Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Methods: Six models—Random Forest, Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, Gradient Boosting Machine, and Decision Tree—were trained and tested on a discovery dataset (N=3,546) and validated in the UK Biobank (N=31,620). …”
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Automated snow cover detection on mountain glaciers using spaceborne imagery and machine learning by R. Aberle, E. Enderlin, S. O'Neel, C. Florentine, L. Sass, A. Dickson, H.-P. Marshall, A. Flores

“…The Sentinel-2 classifier (support vector machine) produces the most accurate glacier mass balance and snowmelt indicators and distinguishes snow from ice and firn the most reliably. …”
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Design of an evolutionary model for international trade settlement based on genetic algorithm and fuzzy neural network. by Jiaqing Huang, Yang Liu, Miaomiao Tu, Osama Sohaib

“…Results show that GA-FNN achieves an average classification accuracy of approximately 90% across high, medium, and low risk levels, outperforming traditional methods such as logistic regression, SVM (Support Vector Machine), and other metaheuristics like PSO (Particle Swarm Optimization) and SA (Simulated Algorithm). …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. …”
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Application of SVM algorithm in fluid prediction of volcanic reservoirs in Nanpu Sag, Bohai Bay Basin by ZHANG Ying,QU Lili,ZHU Lu,ZHANG Yan,HAN Siyang,ZENG Cheng

“…For this reason, the SVM（Support Vector Machine） algorithm of machine learning is used to predict the fluids of unknown reservoirs for the volcanic rock reservoirs in the Nanpu Sag of the Bohai Bay Basin. …”
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A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous, Nasem Badreldin

“…The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). …”
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Comparative Analysis of Machine learning Methods to Identify signs of suspicious Transactions of Credit Institutions and Their Clients by Yu. M. Beketnova

“…The author concluded that the PCA-Based Anomaly Detection algorithm showed more accurate results compared to the One-Class Support Vector Machine algorithm. …”
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IoT enabled health monitoring system using rider optimization algorithm and joint process estimation by J. Prabin Jose, G. Jaffino, Mohammed Al Awadh, Koppula Srinivas Rao, Yan Yafang, Krishna Moorthy Sivalingam

“…The performance of the proposed method is compared with other five machine learning algorithms, including Support Vector Machine, Random Forest, Gradient Boosting, Naive Bayes, and Multilayer Perceptron neural networks. …”
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Comparison among grazing animal behavior classification algorithms for use with open-source wearable sensors by B.R. dos Reis, S. Sujani, D.R. Fuka, Z.M. Easton, R.R. White

“…Behavior classification analyses leveraged simple approaches (analysis of variance and logistic regression), as well as more complex machine learning algorithms (support vector machine (SVM) and random forest (RF)) to better understand the trade-offs between classification approach complexity and accuracy. …”
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Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model by Lv Deguo

“…Comparative analyses demonstrate a-HybridSN’s superior performance against established methods such as HybridSN, random forest, SoftMax, decision tree, and support vector machine. These results underscore the robustness and adaptability of a-HybridSN.…”
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A machine vision approach for classification and dimensional design of furniture panels using GMM-SVM by Yuan Tian, Li Zhao, Haoxin Li

“…This method combines Gaussian mixture model (GMM) and support vector machine (Support vector machine, SVM) to effectively identify classification. …”
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Detection of Wood Boring Insects’ Larvae Based on the Acoustic Signal Analysis and the Artificial Intelligence Algorithm by Piotr BILSKI, Piotr BOBIŃSKI, Adam KRAJEWSKI, Piotr WITOMSKI

“…The employed classification was based on features defined in the time domain followed by the support vector machine used as the binary classifier. …”
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Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation by Caroline Bönisch, Christian Schmidt, Dorothea Kesztyüs, Hans A Kestler, Tibor Kesztyüs

“…Logistic regression, k-nearest neighbors, a naive bayes classifier, a decision tree classifier, a random forest classifier, extreme gradient boosting (XGB), and support vector machines (SVM) were selected as machine learning algorithms. …”
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Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm. by Timothy Nugent, David T Jones

“…Using molecular dynamics data, we have trained and cross-validated a support vector machine (SVM) classifier to predict per residue lipid exposure with 69% accuracy. …”
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Comparison of Naive Bayes and SVM Algorithms for Sentiment Analysis of PUBG Mobile on Google Play Store by Putri Ratna Sari, Dwi Rosa Indah, Errissya Rasywir, Mgs Afriyan firdaus, Ghita Athalina

“…This research will employ a positive and negative sentiment analysis of Indonesian PUBG Mobile reviews on the Google Play Store, utilizing a comparative approach to evaluate the performance of two algorithms: Naïve Bayes and Support Vector Machine (SVM). …”
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Sample Denoising and Optimization Technique Based on Noise Filtering and Evolutionary Algorithms for Imbalanced Data Classification by Fhira Nhita, Asniar, Isman Kurniawan, Adiwijaya

“…Then, the selected train set is used to develop classification model using five classifier, i.e., decision tree, logistic regression, support vector machine, k-nearest neighbors, and naive bayes. …”
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Review of Icing Prediction Model and Algorithm for Overhead Transmission Lines Considering Time Cumulative Effects by Chuanqi WANG, Liwen WU, Zhibin DENG, Weifeng DENG, Bin YANG

“…Secondly, the combination and intercrossing mode of support vector machine, hybrid swarm intelligence optimization algorithm, genetic algorithm in the model focus on the identification and modeling of icing process. …”
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Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions by A. M. Umbrajkaar, A. Krishnamoorthy, R. B. Dhumale

“…The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. …”
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DATA MINING ALGORITHMS FOR PREDICTION OF STUDENT TEACHERS’ PERFORMANCE IN ICT: A SYSTEMATIC LITERATURE REVIEW by Juma Habibu Shindo, Mohamedi Mohamedi Mjahidi, Mohamed Dewa Waziri

“…They are Naive Bayes, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Decision Tree. …”
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Nature-inspired MPPT algorithms for solar PV and fault classification using deep learning techniques by S. Senthilkumar, V. Mohan, S. P. Mangaiyarkarasi, R. Gandhi Raj, K. Kalaivani, N. Kopperundevi, M. Chinnadurai, M. Nuthal Srinivasan, L. Ramachandran

“…From the simulation results, SSOA exhibits a supreme TE of 98.38%, which is better than the other algorithms like DA, GOA, and MFOA. To further classify the faults in solar PV systems, random forest (RF), artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN) models are employed. …”
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