Development of a novel sustainable, portable, fast, and non-invasive platform based on ATR-FTIR technology coupled with machine learning algorithms for Helicobacter pylori detectio... by Ghabriel Honório-Silva, Marco Guevara-Vega, Nagela Bernadelli Sousa Silva, Marcelo Augusto Garcia-Júnior, Deborah Cristina Teixeira Alves, Luiz Ricardo Goulart, Mario Machado Martins, André Luiz Oliveira, Rui Miguel Pinheiro Vitorino, Thulio Marquez Cunha, Carlos Henrique Gomes Martins, Murillo Guimarães Carneiro, Robinson Sabino-Silva

“…The obtained spectra were applied to Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to perform the H. pylori detection. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment by G Logeswari, K Thangaramya, M Selvi, J. Deepika Roselind

“…A robust feature selection subsystem, employing Synergistic Dual-Layer Feature Selection (SDFC) algorithm, combines statistical methods, such as mutual information and variance thresholding, with advanced model-based techniques, including Support Vector Machine (SVM) with Recursive Feature Elimination (RFE) and Particle Swarm Optimization (PSO) are employed to identify the most relevant features. …”
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On the Application of a Sparse Data Observers (SDOs) Outlier Detection Algorithm to Mitigate Poisoning Attacks in UltraWideBand (UWB) Line-of-Sight (LOS)/Non-Line-of-Sight (NLOS) C... by Gianmarco Baldini

“…The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model.…”
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Predicting risky driving behavior with classification algorithms: results from a large-scale field-trial and simulator experiment by Thodoris Garefalakis, Eva Michelaraki, Stella Roussou, Christos Katrakazas, Tom Brijs, George Yannis

“…To that end, four classification algorithms, namely support vector machines, random forest (RFs), AdaBoost, and multilayer perceptron (MLP) neural networks were implemented and compared. …”
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Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression by Kun Guo, Kun Guo, Bo Zhu, Lei Zha, Yuan Shao, Zhiqin Liu, Naibing Gu, Kongbo Chen

“…Six ML models were constructed: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and AdaBoost. …”
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Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM by DING Xiaofeng, ZHANG Yuhua

“…To solve the challenges related to non-stationary vibration signals in gearboxes, i.e. difficult feature extraction, high redundancy of feature vectors and low fault identification rate, this paper proposed a method of feature extraction and fault diagnosis of gearboxes based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multi-scale permutation entropy (MPE), random forest (RF) feature importance ranking and support vector machine (SVM). …”
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Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology by Hyuna Cho, Feng Tong, Sungyong You, Sungyoung Jung, Won Hwa Kim, Jayoung Kim

“…Immune phenotypes were analyzed with gene expressions using traditional but powerful classification methods such as random forests, Deep Neural Networks (DNN), Support Vector Machines (SVM) together with boosting and feature selection methods. …”
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An integrated strategy based on radiomics and quantum machine learning: diagnosis and clinical interpretation of pulmonary ground-glass nodules by Xianzhi Huang, Fangyi Xu, Wenchao Zhu, Lin Yao, Jiahuan He, Junhao Su, Wending Zhao, Hongjie Hu

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Genetic algorithm optimization of ensemble learning approach for improved land cover and land use mapping: Application to Talassemtane National Park by Ali Azedou, Aouatif Amine, Isaya Kisekka, Said Lahssini

“…Using Sentinel-2 satellite imagery processed in Google Earth Engine (GEE), six spectral features and six vegetation indices were extracted. Multiple Machine Learning (ML) classifiers including Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), Classification and Regression Tree (CART), Minimum Distance (MinD), and Gradient Tree Boost (GTB), and a Grid Search (GS)-optimized ensemble-were evaluated. …”
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Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia: further analysis of the 2011 and 2016 Ethiopian Demographic and... by Mequannent Sharew Melaku, Lamrot Yohannes, Eliyas Addisu Taye, Nebebe Demis Baykemagn

