A Wind Power Density Forecasting Model Based on RF-DBO-VMD Feature Selection and BiGRU Optimized by the Attention Mechanism by Bixiong Luo, Peng Zuo, Lijun Zhu, Wei Hua

“…Notably, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Squared Error (MSE) are substantially minimized compared to alternative models. …”
Get full text

An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields by Yi-Fan Wang, Max Yue-Feng Wang, Li-Ying Tu

“…Four forecasting models are employed for comparative analysis: linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural networks. …”
Get full text

Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters by Ishan R. Perera, Taylor Daniels, Janella Looney, Kimberly Gittings, Frederic A. Rawlins

“…Abstract Random forest models have demonstrated utility in the determination of New York Heart Association (NYHA) Heart Failure Classifications. …”
Get full text

Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing by Shuai Zhao, Shao-Qun Lin, Dao-Yuan Tan, Hong-Hu Zhu, Zhen-Yu Yin, Jian-Hua Yin

“…The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.…”
Get full text

Evaluation of hydraulic fracturing using machine learning by Ali Akbari, Ali Karami, Yousef Kazemzadeh, Ali Ranjbar

“…This study presents a comprehensive machine learning (ML)-based framework to address this challenge by predicting HF efficiency using three widely used algorithms: Random Forest (RF), Support Vector Machine (SVM), and Neural Networks (NN). …”
Get full text

Estimating Spatiotemporal Dynamics of Carbon Storage in <i>Roinia pseudoacacia</i> Plantations in the Caijiachuan Watershed Using Sample Plots and Uncrewed Aerial Vehicle-Borne Las... by Yawei Hu, Ruoxiu Sun, Miaomiao He, Jiongchang Zhao, Yang Li, Shengze Huang, Jianjun Zhang

“…Forest ecosystems play a pivotal role in the global carbon cycle and climate change mitigation. …”
Get full text

Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…Low values of the mean square error (0.0367) and mean absolute error (0.0324) were recorded. …”
Get full text

Real-time prediction of the rate of penetration via computational intelligence: a comparative study on complex lithology in Southwest Iran by Mohammad Najafi, Yousef Shiri

“…Similarly, the ANN had root mean square errors (RMSEs) of 0.69, mean absolute percentage errors (MAPEs) of 5.01%, and correlation coefficients of 0.93. …”
Get full text

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). …”
Get full text

Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN by QIN Hao, SU Liwei, WU Guangbin, JIANG Chongying, XU Zhipeng, KANG Feng, TAN Huochao, ZHANG Yongjun

“…Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. First, the isolated forest algorithm is used to identify the abnormal data, and the Lagrange interpolation function is applied to repair the abnormal data or missing data. …”
Get full text

Quantifying synthetic bacterial community composition with flow cytometry: efficacy in mock communities and challenges in co-cultures by Fabian Mermans, Ioanna Chatzigiannidou, Wim Teughels, Nico Boon

“…Therefore, axenic cultures, mock communities and co-cultures of oral bacteria were prepared. Random forest classifiers trained on flow cytometry data of axenic cultures were used to determine the composition of the synthetic communities, as well as strain specific qPCR and 16S rRNA gene amplicon sequencing. …”
Get full text

Improving phenological event identification in trees using manually measured dendrometer data: conventional approaches vs. the novel two-stage threshold approach by Przemysław A. Jankowski, Przemysław A. Jankowski, Rafael Calama, Jorge Aldea, Matías García, Guillermo Madrigal, Marta Pardos

“…Accurate detection of phenological events, such as growth onset, cessation, and seasonal transitions, is essential for understanding tree growth dynamics, particularly in Mediterranean forests where bimodal growth patterns are common. …”
Get full text

An Approach to Predicting Urban Carbon Stock Using a Self-Attention Convolutional Long Short-Term Memory Network Model: A Case Study in Wuhan Urban Circle by Zhi Zhou, Xueling Wu, Bo Peng

“…For forests, grasslands, and water bodies, the future spatial error model (SEM) indicated that for each unit increase in carbon stock change, the SLUDD would increase by 55, 7, and −305 units, respectively. …”
Get full text

Simulations of Microwave Land Surface Emissivity Using FengYun-3D Microwave Radiation Imager Data: A Case in the Tibetan Plateau by Yonghong Liu, Fuzhong Weng, Fei Tang, Rui Li, Yongming Xu, Yang Han, Jun Yang, Qingyang Liu

“…In this article, a new random forest (RF) algorithm is developed for retrieving MLSE under all-sky conditions. …”
Get full text

Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The metrics used in evaluating the performance of the models were accuracy score, mean absolute error (MAE), and root mean squared error (RMSE). …”
Get full text

Comparative Study of Machine Learning Techniques for Predicting UCS Values Using Basic Soil Index Parameters in Pavement Construction by Mudhaffer Alqudah, Haitham Saleh, Hakan Yasarer, Ahmed Al-Ostaz, Yacoub Najjar

“…The results indicate that the ANN-based model provided the most accurate predictions for UCS, achieving an R² of 0.83, a root-mean-squared error (RMSE) of 1.11, and a mean absolute relative error (MARE) of 0.42. …”
Get full text

Assessment of Future Flood Loss in the Daqing River Basin Based on Flood Loss Rate Function by SHI Rongqing, HUANG Lingmei, LI Jia, SHEN Ao

“…To identify flood-prone areas in the Daqing River Basin and classify flood risk levels, the Spearman's rank correlation coefficient and the random forest method were employed to analyze the correlation and importance between flood loss rates and influencing factors. …”
Get full text

Predictive modeling of punchouts in continuously reinforced concrete pavement: a machine learning approach by Ghazi Al-Khateeb, Ali Alnaqbi, Waleed Zeiada

“…By employing the random forest algorithm, key predictors like age, climate zone, and total thickness are identified. …”
Get full text

Precise Apple Yield Prediction Utilizing Differential Fusion of UAV and Satellite Multispectral Images by Meixuan Li, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Four predictive models&#x2014;partial least squares regression, support vector machine, random forest (RF), and backpropagation neural network&#x2014;were constructed and validated using field survey data from Qixia in 2023 and 2024. …”
Get full text

Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

“…Several models, including LSTM, GRU, Random Forest, KNN, Decision Tree, and XGBoost, were trained and evaluated for positioning accuracy. …”
Get full text