A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…Notably, the coefficient of determination (R2) on the test set increased by 16.14%, while the root mean square error decreased by 20.44%, respectively. Nine machine learning algorithms, including decision tree (DT), random forest (RF), CatBoost, LassoLarsCV, histogram gradient boosting, extreme gradient boosting (XGBoost), K nearest neighbor, ridge regression (Ridge), and linear regression (Linear R), were employed to optimize PSC preparation and assess its impact on device performance. …”
Get full text

Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Compared with traditional stepwise linear regression (LR) algorithm, machine learning algorithms including artificial neural network (ANN), support vector regression, K‐nearest neighbor, random forest, and extreme gradient boosting were utilized to establish the regression model. …”
Get full text

Associations between ambient particulate matter exposure and the prevalence of arthritis: Findings from the China Health and Retirement Longitudinal Study. by Yuntian Ye, Kuizhi Ma, Aifeng Liu

“…The levels of air pollution exposure were estimated using a spatial-temporal extreme random forest model, integrating ground monitoring, remote sensing data, and model simulations, encompassing PM1, PM2.5, PM10, NH4, NO3, O3, and SO4 concentrations. …”
Get full text

A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis by Bin Li, Bo Qiao, Qianyu Zhao, Dan Yang, Rongcheng Zhu, Zhexuan Wang, Yujie Yang

“…Based on the collected data, a support vector regression (SVR) algorithm was trained and its performance was compared with that of a backpropagation (BP) neural network, a radial basis function (RBF) neural network, and a random forest (RF) algorithm. To optimize performance, a grid search with five-fold cross-validation was conducted to identify optimal hyperparameters; this process yielded a cost parameter (C) of 38 and a gamma parameter (γ) of 8, which minimized the root mean square error (RMSE) on the training set. …”
Get full text

Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Among the tested models, GPR consistently outperformed all others, achieving a maximum coefficient of determination (R²) of 0.93 and the lowest root mean square error (RMSE) of 16.54, thereby demonstrating superior capability in capturing the underlying nonlinear relationships within the data. …”
Get full text

Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Materials and Methods Using demographic and preoperative variables of 823 patients with clinical T1N0M0 renal tumors who underwent PN or RN between 2007 and 2019, we employed 5 different machine learning algorithms—general linear model (GLM), extreme gradient boosting (XgBoost), gradient boosting machine, distributed random forest, deep learning—and compared to predict recurrence probability and estimated glomerular filtration rate (eGFR) at 5-year after surgery. …”
Get full text

Automated Broiler Mobility Evaluation Through DL and ML Models: An Alternative Approach to Manual Gait Assessment by Mustafa Jaihuni, Yang Zhao, Hao Gan, Tom Tabler, Hairong Qi

“…The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. …”
Get full text

Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning by Tong Li, Yunpeng Li, Wenxin Cheng, Jufeng Zheng, Lianqing Li, Kun Cheng

“…This study employed four classical modeling approaches—the Stepwise Regression Model, Decision Tree Regression, Support Vector Machine, and Random Forest (RF)—to simulate soil N₂O emissions from Chinese upland fields. …”
Get full text

Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…The study constructed four machine learning models—random forest (RF), a Support Vector Machine (SVM), Gradient Boosting (GB), and Ridge Regression (Ridge)—using the meteorological variables required by the Priestley–Taylor (PT) and Hargreaves (HG) equations as inputs. …”
Get full text

Biomass distribution law of winter wheat in mining-affected area based on UAV remote sensing by Jing WANG, Wenbing GUO, Zhichao CHEN, Erhu BAI

“…Biomass inversion models were constructed using decision tree regression (DTR), random forest regression (RFR), and support vector regression (SVR) based on field-synchronous biomass data. …”
Get full text

Continuous Estimation of Swallowing Motion With EMG and MMG Signals by Zhenhui Guo, Ziyang Wang, Yue Wang, Weiguang Huo, Jianda Han

“…., Gaussian process regression (GPR), LSTM, and random forest (RF), are used for swallowing motion estimation based on EMG/MMG signals measured from six healthy subjects and a patient with PD, respectively. …”
Get full text

Chronic Kidney Disease Prediction Based On Machine Learning Algorithms by Kethineni Likitha., Nithinchandra, Kumar Narendra, Sk Sajida Sultana.

“…It was found that the maximum accuracy was achieved by the Random Forest model that justified its applicability as an effective tool for the early diagnosis of CKD. …”
Get full text

Assessing dengue forecasting methods: a comparative study of statistical models and machine learning techniques in Rio de Janeiro, Brazil by Xiang Chen, Paula Moraga

“…Machine learning techniques evaluated were Random Forest, XGBoost, Support Vector Machine (SVM), Long–Short-Term Memory (LSTM) networks, and Prophet. …”
Get full text

Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

“…Ten different machine learning models, including AdaBoost, Bagging, CatBoost, Decision Tree, Extra Trees, Gradient Boosting, HistGradientBoosting, LightGBM, Random Forest, and XGBoost, were evaluated using CuWO₄@TiO₂ as a photocatalyst. …”
Get full text

Evaluating Supervised Learning Classifier Performance for OFDM Communication in AWGN-Impacted Systems by Lavanya Vaishnavi D A, Anil Kumar C

“…., Peak Signal to Noise Ratio (PSNR), Power to Average Power Ratio (PAPR) and Bit Error Ratio (BER) and Accuracy, in the perspective of the AWGN channel Mean and Variance are considered as variables from 0.4 to 1 and 0.7 ± 0.3 respectively. …”
Get full text

Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Feature selection was performed using Shapley additive explanations (SHAP) on a random forest model, and training was done with 5-fold cross-validation (80% training, 20% testing). …”
Get full text

Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…A three-stage random forest model was developed to predict annualized changes in muscle composition and a functional outcome (timed up-and-go (TUG)). …”
Get full text

A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines by S. Milani, J. Leoni, S. Cacciola, A. Croce, M. Tanelli

“…</p> <p>To tackle this challenge, this paper introduces a novel machine-learning-based approach that relies on the combination of random forest classifier instances and linear regression for automatic pitch misalignment detection and localization. …”
Get full text

Rapid diagnosis of drug resistance to fluoroquinolones, amikacin, capreomycin, kanamycin and ethambutol using genotype MTBDRsl assay: a meta-analysis. by Yan Feng, Sijun Liu, Qungang Wang, Liang Wang, Shaowen Tang, Jianming Wang, Wei Lu

“…From these calculations, forest plots and summary receiver operating characteristic (SROC) curves were produced.…”
Get full text

Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou, Xiubin Luo

“…We employed four machine learning models—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Geographical Gaussian Process Regression (GGPR) for modeling, prediction, and accuracy comparison, with the objective of achieving high-precision salinity mapping under complex vegetation cover conditions. …”
Get full text