Filter-Based Feature Selection Using Information Theory and Binary Cuckoo Optimisation Algorithm by Ali Muhammad Usman, Umi Kalsom Yusof, Maziani Sabudin

“…A support vector machine classifier was used to measure and computes the error rates of each of the datasets for both BCOA-MI and BCOA-E. …”
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Improving kinetic model fitting for total titratable acidity in bananas using genetic algorithms by Alejandro Kevin Méndez Castillo, Elizabeth Contreras López, Jesús Guadalupe Pérez Flores, Laura García Curiel, Emmanuel Pérez Escalante, Karla Soto Vega, Carlos Ángel-Jijón, Alicia Cervantes Elizarrarás

“…Future research should explore hybrid models that combine mechanistic kinetic equations with machine learning techniques (e.g., neural networks, or support vector regression), to better capture the biochemical processes underlying acidity changes in bananas. …”
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Non-Destructive Detection of External Defects in Potatoes Using Hyperspectral Imaging and Machine Learning by Ping Zhao, Xiaojian Wang, Qing Zhao, Qingbing Xu, Yiru Sun, Xiaofeng Ning

“…Then, principal component regression (PCR), support vector machine (SVM), partial least squares regression (PLSR), and least squares support vector machine (LSSVM) algorithms were used to establish quantitative models to find the most suitable preprocessing algorithm. …”
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Design of performance evaluation method for higher education reform based on adaptive fuzzy algorithm by Dakun Yang, Muhammad Sheraz Arshad Malik

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Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD... by Latifa Dwiyanti, Hidetaka Nambo, Nur Hamid

“…Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. …”
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Steiner eccentricity: Predictions and applications by Xingfu Li, Zongbo Yang, Siyi Lin, Jialu Wang

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An Accelerated Proximal Gradient Algorithm for Singly Linearly Constrained Quadratic Programs with Box Constraints by Congying Han, Mingqiang Li, Tong Zhao, Tiande Guo

“…Numerical results are reported for solving quadratic programs arising from the training of support vector machines, which show that the new algorithm is efficient.…”
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The classification of EMG signals using machine learning for the construction of a silent speech interface. by Chandrashekhar, V.

“…The SSI records EMG signals from the speech system which are then classified into speech in real-time using a trained Machine Learning model. It was found that the Support Vector Machine algorithm yielded the highest SSI accuracy of 90.1% The device created measures biomedical signals and translates them into speech accurately using a Machine Learning algorithm. …”
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Application of a Supervised Learning Machine for Accurate Prognostication of Hydrogen Contents of Bio-Oil by Binghui Xu, Tzu-Chia Chen, Danial Ahangari, S. M. Alizadeh, Marischa Elveny, Jeren Makhdoumi

“…This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. …”
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Predicting sensory evaluation of spinach freshness using machine learning model and digital images. by Kento Koyama, Marin Tanaka, Byeong-Hyo Cho, Yusaku Yoshikawa, Shige Koseki

“…Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. …”
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Research on Tool Wear Monitoring Based on ET-GD and K-nearest Neighbor Algorithm by QIN Yiyuan, LIU Xianli, YUE Caixu, GUO Bin, DING Mingna

“…The optimized features are used to train logical regression extreme random tree support vector regression and K-nearest neighbor algorithm models and verified by ten fold cross validation method and test set. …”
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Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine by Shuhan Yang, Shufang Tian

“…The experimental results show that, when compared to the Random Forest and the Support Vector Machine algorithms, the Kernel-Based Extreme Learning Machine algorithm achieved the highest accuracy and the best effect in the extraction results. …”
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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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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 Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

“…Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. …”
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Shale volume estimation using machine learning methods from the southwestern fields of Iran by Parirokh Ebrahimi, Ali Ranjbar, Yousef Kazemzadeh, Ali Akbari

“…This study aims to compare the performance of several advanced ML models—namely, Artificial Neural Networks (ANNs), Bayesian Algorithm (BA), Least Squares Boosting (Lsboost), Linear Regression (LR), Random Forest (RF), and Support Vector Machine (SVM)—for shale volume estimation using well log data. …”
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Machine Learning in Biomedical Informatics: Optimizing Resource Allocation and Energy Efficiency in Public Hospitals by Agostino Marengo, Vito Santamato, Massimo Iacoviello

“…The framework integrates several predictive models&#x2014;including Random Forest, Support Vector Machines, and Logistic Regression&#x2014;developed in Python using the scikit-learn library. …”
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Substantiation of Typical Agricultural Areas As a Methodological Basis for the Development of Regional Machine Systems by E. V. Zhalnin, V. A. Zubina

“…It was shown that when developing regional systems of technologies and machines, it is important to take into account all natural agro-climatic conditions and production capabilities in each region’s agricultural sector.Research purpose To develop a methodology for substantiating typical agricultural territories in each agricultural region so that the calculation results obtained for this territory are then generalized for the entire region.Materials and methods The production potential of the region was monitored, the basic assessment criteria were statistically processed and typical agricultural territories were selected according to the minimum deviation of the criteria particular values for the individual regions from the average weighted values for the whole region. …”
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Advancing Kidney Transplantation: A Machine Learning Approach to Enhance Donor–Recipient Matching by Nahed Alowidi, Razan Ali, Munera Sadaqah, Fatmah M. A. Naemi

“…A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. …”
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