Enhancing the color and stress tolerance of cherry shrimp (Neocaridina davidi var. red) using astaxanthin and Bidens Pilosa. by Wei-Wei Hou, Yu-Tzi Chang, Wen-Chin Yang, Hong-Yi Gong, Yen-Ju Pan, Te-Hua Hsu, Chang-Wen Huang

“…This study aimed to evaluate the effects of different concentrations of astaxanthin and Bidens Pilosa compound feed additives on the color and hypoxia tolerance of cherry shrimp (Neocaridina davidi var. red). …”
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Using ML techniques to predict extubation outcomes for patients with central nervous system injuries in the Yun-Gui Plateau by Zi-han Chen, Hao-tian Wu, Zhou Yao, Qian Liu, Hong-mei Zhang, Xiao-chen Li, Li-qing Yao, Xue Yang

“…Hyperparameter optimization and tenfold cross-validation were used to assist in choosing model parameters. Random forest (ACC = 84.15, AUC = 0.85), extra trees (83.17%, 0.87), K-NN (82.18%, 0.85), and gradient boosting (81.19%, 0.85) performed well. …”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Abstract Purpose To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters. …”
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A Practical Model for Predicting Esophageal Variceal Rebleeding in Patients with Hepatitis B-Associated Cirrhosis by Linxiang Liu, Yuan Nie, Qi Liu, Xuan Zhu

“…The objective of our study was to evaluate the ability of generating a extreme gradient boosting (XGBoost) algorithm model to improve the prediction of variceal rebleeding. …”
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Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

“…Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. …”
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Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis by Aliyu Tetengi Ibrahim, Ibrahim Hayatu Hassan, Mohammed Abdullahi, Armand Florentin Donfack Kana, Amina Hassan Abubakar, Mohammed Tukur Mohammed, Lubna A. Gabralla, Mohamad Khoiru Rusydi, Haruna Chiroma

“…To optimize key hyper-parameters, this study employed the Dynamic Chaotic Whale Optimization Algorithm. …”
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A Novel Center of Mass Optimization (CMO) Algorithm for Truss Design Problems by Hesam Varaee

“…Mutation and elitism selection operators are also used to boost the overall performance of the proposed algorithm. …”
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Machine learning prediction of extended length of stay following endoscopic decompression for lumbar spinal stenosis: A retrospective cohort study by Fuqiang Tan, Xiaobin Li, Chaoyang Qu, Xin Shu, Xu Peng

“…Model performance was evaluated via AUC-ROC, F1-score, and SHAP interpretability. …”
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Enhancing Healthcare With WBAN and Digital Twins: A Machine Learning Approach for Predictive Health Monitoring by Rishit Mahapatra, Deepak Sethi, Kaushik Mishra

“…Each model was evaluated based on accuracy, precision, recall, F1 score, ROC AUC score, and cross-validation accuracy. …”
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Enhancing stone matrix asphalt performance with sugarcane bagasse ash: Mechanical properties and machine learning-based predictions using XGBoost and random forest by Hamed Khani Sanij, Rezvan Babagoli, Reza Mohammadi Elyasi

“…In parallel, the study applied machine learning (ML) models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—to predict the mechanical properties of SMA based on input mix parameters. …”
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Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. …”
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Predicting in-hospital mortality in ICU patients with lymphoma using machine learning models. by Ling Xu, Guang Tu, Zhonglan Cai, Tianbi Lan

“…Model performance was evaluated through cross-validation and SHapley Additive exPlanation (SHAP) values to interpret variable importance. …”
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Machine Learning Models for the Noninvasive Diagnosis of Bladder Outlet Obstruction and Detrusor Underactivity in Men With Lower Urinary Tract Symptoms by Hyungkyung Shin, Kwang Jin Ko, Wei-Jin Park, Deok Hyun Han, Ikjun Yeom, Kyu-Sung Lee

“…Purpose This study aimed to develop and evaluate machine learning models, specifically CatBoost and extreme gradient boosting (XGBoost), for diagnosing lower urinary tract symptoms (LUTS) in male patients. …”
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Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel by Fariha Ahmed Nishat, M. F. Mridha, Istiak Mahmud, Meshal Alfarhood, Mejdl Safran, Dunren Che

“…A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. …”
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Machine learning for cardio-oncology: predicting global longitudinal strain from conventional echocardiographic measurements in cancer patients by Tagayasu Anzai, Kenji Hirata, Ken Kato, Kohsuke Kudo

“…The best model for the test dataset was the CatBoost classifier (AUC, 0.748; accuracy, 0.734). Diastolic dysfunction indices [such as septal/lateral mitral annular early diastolic velocity (e’) and E-wave to atrial contraction filling velocity (E/A)] and peak velocity‑related parameters [aortic valve peak velocity (AV-Vmax) and left ventricular outflow tract velocity maximum (LVOT-Vmax)] played essential roles in the Low-GLS prediction model. …”
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Investment Assessments in the Adoption of Accessible and Assistive Technologies Within Built Environments for Persons with Disabilities by Siny Joseph, Vinod Namboodiri

“…Numerical evaluations demonstrate the range of parametric values where accessibility investments pay off. …”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…The performance of various ML algorithms is evaluated while investigating the effect of data transformation techniques on prediction accuracy. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…The effects of key process parameters in spray drying (sorbent particle size, inlet gas phase temperature, and Ca:S ratio) on desulfurization efficiency were investigated using central composite design (CCD). …”
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Development and validation of a predictive model for invasive ventilation risk within 48 hours of admission in patients with early sepsis-associated acute kidney injury by Li Hong, Bin Wang

“…Variables included age, blood parameters, and vital signs at admission. Patients were divided into training and validation cohorts. …”
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Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics by Jinghong Pei BD, Jing Yu BD, Ping Ge BD, Liman Bao BD, Haowen Pang MS, Huaiwen Zhang MS

“…Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. …”
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