Forecasting the Remaining Duration of an Ongoing Solar Flare by Jeffrey W. Reep, Will T. Barnes

“…We test this on a large collection of flares observed with GOES‐15, and show that it generally outperforms simple linear regression, giving a median error of less than 2 min for the approximate end time of a flare. …”
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Random Algorithm and Skill Evaluation System Based on the Combing of Construction Mechanism of Higher Vocational Professional Group by Wei Jia

“…The simulation results show that the random forest algorithm is applied to skill evaluation with high accuracy, small error, and better generalization ability.…”
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Comparative Analysis of Machine Learning Techniques for Prediction of the Compressive Strength of Field Concrete by Omobolaji Opafola, Abisola Olayiwola, Ositola Osifeko, Adekunle David, Ajibola Oyedejı

“…The developed GB model achieved R-squared values of 91.60%, 91.43%, and 90.18% for the 10-fold, 5-fold, and 3-fold cross-validations, respectively, with mean absolute error, root mean squared error, and mean absolute percentage error values of 2.6776, 4.3523, and 9.19%, respectively. …”
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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…Conventional ITS algorithms often struggle in complex forest stands due to reliance on heuristic rules and manual feature engineering. …”
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Influencing factors of cross screening rate and its intelligent prediction model by Lala ZHAO, Feng XU, Chenlong DUAN, Chenhao GUO, Wei WANG, Haishen JIANG, Jinpeng QIAO

“…The prediction performance of each model was compared by using three evaluation indexes coefficient of determination (R2), mean square error (EMS) and mean absolute error (EMA). Among them, the PSO-SVM prediction model has the best performance and the strongest fitting ability to the data. …”
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Machine learning vehicle fuel efficiency prediction by So-rin Yoo, Jae-woo Shin, Seoung-Ho Choi

“…To evaluate the machine learning model, MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R-squared ( $$R^2$$ Score) were used. …”
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Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks by Ivan Gudelj, Mario Lovrić, Emmanuel Karlo Nyarko

“…The results show that the 1D CNN model outperforms the other machine learning models (LSTM and Random Forest) in terms of the coefficients of determination and absolute errors.…”
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Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…In conclusion, the SAHI-Improved-YOLOv8 has the capability of efficiently processing high-resolution images, which alleviates the problems of high density of small targets, false detections, missed detections, and high localization error. In practical applications, the SAHI-Improved-YOLOv8 model performs excellently in tree pit detection in UAV imagery, significantly reducing false detections and missed detections, and providing reliable technology support for large-scale forest management.…”
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Estimation of Above-Ground Biomass for <italic>Dendrocalamus Giganteus</italic> Utilizing Spaceborne LiDAR GEDI Data by Huanfen Yang, Zhen Qin, Qingtai Shu, Li Xu, Jinge Yu, Shaolong Luo, Zaikun Wu, Cuifen Xia, Zhengdao Yang

“…The outcomes reveal that 1) the results showed that the power function emerged as the most efficacious model, with coefficient of determination (<italic>R</italic>²) &#x003D; 0.87 and root mean square error (RMSE) &#x003D; 0.00051 Mg, in estimating the AGB of <italic>Dendrocalamus giganteus</italic>. 2) Based on the feature importance ranking of Random Forest, five variables were selected from the 40 extracted from GEDI, achieving RMSE &#x003D; 8.21 Mg&#x002F;ha and mean absolute error (MAE) &#x003D; 6.12 Mg&#x002F;ha. …”
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Integration of Convolutional Neural Network and Image Processing for Pulp Fibril Detection and Measurement by Tanachot Chirakitsakul, Pakaket Wattuya, Phichit Somboon, Panthira Jansakra, Chakrit Watcharopas

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Micro hole drilling and multi criteria optimization of soda lime glass via ultrasonic assisted rotary electrochemical discharge drilling by Sahil Grover, Viveksheel Rajput, Sanjay Kumar Mangal, Sarbjit Singh, Sandeep Singh, Shubham Sharma, Ehab El Sayed Massoud, Dražan Kozak, Jasmina Lozanovic

“…Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. …”
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The Role of Country- and Firm-Level Factors in Determining Firms&#x2019; Environmental, Social, and Governance (ESG) Performance: A Machine Learning Approach by Eman Abdelfattah, Mahfuja Malik, Syed Muhammad Ishraque Osman

“…For the random forests regressor, the coefficient of determination (R2) was 30% and the mean absolute error (MAE) was 1.52. …”
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Addressing Spatial Variability in Estimating Cover Management Factor of Soil Erosion Models using Geoinformatics: A Case Study of Netravati Catchment, Karnataka, India by Waleed Makhdumi, Shwetha H. R., G. S. Dwarakish, Jagadeesha B. Pai

“…The model’s performance was evaluated using statistical metrics, including a correlation coefficient of 0.984, mean absolute error of 0.048, root mean square error of 0.058, and Kling-Gupta efficiency of 0.921, indicating superior accuracy compared to existing methods. …”
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A stacking ensemble model for food demand forecasting: A preventative approach to food waste reduction by Asmaa Seyam, Sujith Samuel Mathew, Bo Du, May El Barachi, Jun Shen

“…The experimental results reveal that the proposed stacking model outperforms random forest and eXtreme gradient boosting while consistently outperforming support vector regression and long short-term memory model, achieving a coefficient of determination score of 0.99, mean absolute error of 0.63, mean absolute percentage error of 1.8, and prediction accuracy of 98.2%. …”
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A rapid approach to profiling diverse fungal communities using the MinION™ nanopore sequencer by Korena K Mafune, Bruce J Godfrey, Daniel J Vogt, Kristiina A Vogt

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Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin, Zixuan Qiu

“…A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. …”
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Research on memory failure prediction based on ensemble learning. by Peng Zhang, Jialiang Zhang, Yi Li

“…To address this, we propose a new ensemble model for predicting CE-driven memory failures, where failures occur due to a surge of correctable errors (CEs) in memory, causing server downtime. Our model combines several strong-performing classifiers, such as Random Forest, LightGBM, and XGBoost, and assigns different weights to each based on its performance. …”
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Hybrid Model for 6G Network Traffic Prediction and Wireless Resource Optimization by Mohammed Anis Oukebdane, A. F. M. Shahen Shah, Md Baharul Islam, John Ekoru, Milka Madahana

“…The results of the proposed hybrid model are presented and compared with baseline methods, including LSTM, GRU, random forest, and XGBoost. Our model obtains a Root Mean Squared Error (RMSE) of 0.0049, an Mean Absolute Error (MAE) of 0.0034, a mean absolute percentage error (MAPE) of 0.46%, and a coefficient of determination <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.9970 according to experimental findings on a whole dataset. …”
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Design of upper limb muscle strength assessment system based on surface electromyography signals and joint motion by Siqi Wang, Wei Lai, Yipeng Zhang, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Model performance was evaluated using Root Mean Squared Error (RMSE), R-Square (R2), Mean Absolute Error (MAE), and Mean Bias Error (MBE), to identify the most accurate regression prediction algorithm.ResultsThe system effectively collected and analyzed the sEMG from the deltoid muscles and shoulder joint motion data. …”
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A Sensor Data Prediction and Early-Warning Method for Coal Mining Faces Based on the MTGNN-Bayesian-IF-DBSCAN Algorithm by Mingyang Liu, Xiaodong Wang, Wei Qiao, Hongbo Shang, Zhenguo Yan, Zhixin Qin

“…Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). …”
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