Non-intrusive Load Decomposition Model Based on Deep Fusion of Multi-modal Integration by YAO Gang, WANG Yun, WANG Yuanliang, SONG Zihao

“…Firstly, the CNN-LSTM decomposition model was enhanced in the time-dependent modeling capacity by the temporal pattern attention (TPA) mechanism, to capture the load characteristics of the original electricity consumption information and conduct preliminary load decomposition. Secondly, the support vector regression (SVR) was used to model the nonlinear state space of the target device and the cubature Kalman filter (CKF) algorithm was modified by the improved tracking technology and variational Bayesian to create a VB-STCKF model for secondary dynamic adjustment to the preliminary decomposition results. …”
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Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea by Lifeng Li, Qimeng Shi, Bowen Fang, Yuting Liu, Xiang Liu, Yongqiang Shu, Yingke Deng, Yumeng Liu, Haijun Li, Junjie Zhou, Dechang Peng

“…Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. …”
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Predicting Movie Production Years through Facial Recognition of Actors with Machine Learning by Asraa Muayed Abdalah, Noor Redha Alkazaz

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Proposing a framework for body mass prediction with point clouds: A study applied in typical swine pen environments by Gabriel Pagin, Luciane Silva Martello, Rubens André Tabile, Rafael Vieira de Sousa

“…Subsequently, machine learning models (Random Tree - RT, Random Forest - RF, Linear Regression - LR, K-Nearest Neighbors - KNN, Support Vector Regression - SVR, and Multilayer Perceptron - MLP) were trained, optimized, and evaluated using k-fold cross-validation, followed by statistical analysis. …”
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Soft-sensor modeling of silicon content in hot metal based on sparse robust LS-SVR and multi-objective optimization by GUO Dong-wei, ZHOU Ping

“…Next, in view of the problem that the standard least squares support vector machine has no regularization term, a method to improve the modeling ro-bustness was proposed by introducing the IGGⅢ weighting function into the obtained sparse least squares support vector regression (S-LS-SVR) model. …”
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Hybrid optimization of thermally-enhanced Zn-Fe LDH catalysts for fenton-like reactions: Integrating design of experiments with machine learning models for optimisation by Ramadhan Muhammad Naufal, Nawwal Hikmah, Dessy Ariyanti

“…This study presents a novel hybrid modeling framework that combines Response Surface Methodology (RSM) with machine learning (ML) algorithms– Support Vector Regression (SVR) and Gradient Boosting Regression (GBR)– to contribute to the predictive modeling and optimization of thermally-activated ZnFe-LDH based Fenton catalysis. …”
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ANALISIS KINERJA MODEL STACKING BERBASIS RANDOM FOREST DAN SVM DALAM KLASIFIKASI RUMAH TANGGA BERDASARKAN GARIS KEMISKINAN MAKANAN DI PROVINSI JAWA BARAT by Ghardapaty Ghaly Ghiffary, Nabila Tri Amanda, Rizky Ardhani, Bagus Sartono, Aulia Rizki Firdawanti

“…This research applies the stacking method with two machine learning algorithms, namely Random Forest and Support Vector Machine (SVM) as base learners and logistic regression as a meta learner. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Among the machine learning models evaluated, artificial neural networks and support vector machines demonstrated superior predictive performance, achieving a coefficient of determination (R²) above 0.77 and lower root mean square error (RMSE) values (less than 0.14). …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…Methods This study was a secondary analysis of a recently published clinical trial that recruited 1,070 children aged 3- to 4-year-old with ECC. Machine learning algorithms including Naive Bayes, logistic regression, decision tree, random forest, support vector machine, and extreme gradient boosting were adopted to predict the caries-arresting outcome of ECC at 30-month follow-up after receiving fluoride and silver therapy. …”
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Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation by Ahmad J. Obaidullah, Wael A. Mahdi

“…Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)—are evaluated. …”
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Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis by Huan-Zhong Su, Dao-Hui Yang, Long-Cheng Hong, Yu-Hui Wu, Kun Yu, Zuo-Bing Zhang, Xiao-Dong Zhang

“…The AUCs ranged from 0.575 to 0.827 for the nine models. The support vector machine (SVM) algorithm achieved the highest performance with an AUC of 0.827, accompanied by an accuracy of 0.798, precision of 0.792, recall of 0.862, and an F1 score of 0.826. …”
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Integrated Forecast of Monthly Saltwater Intrusion at Modaomen Waterway Based on Multiple Models by LU Pengyu, LIN Kairong, YANG Yugui, YUAN Fei, HE Yong

“…This paper builds the regression model by Random Forest (RF) algorithm, Support Vector Machine (SVM) and Elman Neural Network (ENN), and conducts a monthly integrated forecast through Bayesian Model Averaging (BMA) method. …”
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SVR Data-Driven Optimization of Generator Leading Phase Operation Limit by Dengfeng LI, Mincai YANG, Yuming LIU, Ruilin XU, Xia YU, Zhaojiong LI

“…In view of the difficulty in modeling the mechanism caused by the complex and strong coupling nonlinearities between the multiple variables in the limiting conditions of leading phase operation, a novel method is proposed in this paper to optimize the leading phase operation limit of generator based on data-driven support vector machine regression (SVR). The limit calculation of generator leading phase operation is converted to the minimization of reactive power subject to the multiple constraints of leading phase. …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…In this work, multiple machine learning models, including linear regression (LR), support vector machine (SVM), decision tree regressor (DTR), random forest regressor (RFR), and multilayer perceptron (MLP), are used to predict the IFT of the CO2 and aqueous solution of NaCl. …”
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Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients by Tuğba Şentürk, Fatma Latifoğlu, Çiğdem Gülüzar Altıntop, Arzu Yay, Zeynep Burçin Gönen, Gözde Özge Önder, Özge Cengiz Mat, Yusuf Özkul

“…A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. Classification was performed using ML algorithms, including decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and Artificial Neural Network (ANN). …”
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Does machine learning outperform logistic regression in predicting individual tree mortality? by Aitor Vázquez-Veloso, Astor Toraño Caicoya, Felipe Bravo, Peter Biber, Enno Uhl, Hans Pretzsch

“…Here, we compare the performance of five different ML algorithms (Decision Trees, Random Forest, Naive Bayes, K-Nearest Neighbour, and Support Vector Machine) against Logistic binomial Regression in individual tree mortality classification under 40 different case studies and a cross-validation case study. …”
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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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Analysis of machine learning approaches for the interpretation of acoustic fields obtained by well noise data modelling by N. V. Mutovkin

“…In order to build the best model, machine learning approaches such as linear regression with different variants of regularisation, Bayesian regression, neural net, methods of supporting vectors, decision tree, random forest and gradient boosting are considered. …”
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UHVDC Transmission Line Fault Identification Method Based on Generalized Regression Neural Network by XIE Jia, LIU Feng, KE Yanguo, YIN Zhen, RUAN Wei, YAO Jinming

“…Compared to traditional convolutional neural networks, generalized regression neural networks, support vector machines, and other methods, the fault recognition accuracy of the proposed method in this paper has been improved by 6. 6% , 0. 65% , and 7. 69% , respectively, meeting the requirements of protection speed and reliability.…”
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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
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