Wearable Internet-of-Things platform for human activity recognition and health care by Asif Iqbal, Farman Ullah, Hafeez Anwar, Ata Ur Rehman, Kiran Shah, Ayesha Baig, Sajid Ali, Sangjo Yoo, Kyung Sup Kwak

“…On the given data set, we evaluate a number of widely known classifiers such random forests, support vector machine, and many others using the WEKA machine learning suite. …”
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Wavelet filterbank‐based EEG rhythm‐specific spatial features for covert speech classification by Sukanya Biswas, Rohit Sinha

“…Radial basis function kernel‐based support vector machines are used for covert speech classification. …”
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Preliminary study: Data analytics for predicting medication adherence in Malaysian arthritis patients by Firdaus Aziz, Shubathira Sooriamoorthy, Bryan Liew, Sharifah M. Syed Ahmad, Wei Wen Chong, Sorayya Malek, Adliah Mhd Ali

“…Support vector machine, gradient boosting, and random forest models performed best with AUC values of 0.907, 0.775, and 0.632 utilizing all variables. …”
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Prediction of HIV status based on socio-behavioural characteristics in East and Southern Africa. by Erol Orel, Rachel Esra, Janne Estill, Amaury Thiabaud, Stéphane Marchand-Maillet, Aziza Merzouki, Olivia Keiser

“…<h4>Methods</h4>We analysed the most recent Demographic and Health Survey from these 10 countries to predict individual's HIV status using four different algorithms (a penalized logistic regression, a generalized additive model, a support vector machine, and a gradient boosting trees). …”
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Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification by Achmad Rizal, Sugondo Hadiyoso

“…In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.…”
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Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. …”
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Decision-making power enhances investors’ neural processing of persuasive message in partnership investment by Jianbiao Li, Peikun Chen, Jingjing Pan, Chengkang Zhu

“…Furthermore, using a support vector machine (SVM) algorithm, the INS could accurately predict receivers’ adoption of persuasive messages when they held dominant decision-making power. …”
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Land-cover classification in Addo Elephant National Park: Analyzing the impact of variables, classifiers, and object-based approach by Mohammad Safaei, Jane Southworth, Cerian Gibbes, Hannah V. Herrero, Mashoukur Rahaman, Bewuket B. Tefera, Jason K. Blackburn

“…Object-based classification was compared with a pixel-based approach, revealing the superior performance of the pixel-based approach. Two machine learning (ML) techniques, Random Forest (RF) and Support Vector Machines (SVM), were compared with a deep learning (DL) technique, UNet++. …”
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Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning by Abdul Razzaq, Sharaiz Shahid, Muhammad Akram, Muhammad Ashraf, Shahid Iqbal, Aamir Hussain, M. Azam Zia, Sulman Qadri, Najia Saher, Faisal Shahzad, Ali Nawaz Shah, Aziz-ur Rehman, Sven-Erik Jacobsen

“…The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.…”
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Experimental Study on Evaluation of Organization Collaboration in Prefabricated Building Construction by Dingjing Bao, Yuan Chen, Shuai Wan, Jinlai Lian, Ying Lei, Kaizhe Chen

“…Moreover, the BO-XGBoost model was compared with the random forest, support vector machine, and logistic regression prediction models. …”
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Potentiality of Landsat-9 for early-season mapping of winter garlic and winter wheat by Haifeng Tian, Mengdan Yang, Fangli Wu, Yaochen Qin, Xiwang Zhang, Jiayi Liu, Weiyang Yan

“…Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. …”
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The Development Path and Carbon-Reduction Method of Low-Carbon Pilot Urban Areas in China by Lining Zhou, Qingqin Wang, Haizhu Zhou, Yiqiang Jiang, Rongxin Yin, Tong Lu

“…At the same time, after comparing models, such as random forest and support vector machine, the XGBoost algorithm is adopted for short-term prediction (R² = 0.984, MAE = 0.195). …”
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Recognition of Symbolic Gestures Using Depth Information by Hasan Mahmud, Md. Kamrul Hasan, Abdullah-Al-Tariq, Md. Hasanul Kabir, M. A. Mottalib

“…These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. …”
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Research on green supply chain finance risk identification based on two-stage deep learning by Ying Liu, Sizhe Li, Chunmei Yu, Mingli Lv

“…Finally, to model low-order feature interactions, we integrate the Support Vector Machine (SVM) algorithm. The paper is grounded in the green innovation production of enterprises, collecting financial data of 176 upstream and downstream enterprises and corresponding core enterprise green indicators from 2013 to 2022. …”
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Synergistic effect of artificial intelligence and new real-time disassembly sensors: Overcoming limitations and expanding application scope by Bozhou Li, Dajiang Ju, Xingwang Li, Yan Liu, Hongru He, Hao Wang

“…Then, based on the gated recurrent unit (GRU) model, the article applied the particle swarm optimization (PSO) algorithm to optimize the parameters of the GRU network and used the support vector machine (SVM) model to optimize the classification function of the network output. …”
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Signatures of Six Autophagy‐Related Genes as Diagnostic Markers of Thyroid‐Associated Ophthalmopathy and Their Correlation With Immune Infiltration by Qintao Ma, Yuanping Hai, Jie Shen

“…Gene ontology analysis (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to perform the enrichment analysis of AR‐DEGs. LASSO regression, support vector machine recursive feature elimination, and random forest were performed to screen for disease signature genes (DSGs), which were further validated in another independent validation dataset. …”
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Texture Analysis of T2-Weighted Images as Reliable Biomarker of Chronic Kidney Disease Microstructural State by Marcin Majos, Artur Klepaczko, Katarzyna Szychowska, Ludomir Stefanczyk, Ilona Kurnatowska

“…The T2-weighted MRI images were analyzed using a Support Vector Machine (SVM) model created with qMazDa software, which was trained to classify images into the appropriate group of CKD activity. …”
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Combination of skin sympathetic nerve activity and urine biomarkers in improving diagnostic accuracy for urge urinary incontinence by Yu-Chen Chen, Hao-Wei Chen, Tzu-Yu Liu, Yung-Shun Juan, Yu-Peng Liu, Shiou-Lan Chen, Chien-Hung Lee, Wei-Chung Tsai, Wen-Jeng Wu

“…Logistic regression and support vector machine with L1 penalty were applied to SKNA and urine biomarker measurements. …”
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Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples. by Fatma Alamri, Imad Barsoum, Shrinivas Bojanampati, Maher Maalouf

“…This work compares five supervised machine learning algorithms, including artificial neural networks, support vector regression, kernel ridge regression, random forest, and Lasso regression. …”
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