Unveiling global flood hotspots: Optimized machine learning techniques for enhanced flood susceptibility modeling by Mahdi Panahi, Khabat Khosravi, Fatemeh Rezaie, Zahra Kalantari, Jeong-A. Lee

“…Flood susceptibility mapping is a cost-effective tool to mitigate and manage the impacts of flood occurrences, but high accuracy in mapping is important to support management strategies. This study assessed the efficiency of three machine learning approaches, including support vector regression (SVR) and its optimized versions through combination with grey wolf optimizer (GWO) and whale optimization algorithm (WOA), in generating accurate flood susceptibility maps at a global scale. …”
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Machine learning approach for prediction of safe mud window based on geochemical drilling log data by Hongchen Cai, Yunliang Yu, Yingchun Liu, Xiangwei Gao

“…Traditional geomechanical methods for SMW determination face challenges in handling complex, nonlinear relationships within drilling datasets.PurposeThis study aims to develop robust machine learning (ML) models to predict two key SMW parameters—Mud Pressure below shear failure (MWsf) and tensile failure (MWtf)—using geochemical drilling log data from Middle Eastern carbonate reservoirs.MethodsHybrid ML models combining Least Squares Support Vector Machine (LSSVM) and Multilayer Perceptron (MLP) with optimization algorithms (Gray Wolf Optimization, GWO; Grasshopper Optimization Algorithm, GOA) were trained on 2,820 data points from three wells. …”
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A Machine Learning-Based Risk Prediction Model During Pregnancy in Low-Resource Settings by Kapil Tomar, Chandra Mani Sharma, Tanisha Prasad, Vijayaraghavan M. Chariar

“…The Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) algorithms, with 10-fold cross validation, are used in this study. …”
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Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors by Bo Wu, Songlin Feng, Guodong Sun, Liang Xu, Chenghan Ai

“…In this paper, an algorithm based on two novel shape descriptors and support vector machine (SVM) is proposed to improve the recognition accuracy and speed of shaft orbits of rotating machines. …”
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Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases by Wanshan Ning, Zhicheng Wang, Ying Gu, Lindan Huang, Shuai Liu, Qun Chen, Yunyun Yang, Guolin Hong

“…We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. …”
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Intelligent identification of electromagnetic radiation signals induced by coal rock fractures using machine learning by LI Baolin, FENG Jiaqi, WANG Enyuan, SUN Xinyu, WANG Shuowei

“…This study conducted monitoring experiments on electromagnetic radiation during uniaxial compression of coal rock, analyzing the time-domain, frequency-domain, and fractal characteristics of both valid and interference signals. Machine learning algorithms, such as linear discriminant analysis, support vector machines, and ensemble learning methods, were utilized to develop intelligent identification models for effective and interference signals. …”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). …”
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Identification of Energy Metabolism-Related Subtypes and Diagnostic Biomarkers for Osteoarthritis by Integrating Bioinformatics and Machine Learning by Xu S, Ye J, Cai X

“…Unsupervised clustering was performed to classify molecular subtypes. Three machine learning algorithms were employed to identify key diagnosis genes, specifically Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF). …”
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Exploiting heart rate variability for driver drowsiness detection using wearable sensors and machine learning by Zakwan AlArnaout, Chamseddine Zaki, Yehia Kotb, Mouhammad AlAkkoumi, Nour Mostafa

“…Our results demonstrate that the Random Forest (RF) classifier achieves the highest testing accuracy (86.05%), precision (87.16%), recall (93.61%), and F1-score (89.02%) with the smallest mean change between training and testing datasets (-4.30%), highlighting its robustness for real-world deployment. The Support Vector Machine with Radial Basis Function (SVM-RBF) also shows strong generalization performance, with a testing F1-score of 87.15% and the smallest mean change of -3.97%. …”
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Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment by Sheng Hu, Junyu Liu, Jiayi Hong, Yuting Chen, Ziwen Wang, Jibo Hu, Shiying Gai, Xiaochao Yu, Jingjing Fu

“…The interpretability and predictor importance of the model were analyzed using Shapley additive explanations.ResultsOf the 159 patients, 100 (62.9%) exhibited HT. The support vector machine (SVM) was the optimal ML algorithm for constructing the models. …”
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Replicating Current Procedural Terminology code assignment of rhinology operative notes using machine learning by Christopher P. Cheng, Ryan Sicard, Dragan Vujovic, Vikram Vasan, Chris Choi, David K. Lerner, Alfred‐Marc Iloreta

“…The Decision Tree, Support Vector Machine, Logistic Regression and Naïve Bayes (NB) algorithms were used to train and test models on operative notes. …”
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Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning by Jiao Li, Yupei Liu, Zhiyi Sun, Suqi Zeng, Caisong Zheng

“…To identify potential diagnostic biomarkers, we applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE) model, and Random Forest (RF) algorithm, and constructed Receiver Operating Characteristic (ROC) curves to evaluate accuracy. …”
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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms &#x2014; Chi-squared (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) &#x2014; and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…Core genes in key modules were screened using LASSO regression and support vector machine recursive feature elimination (SVM-RFE). …”
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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. …”
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Depression Analysis and Detection Using Machine Learning: Incorporating Gender Differences in a Comparative Study by Marina Galanina, Anna Rekiel, Anna BaCzyk, Bozena Kostek

“…The proposed approach performs spectrogram analysis and utilizes several machine learning methods, including SVM (Support Vector Machine), Random Forest, MLP (Multilayer Perceptron), and DepAudioNet. …”
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Novel Hybrid Feature Selection Using Binary Portia Spider Optimization Algorithm and Fast mRMR by Bibhuprasad Sahu, Amrutanshu Panigrahi, Abhilash Pati, Manmath Nath Das, Prince Jain, Ghanashyam Sahoo, Haipeng Liu

“…<b>Methods:</b> This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. …”
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Predicting bearing capacity of gently inclined bauxite pillar based on numerical simulation and machine learning by Deyu WANG, Defu ZHU, Biaobiao YU, Chen WANG

“…Support Vector Machine (SVM), Extreme Learning Machine (ELM) and Light Gradient Boosting Machine (LightGBM) were used to construct the model for predicting the strength of gently inclined pillars. …”
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Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy by Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin

“…Random Oversampling, SMOTE, and ADASYN are employed to address data imbalance and improve model robustness. Nine machine learning algorithms, including Decision Tree, Random Forest, XGBoost, LightGBM, Gradient Boosting, Support Vector Machine, AdaBoost, and Logistic Regression, are tested for classification. …”
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The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review by Norah Hamad Alhumaidi, Doni Dermawan, Hanin Farhana Kamaruzaman, Nasser Alotaiq

“…For example, random forest models for cardiovascular disease prediction demonstrated an area under the curve of 0.85 (95% CI 0.81-0.89), while support vector machine models for cancer prognosis achieved an accuracy of 83% (P=.04). …”
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