Prediction of R1234yf flow boiling behavior in horizontal, vertical, and inclined tubes using machine learning techniques by Farzaneh Abolhasani, Behrang Sajadi, Mohammad Ali Akhavan-Behabadi

“…A total of 339 experimental data points sourced from the literature are employed to develop and train four methods of MLAs, including the multi-layer perceptron (MLP) neural network, support vector regression (SVR), random forest, and adaptive boosting (AdaBoost). …”
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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. …”
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A comparative study of hybrid adaptive neuro-fuzzy inference systems to predict the unconfined compressive strength of rocks by Wei Cao

“…Hybrid models included support vector regression (SVR) combined with the Seahorse Optimizer (SVSH) and SVR combined with the COOT optimization algorithm (SVCO). …”
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Extreme high accuracy prediction and design of Fe-C-Cr-Mn-Si steel using machine learning by Hao Wu, Jianyuan Zhang, Jintao Zhang, Chengjie Ge, Lu Ren, Xinkun Suo

“…The results show that the trained support vector regression (SVR) model demonstrated the highest prediction precision for microhardness. …”
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Adaptive drive-based integration technique for predicting rheological and mechanical properties of fresh gangue backfill slurry by Chaowei Dong, Jianfei Xu, Nan Zhou, Jixiong Zhang, Hao Yan, Zejun Li, Yuzhe Zhang

“…Analysis demonstrates that the particle swarm optimal (PSO) algorithm based on adaptive adjustment strategy can effectively optimize the hyperparameters of support vector regression (SVR), and the MC-PSO-SVR model exhibits better predictive capability (R2> 0.88) and lower error coefficients (MAE, RSE, and RMSE values approaching 0) and narrower widths of 95 % confidence intervals for yield stress, plastic viscosity, fluidity, and UCS. …”
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Non-Destructive Methods Based on Machine Learning for the Prediction of Sweet Potato Leaf Area: A Comparative Approach by Joao Everthon Da Silva Ribeiro, Ester Dos Santos Coelho, Antonio Gideilson Correia Da Silva, Pablo Henrique De Almeida Oliveira, Elania Freire Da Silva, Gisele Lopes Dos Santos, Anna Kezia Soares De Oliveira, John Victor Lucas Lima, Walter Esfrain Pereira, Lindomar Maria Da Silveira, Aurelio Paes Barros

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ACO-NM hybrid optimization calculation method for transit time of oxygen activation logging in CO2 injection profile by WANG Zhengyan, CHEN Meng, YANG Guofeng, LIU Guoquan, PEI Yang, CHEN Qiang

“…This approach reduced the dimension of the solution vector and enhanced operational efficiency. Despite the NM algorithm’s advantages of requiring no prior guidance and exhibiting rapid convergence, as a direct optimization algorithm, its results were greatly affected by the initial solution. …”
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Optimizing machine learning methods for groundwater quality prediction: Case study in District Bagh, Azad Kashmir, Pakistan by Usman Basharat, Wenjing Zhang, Cuihong Han, Shoukat Husain Khan, Arshad Abbasi, Sehrish Mahroof, Shuxin Li

“…Six supervised machine learning classifiers were utilized, namely Logistic Regression (LR), K-Nearest Neighbours (KNN), Decision Trees (DT), Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB). …”
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Combine photosynthetic characteristics and leaf hyperspectral reflectance for early detection of water stress by Linbao Li, Linbao Li, Linbao Li, Guiyun Huang, Guiyun Huang, Guiyun Huang, Jinhua Wu, Jinhua Wu, Jinhua Wu, Yunchao Yu, Yunchao Yu, Yunchao Yu, Guangxin Zhang, Guangxin Zhang, Guangxin Zhang, Yang Su, Yang Su, Yang Su, Xiongying Wang, Xiongying Wang, Xiongying Wang, Huiyuan Chen, Huiyuan Chen, Huiyuan Chen, Yeqing Wang, Di Wu, Di Wu, Di Wu

