Oguz Gunes, Burcu Gunes

“…The numerical study shows detection accuracy above 90% with one-class support vector machine (OCSVM) and correct localization across all damage scenarios. …”
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Analysis of corn price forecast in China based on Lasso-XGBoost-SHAP by Wenming Cheng, Fangyuan Li

“…Results demonstrate that the Lasso-XGBoost model outperforms traditional linear models (LM) and other algorithms, including SVM (Support Vector Machine) and MLP (Multilayer Perceptron), with root mean squared error (RMSE) of 0.094, coefficient of determination (R2) of 0.973, mean absolute error (MAE) of 0.072, representing a 7.84% reduction in RMSE compared to standalone XGBoost. …”
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A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition by Andisani Nemavhola, Colin Chibaya, Serestina Viriri

“…In contrast, earlier models like Support Vector Machine (SVM) and Gabor Wavelets perform well on smaller datasets but lack scalability for larger, more complex datasets. …”
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