Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…Data points, including x and y coordinates and corresponding solute concentrations (C), were generated through CFD simulations by solving mass transfer equations under varying conditions. Three supervised regression models of Kernel Ridge Regression (KRR), Decision Tree Regression (DT), and Radial Basis Function Support Vector Machine (RBF-SVM) were developed to map spatial coordinates to solute concentrations. …”
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Deployment and Operation of Battery Swapping Stations for Electric Two-Wheelers Based on Machine Learning by Yu Feng, Xiaochun Lu

“…Then, on a 3000 m grid scale, a prediction model of BSS quantity with random forest, support vector regression, and gradient-boosting decision tree algorithm was built. …”
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A New Hybrid Model for Underwater Acoustic Signal Prediction by Guohui Li, Wanni Chang, Hong Yang

“…Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. …”
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Prediction of water inrush from coal seam floor based on machine learning with small sample data by Chenxi LI, Haifeng LU

“…The influence rule of sample number on prediction accuracy was discussed, and the comparison study was conducted with the commonly used particle swarm, support vector machine, BP neural network, random forest and convolutional neural network. …”
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Transient stability assessment of wind turbine grid-connected systems using ISSA-MSVR by FAN Hong, XU Yongjie, XU Tao

“…To address the issue of inaccurate transient stability analysis in power systems after wind farm integration， this paper proposes a method combining improved sparrow search algorithm （ISSA）-optimized multi-output support vector regression （MSVR）. …”
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Stability Analysis and Construction Parameter Optimization of Tunnels in the Fractured Zone of Faults by Banma Huang, Haibo Chen, Chenglong Duan, Wenhu Li

“…The results show that the proposed optimization model of drilling and blasting construction parameters for highway tunnel faults based on the Support Vector Regression (SVR) algorithm combined with a genetic algorithm (GA) has a short calculation time and high parameter optimization accuracy. …”
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Research on measurement of supply chain finance credit risk based on Internet of Things by Waseem Ahmed Abbasi, Zongrun Wang, Yanju Zhou, Shahzad Hassan

“…Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. …”
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FEATURE EXTRACTION AND ESTABLISHMENT BASED ON PUMPING UNIT WORKING CONDITIONS AND GLOBAL FAULT IDENTIFICATION by REN Taizhu, FAN Jun, JIANG Xiaxin

“…Then, two methods of obtaining valve opening and closing points and load variation characteristics were proposed, and 54 new features of global faults of pumping units were extracted, and the characteristic database of working conditions of the pumping unit was established.Finally, the algorithm of decision tree, logistic regression and support vector machine was used to verify that the feature database has good classification effect under different working conditions. …”
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Enhanced detection of accounting fraud using a CNN-LSTM-Attention model optimized by Sparrow search by Peifeng Wu, Yaqiang Chen

“…The detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activities. …”
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Machine learning-enabled prediction of bone metastasis in esophageal cancer by Liqiang Liu, Wanshi Duan, Tao She, Shouzheng Ma, Haihui Wang, Jiakuan Chen

“…Six machine learning models were constructed: Support Vector Machine, Logistic Regression, Extreme Gradient Boosting, Neural Network, Random Forest, and k-Nearest Neighbors. …”
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Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform by Jianhui Wu, Lu Zhang, Sufeng Yin, Haidong Wang, Guoli Wang, Juxiang Yuan

“…Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. …”
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Prediction of Cover–Subsidence Sinkhole Volume Using Fibre Bragg Grating Strain Sensor Data by Wesley B. Richardson, Suné von Solms, Johan Meyer, Charis Harley

“…Weighted Least Squares regression, Support Vector Regression, and eXtreme Gradient Boosting were implemented on the data during phase two of the cover–subsidence sinkhole formation to determine the volume of the sinkhole. …”
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Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…The findings demonstrate short-wave-near-infrared hyperspectral imaging combined with linear models (logistic regression and Support Vector Machine with linear kernel parameter settings) is better for identifying the types of jerky. …”
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Artificial liver classifier: a new alternative to conventional machine learning models by Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid

“…The results demonstrate competitive performance, with ALC achieving up to 100% accuracy on the Iris dataset–surpassing logistic regression, multilayer perceptron, and support vector machine–and 99.12% accuracy on the Breast Cancer dataset, outperforming XGBoost and logistic regression. …”
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Rapid and Accurate Measurement of Major Soybean Components Using Near-Infrared Spectroscopy by Chenxiao Li, Jiatong Yu, Sheng Wang, Qinglong Zhao, Qian Song, Yanlei Xu

“…Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). …”
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Deploying machine learning for long-term road pavement moisture prediction: A case study from Queensland, Australia by Ayesh Dushmantha, Ruixuan Zhang, Yilin Gui, Jinjiang Zhong, Chaminda Gallage

“…Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. …”
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Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol by Qunling Luo, Zhiqiang Guo, Danping Lin, Boxue Chang, Yinlan Ruan

“…The modeling performances of three algorithms including Support Vector Machine Regression (SVMR), Principal Component Regression (PCR), and Partial Least Squares Regression (PLSR) were systematically evaluated, with PLSR identified as the optimal algorithm. …”
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An assessment of the long-term change of the Mersin west coastline using digital shoreline analysis system and detection of pattern similarity using fuzzy C-means clustering by Ozcan Zorlu, Lutfiye Kusak

“…The Google Earth Engine (GEE) platform facilitated data acquisition, classification, and edge detection. A Support Vector Machine (SVM) classification algorithm was applied to distinguish land from water. …”
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Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. …”
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