Combined use of long short‐term memory neural network and quantum computation for hierarchical forecasting of locational marginal prices by Xin Huang, Guozhong Liu, Jiajia Huan, Shuxin Luo, Jing Qiu, Feiyan Qin, Yunxia Xu

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State of Health Estimation of Li-ion Batteries Based on GWO-LSSVM by Ju-chen LI, Yu-li HU, Jian GAO, Li-teng ZENG, Yi ZHENG, Wen-shuai DAI

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Automated painting color matching technology based on semantic intelligence understanding by Jiayin Zhang

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Rapid detection method for pork freshness using fusion spectroscopy and improved BAS-LSSVM by WANG Yao, REN Xiaozhen

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A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis by Alali Abdulla Mousa Falah, Padmaja Dhyaram Lakshmi, Soni Mukesh, Khan Muhammad Attique, Khan Faheem, Ofori Isaac

“…The support vector machine outperformed the multilayer perceptron ensembles (MLPE) neural network (NN) technique, yielding promising findings. …”
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Assessing the Impact of Mixed Pixel Proportion Training Data on SVM-Based Remote Sensing Classification: A Simulated Study by Jianyu Gu, Russell G. Congalton

“…Support vector machine (SVM) algorithms have been widely utilized in the remote sensing community due to their high performance with small training datasets. …”
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Fidex and FidexGlo: From Local Explanations to Global Explanations of Deep Models by Guido Bologna, Jean-Marc Boutay, Damian Boquete, Quentin Leblanc, Deniz Köprülü, Ludovic Pfeiffer

“…We first used FidexGlo with ensembles and support vector machines (SVMs) to show that its performance on three benchmark problems is competitive in terms of complexity, fidelity and accuracy. …”
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A New Texture Aware—Seed Demand Enhanced Simple Non-Iterative Clustering (ESNIC) Segmentation Algorithm for Efficient Land Use and Land Cover Mapping on Remote Sensing Image... by Rohini Selvaraj, D. Geraldine Bessie Amali

“…A BSTL classification approach that synergistically combines the Support Vector Machine’s ability to effectively handle high dimensional data with the k-Nearest Neighbor’s ability to handle irregular data is used. …”
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Enhancing poverty classification in developing countries through machine learning: a case study of household consumption prediction in Rwanda by Fabrice Nkurunziza, Richard Kabanda, Patrick McSharry

“…Among the 12 classifiers evaluated, multiple kernel support vector machines, eXtreme gradient boosting, and multinomial logit demonstrated the highest predictive accuracy, ranging between 86.6% and 88.5%. …”
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Two Machine-learning Hybrid Models for Predicting Type 2 Diabetes Mellitus by Rahman Farnoosh, Karlo Abnoosian, Rasha Abbas Isewid

“…Our proposed hybrid models demonstrated superior performance in two scenarios, handling and rejecting outliers, compared to other machine-learning models in this study, including support vector machines (with radial-based, polynomial, linear, and sigmoid kernel functions), decision trees (J48), and GNB classifiers for diabetes prediction. …”
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Revolutionizing Prenatal Care: Harnessing Machine Learning for Gestational Diabetes Anticipation by Sanmugasundaram Ravichandran, Hui-Kai Su, Wen-Kai Kuo, Manikandan Mahalingam, Kanimozhi Janarthanan, Bruhathi Sathyanarayanan, Kabilan Saravanan

“…Diverse algorithms were tested to compare their accuracies with the complexities of data: K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and decision tree (DT). …”
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Machine-learning-derived prediction models of outcomes for patients with pseudomyxoma peritonei: development and validation in two retrospective cohorts by Yu-Run Cui, Xin-Li Liang, Yan Li

“…Data analysis was conducted using three distinct ML algorithms: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN). …”
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Optimized hybrid SVM-RF multi-biometric framework for enhanced authentication using fingerprint, iris, and face recognition by Sonal, Ajit Singh, Chander Kant

“…The system addresses limitations of uni-modal approaches by combining multiple biometric modalities, exhibiting superior performance and heightened security in practical scenarios, making it more dependable and resilient for real-world applications. The integration of support vector machine (SVM) and random forest (RF) classifiers, along with optimization techniques like bacterial foraging optimization (BFO) and genetic algorithms (GA), improves efficiency and robustness. …”
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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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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…Given the nonlinear characteristics of supplementary cement material concrete (SCMC) mixtures, researchers are increasingly turning to machine learning methods. This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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Fault diagnosis of ZDJ7 railway point machine based on improved DCNN and SVDD classification by Zengshu Shi, Yiman Du, Xinwen Yao

“…Therefore, an improved deep convolutional neural network (DCNN) and support vector data description (SVDD) classification is proposed. …”
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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwor... by Satrio Junaidi, Rani Valicia Anggela, Delsi Kariman

“…The results of this study were obtained with the best algorithm accuracy in the support vector machine (SVM) algorithm is 0.94. …”
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Estimation of emerald mineralization probability using machine learning algorithms by Daniela Neva-Rodriguez, Luis Hernán Ochoa-Gutierrez

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Anomaly detection using machine learning and adopted digital twin concepts in radio environments by Mohamed Hussien Moharam, Omar Hany, Ahmed Hany, Amenah Mahmoud, Mariam Mohamed, Sohila Saeed

“…XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). …”
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Innovations in animal health: artificial intelligence-enhanced hematocrit analysis for rapid anemia detection in small ruminants by Aftab Siddique, Sudhanshu S. Panda, Sophia Khan, Seymone T. Dargan, Savana Lewis, India Carter, Jan A. Van Wyk, Ajit K. Mahapatra, Eric R. Morgan, Thomas H. Terrill

“…Subsequently, these images were examined in correlation with established PCV values obtained from conventional PCV analysis. Four separate machine learning models (ML) supported models, namely support vector machine (SVM), K-nearest neighbors (KNN), backpropagation neural network (BPNN), and image classification-based Keras model, were created and assessed using the image dataset. …”
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