“…Multivariate Linear Regression (MLR) and three machine learning algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) were employed to develop models for estimating LCC and ChlF parameters. …”
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Black-box and white-box machine learning tools to estimate the frost formation condition during cryogenic CO2 capture from natural gas blends by Farag M.A. Altalbawy, Fadhel F. Sead, Dharmesh Sur, Anupam Yadav, José Gerardo León Chimbolema, Suhas Ballal, Abhayveer Singh, Anita Devi, Kamal Kant Joshi, Nizomiddin Juraev, Hossein Mahabadi Asl

“…Three distinct black-box algorithms, including Regression Tree (RT), Radial Basis Function Neural Network (RBF-NN) and Support Vector Machine (SVM) were employed to model FFT. …”
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Improving the accuracy of honey bee forage class mapping using ensemble learning and multi-source satellite data in Google Earth Engine by Filagot Mengistu, Binyam Tesfaw Hailu, Temesgen Alemayehu Abera, Janne Heiskanen, Tadesse Terefe Zeleke, Tino Johansson, Petri Pellikka

“…Four machine learning algorithms (Gradient Tree Boost (GTB), Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM)), all available in GEE, were compared and ensembled for honey bee forage class mapping. …”
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Comparative Analysis of Machine Learning and Deep Learning Models for Lung Cancer Prediction Based on Symptomatic and Lifestyle Features by Bireswar Dutta

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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Machine learning models, including Backpropagation Neural Network (BPNN), Support Vector Machines (SVM), and particle swarm optimization algorithms (PSO-BPNN, PSO-SVR), were applied for regression analysis. …”
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Predicting student retention: A comparative study of machine learning approach utilizing sociodemographic and academic factors by Reymark D. Deleña, Norniña J. Dia, Redeemtor R. Sacayan, Joseph C. Sieras, Suhaina A. Khalid, Amer Hussien T. Macatotong, Sacaria B. Gulam

“…A total of 482 student records and 146 variables were preprocessed using Power BI and prepared via the CRISP-DM methodology before being modeled in Jupyter Notebook. Ten ML algorithms such asExtreme Gradient Boosting (XGBoost), Gradient Boosting (GB), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB) were evaluated using both train-test split and 5-fold cross-validation to ensure robustness and generalizability. …”
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Dengesiz Veri Kümelerinde İnme Tahmini İçin Özel Seçilimli Hibrit Dengeleme Yöntemi Tasarımı ve Uygulaması by Derya Yılmaz Baysoy, Türker Aksoy, Zeynep Orman, Şerife Çelikbaş

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A Dual-Variable Selection Framework for Enhancing Forest Aboveground Biomass Estimation via Multi-Source Remote Sensing by Dapeng Chen, Hongbin Luo, Zhi Liu, Jie Pan, Yong Wu, Er Wang, Chi Lu, Lei Wang, Weibin Wang, Guanglong Ou

“…A dual-variable selection strategy based on SHapley Additive exPlanations (SHAP) was developed, and a genetic algorithm (GA) was used to optimize the parameters of five machine learning models—elastic net (EN), least absolute shrinkage and selection operator (Lasso), support vector regression (SVR), Random Forest (RF), and Categorical Boosting (CatBoost)—to estimate the AGB of <i>Pinus kesiya</i> var. …”
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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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A Hybrid Machine Learning-Based Framework for Data Injection Attack Detection in Smart Grids Using PCA and Stacked Autoencoders by Shahid Tufail, Hasan Iqbal, Mohd Tariq, Arif I. Sarwat

“…However, reducing PCA components from 10 to 5 led to performance decreases in all models. The Support Vector Machine (SVM) Classifier shows the highest accuracy drop of 14.21%. …”
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A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data by Shahid Ahmad Wani, SMK Quadri, Mohammad Shuaib Mir, Yonis Gulzar

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Mosquito breeding water parameters are important determinants for Microsporidia MB in the aquatic stages of Anopheles species by Esinam A. Akorli, Nana Efua Andoh, Richardson K. Egyirifa, Christopher Dorcoo, Sampson Otoo, Seraphim N. A. Tetteh, Reuben Mwimson Pul, Derrick B. Sackitey, Stephen K. D. Oware, Samuel K. Dadzie, Jewelna Akorli

“…Abstract Background Microsporidia MB disrupts Plasmodium development in Anopheles mosquitoes, making it a possible biocontrol tool for malaria. As a tool for vector/disease control, its ecological distribution and the factors that determine their occurrence must be defined. …”
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