Development and optimization of the control device for the hydraulic drive of the belt conveyor by Leonid Polishchuk, Oleh Piontkevych, Artem Svietlov, Oksana Adler, Dmytro Lozinsky

“…Various optimization methods for dynamic processes have been considered. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. …”
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Prediction of ball-on-plate friction and wear by ANN with data-driven optimization by Alexander Kovalev, Yu Tian, Yonggang Meng

“…To predict sliding friction and wear at mixed lubrication conditions, in this study a specific ANN structure was so designed that deep learning algorithms and data-driven optimization models can be used. Experimental ball-on-plate friction and wear data were analyzed using the specific training procedure to optimize the weights and biases incorporated into the neural layers of the ANN, and only two independent experimental data sets were used during the ANN optimization procedure. …”
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Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression by Askhat Diveev, Elena Sofronova, Nurbek Konyrbaev

“…A reference model is then integrated into the control object model, which generates optimal motion trajectories using the derived optimal control functions. …”
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Hybrid machine learning applications in pavement engineering: predicting spalling with PSO-GBM by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…The Particle Swarm Optimization (PSO)-Gradient Boosting Machine (GBM) model was implemented to enhance prediction accuracy and was benchmarked against baseline GBM and Linear Regression models. …”
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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…To address this, we employed five ML models, Decision Tree, Ranger, Random Forest, Support Vector Machine, and k-nearest Neighbors, and optimized their parameters using the random search technique. …”
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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models by Pedro Torres-Bermeo, Kevin López-Eugenio, Carolina Del-Valle-Soto, Guillermo Palacios-Navarro, José Varela-Aldás

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Torque Estimation in Switched Reluctance Machines: A Comprehensive Approach Involving Inductance Modeling Techniques by Ricardo Tirone Fidelis, Ghunter Paulo Viajante, Eric Nery Chaves, Carlos E. Tavares, Augusto W. F. V. Da Silveira, Luciano Coutinho Gomes

“…This work highlights advances in the torque estimation method for Switched Reluctance Machines (SRMs), focusing on a high-precision torque estimator that integrates classical and modern modeling techniques. …”
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Measurement and Modeling of Spindle Thermal Error of Fiveaxis CNC Machine Tool with Double Turntable by LIU Xianli, SONG Houwang, WU Shi, YUE Caixu, Steven Y.Liang, LI Rongyi

“…In order to measure the thermal error of the spindle in the actual cutting process of CNC machine tools and optimize the output of the thermal error model, a method of measuring the thermal error of the spindle of machine tools by using the thermal test piece is proposed, and the thermal error is separated by using the error characteristics. …”
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Hybrid modeling of adsorption process using mass transfer and machine learning techniques for concentration prediction by Jing Lv, Lei Wang

“…To improve model performance, hyperparameters were optimized using the bio-inspired Barnacles Mating Optimizer (BMO) algorithm. …”
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MODELING THE ALLOCATION OF HAZARDOUS SUBSTANCES INTO THE AIR OF WORKING AREA STATIONS RUN THE MACHINE-BUILDING PLANTS by L.N. ALEKSEENKO

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Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models by Reza Piraei, Majid Niazkar, Fabiola Gangi, Gökçen Eryılmaz Türkkan, Seied Hosein Afzali

“…This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…The model was constructed using machine learning techniques based on multicenter data and screened for key features. …”
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Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome by Jing Lv, Juan Chen, Meijun Liu, Xue Dai, Wang Deng

“…The AUC value, AP value, accuracy, sensitivity, specificity, Brier score, and F 1 score were used to evaluate the performance of the models and pick the optimal model. Finally, the SHAP feature importance map was drawn to explain the optimal model.Results10 key variables, namely LAR, Lac, pH, age, PO2/FiO2, ALB, BMI, TP, PT, DBIL were screened using the filtration method. …”
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Hybrid Machine Learning Models for Predicting the Impact of Light Wavelengths on Algal Growth in Freshwater Ecosystems by Himaranga Sumanasekara, Harshi Jayasingha, Gayan Amarasooriya, Narada Dayarathne, Bandita Mainali, Lalantha Senevirathna, Ashoka Gamage, Othmane Merah

“…., and <i>Mougeotia</i> sp.) in freshwater systems, using machine learning to optimize growth models. Natural light yielded the highest algal proliferation, increasing the total count from 90 to 1390 cells/mL in 30 days. …”
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Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI by Nadesalingam Paripooranan, Warnakulasuriya Buddhini Nirasha, H. R. P. Perera, Sahan M. Vijithananda, P. Badra Hewavithana, Lahanda Purage Givanthika Sherminie, Mohan L. Jayatilake

“…Among the morphological features, the total volume of the contralateral breast, surface area of the contralateral breast, breast density, and the ratio of the total volume of the contralateral breast to its surface area had higher F-scores, indicating that the dimensions of the contralateral breast could be an important factor in differentiating IDC and ILC.ConclusionThis study successfully developed and optimized a predictive model based on breast morphological features to differentiate IDC and ILC using machine learning methods.…”
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Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients by Yingxi Chen, Chunyu Wang, Chunyu Wang, Xiaozhu Liu, Minjie Duan, Tianyu Xiang, Haodong Huang, Haodong Huang

“…Logistic regression screened eight features, Lasso regression screened ten features, the RFE method screened eight, fourteen, sixteen, and thirteen features for SVM, RF, XgBoost, and LightGBM, respectively. Among all models, the XgBoost model based on features selected by RFE+LightGBM demonstrated the best performance, achieving an AUC of 0.814 (95% CI, 0.779-0.847), accuracy of 0.799 (95% CI, 0.771-0.827), precision of 0.841 (95% CI, 0.812-0.868), recall of 0.920 (95% CI, 0.898-0.941), and F1-score of 0.879 (95% CI, 0.859-0.897) in the testing set.ConclusionsBased on T2DM data and machine learning theory, a Bayesian-optimized XgBoost model was established using the RFE+LightGBM method. …”
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Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory by Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh, Fei Zhou, Vincenzo Lordi

“…We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. …”
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Predictive value of machine learning model based on CT values for urinary tract infection stones by Jiaxin Li, Yao Du, Gaoming Huang, Chiyu Zhang, Zhenfeng Ye, Jinghui Zhong, Xiaoqing Xi, Yawei Huang

“…Additionally, the XGBoost model was identified as the optimal model significantly outperforming the traditional LR model. …”
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