Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer by Lidan Liu, Bo Liu, Huimei Wu, Qiuying Gan, Qianyi Huang, Mujun Li

“…Multiple machine learning models, such as Logistic Regression, Random Forest, Gradient Boosting, and Voting Classifier, were developed. Metrics including Area Under the Curve(AUC), accuracy, precision, recall, F1 score, and specificity were used to evaluate model performance. …”
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Using Physical Parameters for Phase Prediction of Multi-Component Alloys by the Help of TensorFlow Machine Learning with Limited DataUsing Physical Parameters for Phase Prediction... by Kağan Şarlar

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Efficient degradation of antibiotics mixture in a solar photoelectro-Fenton system using CuFe2O4@GO@MIL-100(Fe): Boosting efficiency via enhanced charge separation by Felipe Gamboa-Savoy, Christian Onfray, Jonathan Correa-Puerta, Durga Prasad Pabba, Natalia Hassan, Abdoulaye Thiam

“…The influence of key operational parameters, including pH, applied current, and nanomaterial concentration, was systematically evaluated. …”
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XGBoost based enhanced predictive model for handling missing input parameters: A case study on gas turbine by Nagoor Basha Shaik, Kittiphong Jongkittinarukorn, Kishore Bingi

“…This work extensively develops and evaluates an XGBoost model for predictive analysis of gas turbine performance. …”
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Logistic Service Improvement Parameters for Postal Service Providers Using Analytical Hierarchy Process and Quality Function Deployment by Nisa James, Anish K. P. Kumar, Robert Jeyakumar Nathan

“…This study investigates India Post, one of the largest postal networks globally, to determine the key logistics service parameters prioritized by customers in southern India. …”
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Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension by Zunfeng Fu, Jing Wang, Wenyi Shen, Yanqing Wu, Jiajun Zhang, Yan Liu, Chongqiang Wang, Yanlin Shen, Ye Zhu, Weifu Zhang, Chunju Lv, Lin Peng

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Reinventing Effective Grounding Systems via Optimal Soil Composition, Thickness, and Replacement Parameters Under Fault Conditions by Ahmed El-Tayeb Khalil, Adel Zein El Dein, Mahmoud Ramadan Hefny, Matti Lehtonen, Mohamed M. F. Darwish

“…The study explores the effects of soil replacement on critical parameters such as total ground resistance, earth surface potential (ESP), current density, and electric field distribution under line-to-ground fault conditions. …”
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Artificial intelligence-based optimization and modeling of cadmium reduction via ultraviolet-assisted malathion/sulfite reaction mechanisms by Hossein Azarpira, Parsa Khakzad, Tayebeh Rasolevandi, Amir Sheikhmohammadi

“…Regarding the training set evaluation, high accuracy was observed with minimal error and R2 of 0.996. …”
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P-12 METHYL GROUP DONOR SUPPLEMENTATION IN A MetALD MODEL: REGULATION OF GUT MICROBIOTA AND METABOLIC PARAMETERS by Carolina Díaz Canul, Angel Omar Vázquez Esqueda, Rebeca Rosas Campos, Rebeca Escutia Gutiérrez, Natalia Gómez Gómez, Monica Ibarra Hernández, Marina Galicia Moreno, Juan Armendáriz Borunda, Ana Sandoval Rodriguez

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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
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Credit card default prediction using ML and DL techniques by Fazal Wahab, Imran Khan, Sneha Sabada

“…The evaluation indicates that the AdaBoost and DT exhibit the highest accuracy rate of 82 ​% in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 ​%.…”
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INFLUENCE OF MEDICINAL PLANTS AND VITAMIN E ON PRODUCTIVE PERFORMANCE AND SOME PHYSIOLOGICAL PARAMETERS OF BROILER CHICKENS UNDER HEAT STRESS by Galawezh Kh. Qader, Ihsan T.Tayeb

“… This study was aimed to evaluate the influence of adding dietary three medicinal plants and vitamin E on productive performance, serum physiological parameters, immunity and antioxidant status of broiler chickens under heat stress (34 - 38) C° for 35 days. …”
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Lycopene dietary supplementation enhances the productive performance, antioxidants and lipid profile parameters in a dual-purpose chicken breed by Abeer Y. Sweed, Mahmoud Azzam, Alessandro Di Cerbo, Kasim Sakran Abass, Mahmoud Madkour, Ahmed A. Elolimy

“…Blood levels of some lipid profile parameters (triglycerides and very low-density lipoprotein) showed a significant reduction with increasing lycopene level. …”
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Multi-Source Satellite Imagery and Machine Learning for Detecting Geological Formations in Cameroon’s Western Highlands by Kacoutchy Jean Ayikpa, Valère-Carin Jofack Sokeng, Abou Bakary Ballo, Pierre Gouton, Koffi Fernand Kouamé

“…Finally, classifiers such as Random Forest, Gradient Boosting, and XGBoost (version 1.6.2) were used to evaluate the impact of each parameter on the classification. …”
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To Evaluate The Efficacy Of Intravenous Selenium Administration In Critically Ill Patients by Sudha puhal, Anju Ghai, Anju Rani, Rachana Verma, Sudhir Kumar Bisherwal

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The Value of MRI and Radiomics for the Diagnostic Evaluation of Thyroid-Associated Ophthalmopathy by Weiyi Zhou, Yan Song, Jufeng Shi, Tuo Li

“…Being one of the most commonly used and accurate objective examinations for TAO assessment, MRI boosts no ionizing radiation, high soft tissue contrast, better reflection of tissue water content, and the ability to quantify multiple parameters. …”
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Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses by Can Wang, Xinhui Wu, Zhaoquan Wang, Han Shao, Dapeng Ye, Xiangzeng Kong

“…Moreover, it boosts inference speed to 39.58 images per second. …”
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Impact of metal oxides on thermal response of zirconia coated diesel engines fueled by Momordica biodiesel machine learning insights by V. S. Shaisundaram, P. V. Elumalai, S. Padmanabhan, U. Nalini Ramachandran, Abhishek Kumar Tripathi, Cui Yaping, B. Nagaraj Goud, S. Prabhakar

“…Additionally, machine learning (ML) algorithms, including Multiple Linear Regression (MLR), Gradient Boosting Regression (GBR), and Random Forest Regression (RF), were applied to predict thermal performance metrics using input parameters such as Fuel, Compression Ratio (CR), Load, and Peak Pressure (Bar). …”
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Stability control of open stopes in high-stress deep mining: a structural parameter design methodology based on the improved mathews stability graph method by Zi-Bin Li, Zi-Bin Li, Deng-Pan Qiao, Deng-Pan Qiao, Tian-Yu Yang, Tian-Yu Yang

“…This finding highlights the pre-final blasting state as the critical node for stability evaluation. An ensemble model integrating Stacking, Bagging, Boosting, and Voting strategies demonstrated significant improvements in prediction accuracy and classification performance over traditional logistic regression.DiscussionFinally, validation in high-stress stopes at 600–1000 m depths confirmed the model’s generalization capability, offering a data-mechanism dual-driven decision framework for structural parameter design in deep open stopes.…”
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Achieving cloud resource optimization with trust-based access control: A novel ML strategy for enhanced performance by Bala Subramanian C, Bharathi ST, Shanmugapriya S

“…Results show that AdaPCA achieved a trust score prediction accuracy of 99.8 %, a resource utilization efficiency of 95 %, and reduced allocation time to 140 ms, outperforming the benchmark models across all evaluated parameters. AdaPCA had superior performance overall—expedited decision-making, optimized resource utilization, reduced latency, and the highest accuracy in trust evaluation among the evaluated models. …”
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