Design and development of an EMG controlled transfemoral prosthesis by R. Dhanush Babu, S. Siva Adithya, M. Dhanalakshmi

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Quantum Machine Learning: Recent Advances, Challenges, and Perspectives by Pradeep Lamichhane, Danda B. Rawat

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An Assessment of Land Use Land Cover Using Machine Learning Technique by V. Pushpalatha, H. N. Mahendra, A. M. Prasad, N. Sharmila, D. Mahesh Kumar, N. M. Basavaraju, G. S. Pavithra and S. Mallikarjunaswamy

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Semisupervised Location Awareness in Wireless Sensor Networks Using Laplacian Support Vector Regression by Jaehyun Yoo, H. Jin Kim

“…In this paper, we extend the standard support vector regression (SVR) to the semisupervised SVR by employing manifold regularization, which we call Laplacian SVR (LapSVR). …”
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Artificial Intelligence Dystocia Algorithm (AIDA) as a Decision Support System in Transverse Fetal Head Position by Antonio Malvasi, Lorenzo E. Malgieri, Tommaso Difonzo, Reuven Achiron, Andrea Tinelli, Giorgio Maria Baldini, Lorenzo Vasciaveo, Renata Beck, Ilenia Mappa, Giuseppe Rizzo

“…The predictive capabilities of three machine learning algorithms (Support Vector Machine, Random Forest, and Multilayer Perceptron) were assessed, and delivery outcomes were analyzed. …”
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Construction of a Multimodal Machine Learning Model for Papillary Thyroid Carcinoma Based on Pathomics and Ultrasound Radiomics DatasetMendeley Data by Yu-Yan Pang, Zhong-Qing Tang, Chang Song, Ning Qu, Jing-Yu Chen, Dan-Dan Xiong, Zhen-Bo Feng, Gang Chen

“…Three methods, as eXtreme gradient boosting (XGBoost), support vector machine (SVM), and random forest (RF) algorithms, were applied to construct PTC cytopathological diagnostic models. …”
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Hydrological and chemical characteristics of karst groundwater and carbon flux estimation by Qing Wang, Yin Zhang, Ningtao Wang, Mengru Li, Yu Zhang

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Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Phase Space Reconstruction and Improved GSA-SVM by Xiaofei XIA, Yufeng LU, Yi SU, Jian YANG

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RESEARCH ON THE REMAINING INTENSITY OF PIPELINE CORROSION BASED ON IWOA-LSSVM by ZHANG Jia, Ll LinFeng, WANG HaoJie, ZHANG Ting

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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
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Informing Disaster Recovery Through Predictive Relocation Modeling by Chao He, Da Hu

“…Leveraging data from 1304 completed interviews conducted as part of the Displaced New Orleans Residents Survey (DNORS) following Hurricane Katrina, we evaluate the performance of Logistic Regression (LR), Random Forest (RF), and Weighted Support Vector Machine (WSVM) models. Results indicate that WSVM significantly outperforms LR and RF, particularly in identifying the minority class of relocated households, achieving the highest F1 score. …”
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Physiological Signals as Predictors of Mental Workload: Evaluating Single Classifier and Ensemble Learning Models by Nailul Izzah, Auditya Purwandini Sutarto, Ade Hendi, Maslakhatul Ainiyah, Muhammad Nubli bin Abdul Wahab

“…A comprehensive evaluation was conducted on several ML algorithms, including both single (Support Vector Machine/SVM and Naïve Bayes) and ensemble learning (Gradient Boost and AdaBoost) classifiers and incorporating selected features and validation approaches. …”
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Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction by Junzhi Wen, Azim Ahmadzadeh, Manolis K. Georgoulis, Viacheslav M. Sadykov, Rafal A. Angryk

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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…The performance of the proposed model is compared with the Support Vector Machine and Random Decision algorithms and evaluated by four significant performance metrics, namely, sensitivity, specificity, accuracy, and the F-measure. …”
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Maize Kernel Broken Rate Prediction Using Machine Vision and Machine Learning Algorithms by Chenlong Fan, Wenjing Wang, Tao Cui, Ying Liu, Mengmeng Qiao

“…The <i>r</i> values of the models built by the two algorithms were 0.985 and 0.910, respectively. SVM (support vector machine) algorithms perform well in constructing maize kernel classification models, with more than 95% classification accuracy. …”
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Optimized decomposition and identification method for multiple power quality disturbances by Wang Zhaoqing, Chang Yanzhao, Chen Jianlei, Bao Weiyu

“…Subsequently, utilizing the characteristic attributes derived from IVMD, an optimized support vector machine (OSVM) algorithm is developed through the synthesis of diverse kernel functions. …”
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Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…With the following supervised machine learning algorithms: Random Forest, Artificial Neural Network (ANN) and Support Vector Regression (SVR); the study modeled RFC. …”
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Advanced Planning Systems in Production Planning Control: An Ethical and Sustainable Perspective in Fashion Sector by Martina De Giovanni, Mariangela Lazoi, Romeo Bandinelli, Virginia Fani

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Comparative study of different machine learning models in landslide susceptibility assessment: A case study of Conghua District, Guangzhou, China by Ao Zhang, Xin-wen Zhao, Xing-yuezi Zhao, Xiao-zhan Zheng, Min Zeng, Xuan Huang, Pan Wu, Tuo Jiang, Shi-chang Wang, Jun He, Yi-yong Li

“…The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), landslide models were constructed. …”
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Integration of Genetic Algorithm with Machine Learning for Properties Prediction by Rathachai Chawuthai, Siripan Murathathunyaluk, Nalin Amornratthamrong, Run Arunchaipong, Amata Anantpinijwatna

“…Consequently, ML’s predictive capabilities have been extended to encompass a broader range of properties, including Partition Coefficient, Boiling Point, and Solubility, among others, for oxygenated hydrocarbon derivatives. Algorithms such as Linear Regression, Support Vector Machine, Random Forest, and Gaussian Process are selected through trial-and-error to identify the most suitable approach. …”
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