An optimized deep learning based hybrid model for prediction of daily average global solar irradiance using CNN SLSTM architecture by Yuvaraj Mariappan, Karthikeyan Ramasamy, Durgadevi Velusamy

“…By using statistical performances metrics, the predictive performance of the developed model is compared with Gated Recurrent Unit, LSTM, CNN-LSTM, SLSTM and machine learning regressor models like Support Vector Machine, Decision Tree, and Random Forest. …”
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A novel spectral transformation technique based on special functions for improved chest X-ray image classification. by Abeer Aljohani

“…The performance of these spectral moments is checked in Support vector machine and Random forest algorithm. …”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…This study utilized UAV (unmanned aerial vehicle) multispectral imagery and field-measured TN data from four key growth stages of silage corn in 2022 at Huari Ranch, Minle County, Hexi region. The support vector machine–recursive feature elimination (SVM-RFE) algorithm was applied to select vegetation indices as model inputs. …”
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Evaluating the efficacy of using large language models in preoperative prediction of microvascular invasion in HCC: a multicenter study by Zongren Ding, Jianxing Zeng, Guoxu Fang, Pengfei Guo, Weiping Zhou, Yongyi Zeng

“…The results showed that the AUC of the ChatGPT 4o was 0.755. Machine learning algorithms use Random Forest, Support Vector Machine, Logistic Regression, XGBoost and Decision Tree, the AUC of 5 machine learning algorithms was range from 0.534 to 0.624. …”
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Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  …”
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Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update by Jie Zhang, Pingping Sun, Feng Zhao, Qianru Guo, Yue Zou

“…The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.…”
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Information Collection, Analysis, and Monitoring System of Children’s Physical Training Based on Multisensor by Zhonglin Zhang, Jiajia Xu, Chenggen Peng, Yuping Chen

“…Support vector machine and decision tree are used to classify children’s different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. …”
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Feature Variable Selection Based on VIS-NIR Spectra and Soil Moisture Content Prediction Model Construction by Nan Zhou, Jin Hong, Bo Song, Shichao Wu, Yichen Wei, Tao Wang

“…To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. …”
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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…Four observed classifier algorithms have been applied: k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). …”
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Understanding students’ sentiment from feedback with a new feature selection and semantics networks by Tran Anh Tuan, Dao Thi Thanh Loan, Nichnan Kittiphattanabawon

“…In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). …”
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A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification by Guizhou Wang, Jianbo Liu, Guojin He

“…First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. …”
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A Fault Identification Method for Electric Submersible Pumps Based on DAE-SVM by Peihao Yang, Jiarui Chen, Hairong Zhang, Sheng Li

“…Additionally, a new method based on the denoising autoencoder (DAE) and support vector machine (SVM) is proposed. Firstly, the ESP production data were processed and fault-related features were screened using the random forest (RF) algorithm. …”
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Estimation of Elasticity of Porous Rock Based on Mineral Composition and Microstructure by Zaobao Liu, Jianfu Shao, Weiya Xu, Chong Shi

“…The results show that ECS exhibits linear relations with the rock minerals, pores, and applied compressive stress. Then the support vector machine (SVM) optimized by the particle swarm optimization algorithm (PSO) is examined to generate estimations of the ECS based on the mineral composition and microstructures. …”
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Speech Emotion Recognition Using a Multi-Time-Scale Approach to Feature Aggregation and an Ensemble of SVM Classifiers by Antonina STEFANOWSKA, Sławomir Krzysztof ZIELIŃSKI

“…The features aggregated at different time windows are subsequently classified by an ensemble of support vector machine (SVM) classifiers. To enhance the generalization property of the method, a data augmentation technique based on pitch shifting and time stretching is applied. …”
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Self-Healing of Active Distribution Networks by Accurate Fault Detection, Classification, and Location by Sally El-Tawab, Hassan S. Mohamed, Amr Refky, A. M. Abdel-Aziz

“…The fault location is achieved by integrating DWT and support vector machine (SVM). The data for training were extracted using DWT and collected, and then SVM was trained to locate the faulted section. …”
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Development of High Accuracy Classifier for the Speaker Recognition System by Raghad Tariq Al-Hassani, Dogu Cagdas Atilla, Çağatay Aydin

“…However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K-nearest neighbour (KNN), and support vector machine (SVM).…”
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A feasibility study of an improved procedure for using EEG to detect brain responses to imagery instruction in patients with disorders of consciousness. by Anna Lisa Mangia, Marco Pirini, Laura Simoncini, Angelo Cappello

“…Five healthy subjects and five patients with different disorders of consciousness took part in the study. A support vector machine classifier applied to EEG data was used to distinguish two mental tasks (Imagery Trial) and to detect answers to simple yes or no questions (pre-Communication Trial). …”
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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…The hybrid BPSO-BWAO method emerged as the optimal solution, achieving an MSE of 53.93 with improved stability and feature set balance, selecting key features such as ‘Days with AQI,’ ‘Median AQI,’ ‘Days CO,’ ‘Days NO2,’ ‘Days PM2.5,’ ‘Good_Days_Percent,’ and ‘Unhealthy_Days_Percent.’ Machine learning models, including Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Linear Regression (LR), were evaluated before and after feature selection. …”
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A secure IoT-edge architecture with data-driven AI techniques for early detection of cyber threats in healthcare by Mamta Kumari, Mahendra Gaikwad, Salim A. Chavan

“…The investigation makes use of an eight variety of machine learning models, including feedforward neural networks, gradient boosting machines (XGBoost and LightGBM), logistic regression, VAEs, random forests, along with support vector machines (SVM). …”
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Development and validation of MRI-based radiomics model for clinical symptom stratification of extrinsic adenomyosis by Man Sun, Jianzhang Wang, Ping Xu, Libo Zhu, Gen Zou, Shuyi Chen, Yuanmeng Liu, Xinmei Zhang

“…Radiomic features were extracted from MRI-T2 image. Random forest algorithm was used to select the key radiomics features of different symptoms and develop the radiomic model by support vector machine algorithm. …”
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