Optimizing Mental Stress Detection via Heart Rate Variability Feature Selection by Mohsen Behradfar, Shotabdi Roy, Joseph Nuamah

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Machine Training of the System of Functional Diagnostics of the Shaft Lifting Machine by Dovbysh A. S., Zimovets V. I., Zuban Y. A., Prikhodchenko A. S.

“…The tasks set forth in the work were to develop a categorical model; to carry out synthesis based on its hierarchical machine teaching algorithm for a functional diagnosis system; and to optimize the system of acceptance tolerance. …”
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Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome by Weirong Xue, Bing Xu, Hui Wang, Xiaoxiao Zhu, Jiajia Qin, Guangshuang Zhou, Peilin Yu, Shengli Li, Yingliang Jin

“…The test set was used to compare kernel function combinations, with the area under the curve (AUC), sensitivity, specificity, and accuracy as evaluation metrics. The optimal model, identified through the best-performing kernel combination, was further compared with three classical machine learning models.ResultA kernel combination integrating all four basic kernels achieved the highest average AUC (0.831), specificity (0.772), and accuracy (0.780), along with marginally lower but more stable sensitivity (0.795) compared to other combinations. …”
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Comparative Analysis of Automated vs. Expert-Designed Machine Learning Models in Age-Related Macular Degeneration Detection and Classification by Ceren Durmaz Engin, Ufuk Beşenk, Denizcan Özizmirliler, Mustafa Alper Selver

“…Objectives: To compare the effectiveness of expert-designed machine learning models and code-free automated machine learning (AutoML) models in classifying optical coherence tomography (OCT) images for detecting age-related macular degeneration (AMD) and distinguishing between its dry and wet forms. …”
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A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents by Ricardo Scheufen Tieghi, José Teófilo Moreira-Filho, Holli-Joi Martin, James Wellnitz, Miguel Canamary Otoch, Marielle Rath, Alexander Tropsha, Eugene N. Muratov, Nicole Kleinstreuer

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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning by Hui Xu, Xingwang Peng, Ziyu Peng, Rui Wang, Rui Zhou, Lianguo Fu

“…First, dual feature selection was conducted to identify important feature variables for model construction. Second, ML models were constructed based on the logistic regression (LR), decision tree (DT), support vector machine (SVM) and eXtreme Gradient Boosting (XGBoost) algorithms, after which random sampling and tenfold cross-validation were separately used to evaluate and compare these models and identify the optimal model. …”
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Advanced machine learning techniques for predicting mechanical properties of eco-friendly self-compacting concrete by Arslan Qayyum Khan, Syed Ghulam Muhammad, Ali Raza, Amorn Pimanmas

“…This study evaluates the performance of advanced machine learning (ML) models in predicting the mechanical properties of eco-friendly self-compacting concrete (SCC), with a focus on compressive strength, V-funnel time, L-box ratio, and slump flow. …”
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A Synergistic Framework for Coupling Crop Growth, Radiative Transfer, and Machine Learning to Estimate Wheat Crop Traits in Pakistan by Rana Ahmad Faraz Ishaq, Guanhua Zhou, Aamir Ali, Syed Roshaan Ali Shah, Cheng Jiang, Zhongqi Ma, Kang Sun, Hongzhi Jiang

“…PROSAIL-HLS has an RMSE of 0.67 for leaf area index (LAI), 5.66 µg/cm² for chlorophyll ab (Cab), 0.0003 g/cm² for dry matter content (Cm), and 0.002 g/cm² for leaf water content (Cw) against the HLS only, with an RMSE of 0.40 for LAI, 3.28 µg/cm² for Cab, 0.0002 g/cm² for Cm, and 0.001 g/cm² for Cw. Optimized machine learning models, namely Extreme Gradient Boost (XGBoost) for LAI, Support Vector Machine (SVM) for Cab, and Random Forest (RF) for Cm and Cw, were deployed for temporal mapping of traits to be used for wheat productivity enhancement.…”
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Optimization of the prognosis of postoperative complications in patients with breast cancer by L. A. Trusova

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Hypothermic machine perfusion in uterus transplantation in a porcine model: A proof of concept and the first results in graft preservation by Carla Héléna Sousa, Marion Mercier, Nathalie Rioux‐Leclercq, Erwan Flecher, Claude Bendavid, David Val‐Laillet, Juliette Ferrant, Sylvie Jaillard, Emma Loiseau, Julien Branchereau, Yanis Berkane, Krystel Nyangoh Timoh, Isis Carton, Maëla Le Lous, Vincent Lavoue, Ludivine Dion