“…Furthermore, Decision Tree, Logistic Regression, Random Forest, KNN, Support Vector Machine, eXtreme Gradient Boosting (XGBoost), and AdaBoost classifiers were employed to identify the most critical predictors of khat chewing practices among men. …”
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High-Accuracy Intermittent Strabismus Screening via Wearable Eye-Tracking and AI-Enhanced Ocular Feature Analysis by Zihe Zhao, Hongbei Meng, Shangru Li, Shengbo Wang, Jiaqi Wang, Shuo Gao

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Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…The random forest (RF) and support vector machine (SVM) algorithms presented the best results, with RF showing higher performance for K40 and magnetic susceptibility, and SVM had higher performance for eU, eTh and apparent electrical conductivity. …”
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Building Information Modeling and AI Algorithms for Optimizing Energy Performance in Hot Climates: A Comparative Study of Riyadh and Dubai by Mohammad H. Mehraban, Aljawharah A. Alnaser, Samad M. E. Sepasgozar

“…To predict Energy Use Intensity (EUI), four ML algorithms, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Machine (SVM), and Lasso Regression (LR), were employed. …”
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Comparative Analysis of Classification Algorithms for Predicting Membership Churn in Fitness Centers: Case Study and Predictive Modeling at EightGym Indonesia by Dewi Lestari Mu'ti, Putri Taqwa Prasetyaningrum

“…This study aims to conduct a comparative analysis of three supervised classification algorithms Random Forest, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) to predict member churn at EightGym Indonesia. …”
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Predicting specific wear rate of laser powder bed fusion AlSi10Mg parts at elevated temperatures using machine learning regression algorithm: Unveiling of microstructural morpholog... by Vijaykumar S. Jatti, R. Murali Krishnan, A. Saiyathibrahim, V. Preethi, Suganya Priyadharshini G, Abhinav Kumar, Shubham Sharma, Saiful Islam, Dražan Kozak, Jasmina Lozanovic

“…However, to accurately predict the wear rate at high temperatures, six different machine learning regression algorithms were used, namely Support Vector Machine (SVM), Linear Regression (LR), Random Forest Regression (RFR), Gaussian Process Regression (GPR), XGBoost regression (XGB) and Decision Tree (DT). …”
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A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders by Shahid Tufail, Hasan Iqbal, Mohd Tariq, Arif I. Sarwat

“…However, reducing PCA components from 10 to 5 led to performance decreases in all models. The Support Vector Machine (SVM) Classifier shows the highest accuracy drop of 14.21%. …”
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Dynamic Workload Management System in the Public Sector: A Comparative Analysis by Konstantinos C. Giotopoulos, Dimitrios Michalopoulos, Gerasimos Vonitsanos, Dimitris Papadopoulos, Ioanna Giannoukou, Spyros Sioutas

“…Using a dataset encompassing public/private sector experience, educational history, and age, we evaluate the effectiveness of seven machine learning algorithms: Linear Regression, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Bagged Decision Trees, and XGBoost in predicting employee capability and optimizing task allocation. …”
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Detection of GenAI-produced and student-written C# code: A comparative study of classifier algorithms and code stylometry features by Adewuyi Adetayo Adegbite, Eduan Kotzé

“…The data was organised into four sets with an equal number of student-written and AI-generated code, and a machine- learning model was deployed with the four sets using six classifiers: extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), AdaBoost, random forest, and soft voting (with XGBoost, KNN and SVM as inputs). …”
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Inversion of citrus SPAD value and leaf water content by combining feature selection and ensemble learning algorithm using UAV remote sensing images by Quanshan Liu, Fei Chen, Ningbo Cui, Zongjun Wu, Xiuliang Jin, Shidan Zhu, Shouzheng Jiang, Daozhi Gong, Shunsheng Zheng, Lu Zhao, Zhihui Wang

“…Feature variable selection methods (decision tree (DT) and least absolute shrinkage and selection operator (Lasso)) were combined with Support vector machine regression (SVR), AdaBoost (Ada), SVR-AdaBoost (SVR-Ada) and WOA-SVR-Ada. …”
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