Compressive strength prediction of fly ash/slag-based geopolymer concrete using EBA-optimised chemistry-informed interpretable deep learning model by Yang Yu, Iman Munadhil Abbas Al-Damad, Stephen Foster, Ali Akbar Nezhad, Ailar Hajimohammadi

“…The results demonstrate that the EBA-optimised CNN outperforms traditional learning models, including support vector machine (SVM), extreme gradient boosting (XGBoost), and artificial neural networks (ANN), with higher performance in terms of R2, MAE, and RMSE. …”
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Sex estimation from the first and second ribs using 3D postmortem CT images in a Japanese population: A comparison of discriminant analysis and machine learning techniques by Tawachai Monum, Yohsuke Makino, Daisuke Yajima, Go Inoguchi, Fumiko Chiba, Suguru Torimitsu, Maiko Yoshida, Patison Palee, Yumi Hoshioka, Naoki Saito, Hirotaro Iwase

“…Sex estimation models using conventional discriminant analysis and ten machine learning algorithms including logistic regression (LR), Naive Bayes (NB), K-Nearest Neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN), and extra tree (ET), were achieved from PMCT measurements of the first and second rib and the accuracy of models were compared. …”
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Enhancing Neural Network Training Through Neuroevolutionary Models: A Hybrid Approach to Classification Optimization by Hyasseliny A. Hurtado-Mora, Luis A. Herrera-Barajas, Luis J. González-del-Ángel, Roberto Pichardo-Ramírez, Alejandro H. García-Ruiz, Katea E. Lira-García

“…Traditional classification algorithms, such as k-Nearest Neighbors (KNN), decision trees, Support Vector Machines (SVMs), and ANNs, often suffer from convergence to suboptimal solutions due to their training methods. …”
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Hyperspectral Detection of Pesticide Residues in Black Vegetable Based on Multi-Classifier Entropy Weight Method by Rongchang Jiang, Guoqiang Zhuang, Shijie Xie, Yang Wang, Guoqi Zhang, Dandan Qu, Wanzhi Wen

“…Three dimensionality reduction techniques, competitive adaptive reweighted sampling, random frog leaping, and successive projections algorithm, were compared. Models were built using eXtreme gradient boosting, random forest, and support vector machine algorithms. …”
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A hybrid model for predicting response to risperidone after first-episode psychosis by Giovany Oliveira Costa, Vanessa K. Ota, Matheus Rodrigues Luiz, Joice Santos Rosa, Gabriela Xavier, Jessica Honorato-Mauer, Marcos L. Santoro, Carolina Muniz Carvalho, Daniel A. Cavalcante, Amanda V.G. Bugiga, Rodrigo A. Bressan, Gerome Breen, Ary Gadelha, Cristiano Noto, Diego R. Mazzotti, Sintia I. Belangero

“…Analyses were performed using a support vector machine (SVM), k-nearest neighbors (kNN), and random forests (RF). …”
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Prediction of cardiovascular diseases based on GBDT+LR by Zengxiao Chi, Li Liu, Liqin Yi, Lin Shi

“…Using the UCI cardiovascular disease dataset, we conduct experimental comparisons between the proposed model and other common disease classification algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). …”
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Confidence-Aware Ship Classification Using Contour Features in SAR Images by Al Adil Al Hinai, Raffaella Guida

“…Two segmentation methods for the contour extraction were investigated: a classical approach using the watershed algorithm and a U-Net architecture. The features were tested using a support vector machine (SVM) on the OpenSARShip and FUSAR-Ship datasets, demonstrating improved results compared to existing handcrafted features in the literature. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. …”
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ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease by Tripti Tripathi, Rakesh Kumar

“…The characteristics are subsequently used to educate five machine learning algorithms, namely k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), XGBoost, and random forest (RF). …”
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Automatic construction of global cloud sample database based on Landsat imagery by Tao He, Guihua Huang, Lei Zhang, Daiqiang Wu, Yichuan Ma

“…In addition to RF, light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and support vector machine (SVM) were also trained based on UAC-CSD sample database and verified using L8_Biome. …”
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A fair and efficient two-step procedure for sugarcane properties prediction based on near-infrared spectra by Luiz Alexandre Peternelli, Andréa Carla Bastos Andrade, Cristina Silva Dias, Reinaldo Francisco Teófilo

