A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

“…Based on key operating parameters like voltage, current, and speed, this article describes how machine learning (ML) algorithms like Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Support Vector Machines (SVM), and Extreme Gradient Boosting with Feature Interaction (XGBoost + FIS) are used to detect different motor faults. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…This study proposes novel hybrid models through a comprehensive investigation of resampling techniques and machine learning algorithms. Four ensemble methods—Random Forest (RF), Extremely Randomized Trees (ET), Adaptive Boosting (ADA), and Extreme Gradient Boosting (XGB)—along with traditional methods such as Support Vector Regression (SVR) and K-Nearest Neighbors (KNN), were employed and compared for daily streamflow forecasting in the Thale Sap Songkhla Basin, southern Thailand. …”
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Development and validation of interpretable machine learning models for postoperative pneumonia prediction by Bingbing Xiang, Yiran Liu, Shulan Jiao, Wensheng Zhang, Shun Wang, Mingliang Yi

“…Through correlation screening, chi-square tests, and feature importance ranking, 47 variables were narrowed down to 5 potential predictive factors based on the main cohort of 528 patients. Nine machine learning models, including k-nearest neighbors, support vector machine, random forest, decision tree, gradient boosting machine, adaptive boosting, naive bayes, general linear model, and linear discriminant analysis, were developed and validated to predict postoperative pneumonia. …”
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Comparative Analysis of Text Mining Classification Algorithms for English and Indonesian Qur’an Translation by Rahmat Hidayat, Sekar Minati

“…The classification experiment uses Support Vector Machine (SVM), Naive Bayes, k-Nearest Neighbor (kNN), and J48 Decision Tree classifier algorithms with Al-Baqarah verses translated to English and Indonesian as the dataset which was labeled by three most fundamental aspects of Islam: 'Iman' (faith), 'Ibadah' (worship), and 'Akhlaq' (virtues). …”
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A novel pedestrian detection algorithm based on data fusion of face images by Jianhu Zheng, Jinshuan Peng

“…Building upon the theoretical principles used to solve small-sample statistical problems, a new hypothesis has been developed; using this concept, we integrate the conjugate orthonormalized partial least-squares regression with the revised support vector machine algorithm to undertake the solution of the facial recognition problem. …”
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A novel Smishing defense approach based on meta-heuristic optimization algorithms by Mohammad Alshinwan, Osama A. Khashan, Zakwan Alarnaout, Salam Salameh Shreem, Ahmed Younes Shdefat, Nader Abdel Karim

“…The Support vector machine (SVM) is used for feature extraction and categorization. …”
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Optimization of particle filter tracking algorithm based on weakly supervised attribute learning by Hui Zhang, Dawang Shen

“…The simulation results show that compared with recurrent neural networks, this method reduces the recognition errors of target center point and target size by 36.61% and 37.53% respectively during the daytime. Compared with the support vector machine model, this method reduces recognition errors by 23.01% and 28.43%, respectively. …”
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A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM by Lu Yu, Jianling Qu, Feng Gao, Yanping Tian

“…However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. …”
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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset by Soojung Ha, Juho Park, Kyuri Jo

“…Results In the experiments, Support Vector Regression algorithm and gene features selected with LINC L1000 dataset showed the best performance in terms of accuracy and execution time. …”
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PredβTM: A Novel β-Transmembrane Region Prediction Algorithm. by Amrita Roy Choudhury, Marjana Novič

“…Here, we describe PredβTM, a transmembrane region prediction algorithm for β-barrel proteins. Using amino acid pair frequency information in known β-transmembrane protein sequences, we have trained a support vector machine classifier to predict β-transmembrane segments. …”
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Intrusion detection algorithm of wireless network based on network traffic anomaly analysis by Xiangqian Nie, Jiao Xing, Qimeng Li, Fan Xiao

“…These features are analyzed by an intrusion detection module combining particle swarm optimization and support vector machine algorithms. Experimental results demonstrate that the algorithm efficiently extracts traffic anomalies, accurately detects attack duration and traffic volume changes in compromised nodes, and maintains a false detection rate below 6 %.…”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…The density is introduced to determine the initial center of the cluster, and the purity metric is introduced to determine the appropriate density radius of the cluster center, to achieve the most effective reduction of the support vector machine training samples.…”
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Evaluating the Nuclear Reaction Optimization (NRO) Algorithm for Gene Selection in Cancer Classification by Shahad Alkamli, Hala Alshamlan

“…Employed as a standalone approach without prior dimensionality reduction, NRO was assessed using both Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN). …”
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Euclidean Distance-Based Tree Algorithm for Fault Detection and Diagnosis in Photovoltaic Systems by Youssouf Mouleloued, Kamel Kara, Aissa Chouder, Abdelhadi Aouaichia, Santiago Silvestre

“…A comparison study between the developed fault detection and diagnosis methodology and Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors algorithms is conducted. …”
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Crow Search Algorithm for MEMS Gyroscope Temperature Drift Signal and Processing for Denoising by Xichen Wang, Huiliang Cao, Xiaomin Duan

“…Third, for the filtering of the drift part, the least square support vector machine optimized by the crow search algorithm (CSA-LSSVM) was used to filter, so as to reduce the effect of temperature drift. …”
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Research on basketball footwork recognition based on a convolutional neural network algorithm by Weili Bao, Yong Bai

“…In subsequent simulation experiments, ten basketball players' footwork data were collected using sensors. The improved CNN algorithm was used for footwork recognition and compared with the support vector machine (SVM) and traditional CNN algorithms. …”
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Multifactor Stock Selection Strategy Based on Machine Learning: Evidence from China by Jieying Gao, Huan Guo, Xin Xu

“…The main findings are as follows: the support vector regression has the most stable successful rate for predicting, while ridge regression and linear regression have the most unstable successful rate with more extreme cases; algorithm of support vector regression fitting higher-degree polynomials in Chinese A-share market is optimized, compared with the traditional linear regression both in terms of stock return and retracement control; the results of support vector regression significantly outperforming the CSI 500 index prove further.…”
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Fault location and isolation technology for power grid automation based on intelligent algorithms by Qi Guo, Fuhe Wang, Suxia Cheng, Ke Wang, Yifan Zhang

“…Methodology The FLA algorithm uses a Support Vector Machine (SVM) classifier to predict fault locations based on key variables like voltage, current, frequency, line impedance, and meteorological conditions. …”
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Machine Learning Innovations for Improving Mineral Recovery and Processing: A Comprehensive Review* by Korie, Josephmartin Izuchukwu*, Chudi-Ajabor, Ogochukwu Gabriela, Ezeonyema, Chukwudalu Chukwuekezie, Oshim, Francisca Ogechukwu

“…The emergence of ML algorithms, such as Artificial Neural Networks (ANN), Support Vector Machines, and others, trigger this paradigm. …”
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