Back analysis of geomechanical parameters based on a data augmentation algorithm and machine learning technique by Hui Li, Weizhong Chen, Xianjun Tan

“…Subsequently, we harness the power of optimized particle swarm optimization (OPSO) integrated with support vector machine (SVM) to mine the intricate nonlinear relationships between input and output variables. …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…The proposed method takes the calculated maximum runout distance obtained from the empirical model as supplementary inputs to increase the amount of training data to construct hybrid machine-learning models. Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…The data was randomly divided into training set and testing set, and feature selection was performed using the Boruta algorithm and SHAP algorithm. Logistic regression (LR), random forest (RF), support vector machine (SVM), and gradient boosting decision tree (GBDT) were constructed, and model performances were evaluated using accuracy, precision, recall, area under curve(AUC) of the receiver operating characteristic curve, and F1-score.Results A total of 856 perimenopausal women were included in the study, of which 557 were in the normal or mild PMS group and 299 were in the moderate to severe PMS group; 599 were in the training set and 257 were in the testing set. 9 features (employment status, exercise, age, menstrual condition, medical history, obesity, residence area, history of health education, household register) were selected as predictors for the final model using the Boruta algorithm and SHAP analysis. …”
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CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms by Milad Nouri, Shadman Veysi

“…Apart from cluster I, where the Support Vector Machine outperformed, the Random Forest technique provided more accurate ETo predictions. …”
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Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning by Samuel Nashed, Rouzbeh Moghanloo

“…For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. …”
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Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria by Zinabu Bekele Tadese, Teshome Demis Nimani, Kusse Urmale Mare, Fetlework Gubena, Ismail Garba Wali, Jamilu Sani

“…Data was thoroughly assessed for missingness and weighted to draw valid inferences. Six machine learning algorithms, namely, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting, were employed on a total sample size of 37,581 in Python 3.9 version. …”
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A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx by Akella S Narasimha Raju, K Venkatesh, Makineedi Rajababu, Ranjith Kumar Gatla, Marwa M. Eid, Enas Ali, Nataliia Titova, Ahmed B. Abou Sharaf

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Comparison of artificial intelligence approaches for estimating wind energy production: A real-world case study by Mohamed Bousla, Mohamed Belfkir, Ali Haddi, Youness El Mourabit, Badre Bossoufi

“…The present work investigates several forecasting methodologies for wind energy by employing sophisticated machine learning algorithms, including Support Vector Machines and Recurrent Neural Networks. …”
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Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms by Yuefei Zhou, Jinghan Wang, Zengjing Song, Miaohang Zhou, Mengnan Lv, Xujun Han

“…Four states were used to train models based on XGBoost, random forest (RF), LightGBM, and support vector machine (SVM), while the remaining four states served for validation. …”
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Inflammatory Gene Signature Identified by Machine Algorithms Reveals Novel Biomarkers of Coronary Artery Disease by Liu X, Zhang Y, Wang Y, Xu Y, Xia W, Liu R, Xu S

“…Four biomarkers (ADM, NUPR1, PTGER1, and PYDC2) were identified using Support Vector Machine (SVM). Ten types of immune cells, including CD8+ T cells, regulatory T cells (Tregs), and resting NK cells, showed significant differences between the CAD and normal groups. …”
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Ｒecognition of Instruments’Sounds Based on VMD and PSO by HUANG Ying-lai, ＲEN Tian-li, ZHAO Peng

“…Proposing the method that based on the variational mode decomposition ( VMD) and particle swarm optimization ( PSO) optimized support vector machine ( SVM) are used to recognize the audio signals of the musical instruments aiming at the problem of the low recognition rate of musical instruments audio signals． …”
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Comprehensive Evaluation Method for Complex Fluid in Low Porosity and Low Permeability Reservoirs by ZHENG Yang, LU Yunlong, SHI Xinlei, ZHU Meng

“…Based on the four types of parameters mentioned above, a fluid discrimination model for oil, gas, and water layers is established using support vector machine algorithm, with 70 samples of oil, gas, and water layers confirmed through testing, pressure measurement, and sampling as data samples, including 51 samples as training samples and 19 samples as testing samples. …”
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Predicting Earthquake Casualties and Emergency Supplies Needs Based on PCA-BO-SVM by Fuyu Wang, Huiying Xu, Huifen Ye, Yan Li, Yibo Wang

“…In order to address challenges such as the large computational workload, tedious training process, and multiple influencing factors associated with predicting earthquake casualties, this study proposes a Support Vector Machine (SVM) model utilizing Principal Component Analysis (PCA) and Bayesian Optimization (BO). …”
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Analysis of the Impact and Weight of Structural Parameters on the Operating Characteristics of the Spring Operating Mechanism by Qi Long, Xu Yang, Keru Jiang, Changhong Zhang, Xiao Wang, Xiongying Duan

“…Third, a reliability model of the spring-operated mechanism was established, and a limit state function based on closing time was determined. By integrating the support vector machine surrogate model with the Monte Carlo reliability analysis method, the failure probability of the mechanism was determined, along with the specific influence weights of structural parameters on the operational characteristics of the spring-operated mechanism. …”
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Feasibility Analysis of Tamura Features in the Identification of Machined Surface Images Using Machine Learning and Image Processing Techniques by Raghavendra C. Kamath, G. S. Vijay, Ganesha Prasad, P. Krishnananda Rao, Uday Kumar Shetty, Gautham Parameshwaran, Aniket Shenoy, Prithvi Shetty

“…Then, Tamura features are extracted and are given as input to ANN, support vector machines (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF). …”
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Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine by Yanbin Wang, Zhuhong You, Liping Li, Li Cheng, Xi Zhou, Libo Zhang, Xiao Li, Tonghai Jiang

“…To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method. …”
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Developing a Model to Predict the Effectiveness of Vaccination on Mortality Caused by COVID-19 by Malihe Niksirat, Javad Tayyebi, Seyedeh Fatemeh Javadi, Adrian Marius Deaconu

“…This study explores the application of various ML techniques, including artificial neural network (ANN), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) to model and forecast the impact of vaccination on COVID-19 mortality. …”
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Utility of an artificial intelligence-based lung CT airway model in the quantitative evaluation of large and small airway lesions in patients with chronic obstructive pulmonary dis... by Zheng Liu, Jing Li, Bo Li, Guozhen Yi, Shaoqian Pang, Ruinan Zhang, Peixiu Li, Zhaoping Yin, Jing Zhang, Bingxin Lv, Jingjing Yan, Jiao Ma

“…An AI-driven airway segmentation model was constructed using Convolutional Neural Regressor (CNR) and Airway Transfer Network (ATN) algorithms. The efficacy of the model was evaluated through support vector machine (SVM) and random forest regression approaches. …”
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Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang, Zhiqian Qi

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The method of increasing the efficiency of the man-machine system functioning by improving the quality of decision support system software by О.В. Турінський, М.А. Павленко, Г.В. Пєвцов, С.В. Осієвський

“…The article addresses improving the performance of man-machine systems (MMS) by improving the quality of decision support system software (DSSS). …”
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