“…We applied our approach to assess three classification techniques – Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machines (SVM), and Random Forests (RF) – about their performance in predicting the classes of two sugarcane properties derived from NIR data. …”
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Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment by Maria de Fátima Araújo Alves, Héliton Pandorfi, Rodrigo Gabriel Ferreira Soares, Gledson Luiz Pontes de Almeida, Taize Calvacante Santana, Marcos Vinícius da Silva

“…The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively.…”
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Evaluating Supervised Learning Classifier Performance for OFDM Communication in AWGN-Impacted Systems by Lavanya Vaishnavi D A, Anil Kumar C

“…The accuracy of the proposed model is subjected to various metrics considered to measure the performance of the model with limited analysis viz., Peak Signal to Noise Ratio (PSNR), Power to Average Power Ratio (PAPR) and Bit Error Ratio (BER) and Accuracy, in the perspective of the AWGN channel Mean and Variance are considered as variables from 0.4 to 1 and 0.7 ± 0.3 respectively. The ML algorithms are used as a tool for classifier at the receiver and significantly three supervised learning algorithms are used such as K Nearest Neighbours (KNN), Support Vector Machine (SVM) and Random Forest (RF) due to its advantages over the other existing methods and unsupervised learning methods are reserved for the further usage of metric calculation. …”
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Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population by Mendoza-Mendoza MM, Acosta-Jiménez S, Galván-Tejada CE, Maeda-Gutiérrez V, Celaya-Padilla JM, Galván-Tejada JI, Cruz M

“…Data are split by sex, and feature selection is performed using GALGO, a genetic algorithm-based tool. Classification models including Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression are trained and evaluated. …”
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Optimizing Cardiovascular Risk Assessment with a Soft Voting Classifier Ensemble by Ammar Oad, Zulfikar Ahmed Maher, Imtiaz Hussain Koondhar, Karishima Kumari, Hammad Bacha

“…The proposed ensemble soft voting classifier employs an ensemble of seven machine learning algorithms to provide binary classification, the Naïve Bayes K Nearest Neighbor SVM Kernel Decision Tree Random Forest Logistic Regression and Support Vector Classifier. …”
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Planococcus citri in coffee trees by supervised classification using multispectral images by Kamila Fernanda Rossati, Vanessa Andaló, George Deroco Martins, Gleice Aparecida de Assis, Vinícius Silva Werneck Orlando, Letícia Pasqualin Messias Arriero, Lucas Silva de Faria, Renan Zampiroli

“…The classifications were made using Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest algorithms. …”
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EEG microstate analysis in children with prolonged disorders of consciousness by Yi Zhang, Zhichong Hui, Yuwei Su, Weihang Qi, Guangyu Zhang, Liang Zhou, Jiamei Zhang, Kaili Shi, Yonghui Yang, Lei Yang, Gongxun Chen, Sansong Li, Mingmei Wang, Dengna Zhu

“…This study demonstrates that EEG microstate analysis is an objective, user-friendly tool for differentiating consciousness states in children with pDoC. Machine learning algorithms, specifically support vector machines, revealed that MS C occurrence is a potential neurophysiological biomarker.…”
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Prognostic correlation analysis of colorectal cancer patients based on monocyte to lymphocyte ratio and folate receptor-positive circulating tumor cells and construction of a machi... by Siying Pan, Chi Lu, Chi Lu, Hongda Lu, Hongda Lu, Hongfeng Zhang

“…Progression-Free Survival (PFS) and Overall Survival (OS) were analyzed using COX analysis and the Kaplan-Meier survival curve. Three ML algorithms, namely, random forest (RF), support vector machine (SVM), and logistic regression (LR), were utilized to construct the predictive models, and their performance metrics including accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), precision, recall, F1 value, AUC, and calibration curve were compared.ResultsMLR, FR+ CTCs, and T stage independently predicted PFS (P<0.05), both higher MLR and FR+CTCs levels indicating a significantly shorter PFS (P=0.004). …”
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Identification of Key Modules of Lung Cancer in Gene Regulatory Network using Greedy Modularity Optimization Approach by Zahra Sadat Mirghadery, Mehrdad Kargari, Mostafa Akhavan-Safar

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Blending Ensemble Learning Model for 12-Lead Electrocardiogram-Based Arrhythmia Classification by Hai-Long Nguyen, Van Su Pham, Hai-Chau Le

“…Experiments conducted with seven diverse machine learning algorithms (Adaptive Boosting, Extreme Gradient Boosting, Decision Trees, k-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machine) demonstrate that the proposed blending solution, utilizing an LR meta-model with three optimal base models, achieves a superior classification accuracy of 96.48%, offering an effective tool for clinical decision support.…”
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