“…The aim of this study was to assess the feasibility and interest of hypothermic machine perfusion (HMP) in uterus transplantation using a porcine model; secondary outcomes were the evaluation of the graft's tolerance to a prolonged cold ischaemia time and to find new biomarkers of uterus viability. …”
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Smart CAR-T Nanosymbionts: archetypes and proto-models by Juan C. Baena, Juan C. Baena, Juan Sebastián Victoria, Juan Sebastián Victoria, Alejandro Toro-Pedroza, Alejandro Toro-Pedroza, Cristian C. Aragón, Cristian C. Aragón, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Joshua Ortiz-Guzman, Juan Esteban Garcia-Robledo, Juan Esteban Garcia-Robledo, David Torres, Lady J. Rios-Serna, Lady J. Rios-Serna, Ludwig Albornoz, Joaquin D. Rosales, Joaquin D. Rosales, Carlos A. Cañas, Carlos A. Cañas, Carlos A. Cañas, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Gustavo Adolfo Cruz-Suarez, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Felipe Ocampo Osorio, Tania Fleitas, Ivan Laponogov, Alexandre Loukanov, Alexandre Loukanov, Alexandre Loukanov, Kirill Veselkov, Kirill Veselkov

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Design and Implementation of a Deep Learning Model and Stochastic Model for the Forecasting of the Monthly Lake Water Level by Waleed Ahmed Hassen Al-Nuaami, Lamiaa Abdul-jabbar Dawod, B. M. Golam Kibria, Shahryar Ghorbani

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A hybrid approach to financial big data analysis using extended ensemble learning and optimized spark streaming by Muhammad Babar

“…The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial datasets. …”
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Damping of Low-Frequency Oscillations in a Power System, Based on Multiple-Model Optimal Control Strategy by Elahe Pagard, Shahrokh Shojaeian, Mohammad Mahdi Rezaei

“…To improve the postfault dynamics of the case study power system, which contains a static VAR compensator, a linear optimal controller (LOC) is designed. The 8th order model of the machines is used in simulations to represent the plant as accurately as possible.…”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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A Storm Frame Optimization Method for Predicting and Warning the Safety Status of a Shearer by Pei Zhang, Yanpeng He, Li Ma, Changkui Cong

“…First, the GRU model is optimized through hyperparameter optimization to achieve adaptive and accurate prediction and early warning of multidimensional state parameters of the shearer. …”
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Driver identification in advanced transportation systems using osprey and salp swarm optimized random forest model by Akshat Gaurav, Brij B. Gupta, Razaz Waheeb Attar, Ahmed Alhomoud, Varsha Arya, Kwok Tai Chui

“…The proposed model achieves an accuracy of 92%, a precision of 91%, a recall of 93%, and an F1-score of 92%, significantly outperforming traditional machine learning models such as XGBoost, CatBoost, and Support Vector Machines. …”
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Application of Response Surface Methodology and Central Composite Inscribed Design for Modeling and Optimization of Marble Surface Quality by Sümeyra Cevheroğlu Çıra, Ahmet Dağ, Askeri Karakuş

“…This study has shown that the CCI could efficiently be applied for the modelling of polishing machine for surface quality of marble strips. …”
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Optimal Configuration Model of Photovoltaic and Energy Storage Joint System Considering Coordination of Multiple Transaction Scenarios by CHEN Kun, SHANG Long-long, LUO Jin-ge

“…This article analyzes the benefits of photovoltaic energy storage participation in electrical energy transactions, frequency regulation, peak adjustment and other application scenarios. We establish a optimization model based on multiple transaction scenarios. …”
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A wireless sensor data-based coal mine gas monitoring algorithm with least squares support vector machines optimized by swarm intelligence techniques by Peng Chen, Yonghong Xie, Pei Jin, Dezheng Zhang

“…Due to the fact that the “negative samples” of coal mine safety data are scarce, least squares support vector machine is introduced to deal with this problem. In addition, several swarm intelligence techniques such as particle swarm optimization, artificial bee colony algorithm, and genetic algorithm are applied to optimize the hyper parameters of least squares support vector machine. …”
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