FlickPose: A Hand Tracking-Based Text Input System for Mobile Users Wearing Smart Glasses by Ryo Yuasa, Katashi Nagao

“…A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. …”
Get full text

Self-Supervised Learning for Precise Individual Tree Segmentation in Airborne LiDAR Point Clouds by Lama Shaheen, Bader Rasheed, Manuel Mazzara

“…Our method achieves up to 87% convexity, 78% solidity, and an elliptical fit error as low as 0.12, substantially reducing over-segmentation compared to baseline clustering techniques. …”
Get full text

Development of an Optimal Machine Learning Model to Predict CO₂ Emissions at the Building Demolition Stage by Gi-Wook Cha, Choon-Wook Park

“…., gradient boosting machine [GBM], decision tree, and random forest), based on the information on building features and the equipment used for demolition, as well as energy consumption data. …”
Get full text

Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. …”
Get full text

The impact of deferred cytoreductive nephrectomy on survival in advanced renal cell carcinoma: A systematic review and meta-analysis by Mohammad Taufiq Alamsyah, Fauriski Febrian Prapiska, Syah Mirsya Warli

“…The fixed-effect and random-effects models were used to obtain pooled estimates using the hazard ratio and standard error, presented using the forest plot with 95% confidence interval. …”
Get full text

Detecting Flooded Areas Using Sentinel-1 SAR Imagery by Francisco Alonso-Sarria, Carmen Valdivieso-Ros, Gabriel Molina-Pérez

“…A procedure developed to correct for this error gives an accuracy of 0.886 for this single event. …”
Get full text

Extreme Gradient Boosting Algorithm for Predicting Shear Strengths of Rockfill Materials by Mahmood Ahmad, Ramez A. Al-Mansob, Kazem Reza Kashyzadeh, Suraparb Keawsawasvong, Mohanad Muayad Sabri Sabri, Irfan Jamil, Arnold C. Alguno

“…XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). …”
Get full text

DEFINING A SUSTAINABLE TOURISM PERSPECTIVES IN EASTERN PART OF BALKHASH-ALAKOL BASIN by Rakhimzhanova GULNUR, Mussina KAMSHAT

“…Analyses show that regression reveals a 38.9% of error of prediction, indicating a moderate level of explanatory power in the model. …”
Get full text

LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…Also, it shows that the proposed LSTM+MA model significantly outperforms other models, with an <i>R</i>² of 0.965 and a mean square error (MSE) of 0.030 in the test datasets. Moreover, an overfitting score is proposed in this work to represent the severity degree of the overfitting problem, and it shows that the proposed model does not suffer severely from overfitting.…”
Get full text

Estimating individual tree DBH and biomass of durable Eucalyptus using UAV LiDAR by Ning Ye, Euan Mason, Cong Xu, Justin Morgenroth

“…Model performance was evaluated using the root mean square error and coefficient of determination (R2). SHapley Additive exPlanations (SHAP) analysis was employed to explain model predictions and evaluate input variables. …”
Get full text

A Bayesian Additive Regression Trees Framework for Individualized Causal Effect Estimation by Lulu He, Lixia Cao, Tonghui Wang, Zhenqi Cao, Xin Shi

“…Compared with mainstream meta-learning methods such as S-Learner, X-Learner and Bayesian Causal Forest, the dual-structure BART-ITE model achieves a favorable balance between root mean square error and bias. …”
Get full text

Load aggregator adjustable capability forecasting based on graph convolution neural network by DONG Lingrui, WU Binyuan

“…Taking the mean absolute percentage error (MAPE) index obtained from the example analysis as an example, compared with long short-term memory (LSTM), support vector machine (SVM), and random forest regression (RFR), the forecasting accuracy of GCN model has increased by 1.83%, 2.10% and 2.72% in terms of RMSE.…”
Get full text

Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning. by Wen Jiang, Wei Liu, Xiaotong Xin, Wei Zhang, Junhui Chen, Jieyu Liu, Yanqi Ma, Cheng Chen, Xiaomei Pan

“…In this paper, five sets of comparison models are set up, including two machine learning models (Random Forest, K-Nearest Neighbors, Artificial Neural Network) and two deep learning models (Convolutional Neural Network and Variational Autoencoder), and the results show that the model in this paper fits the best, obtaining an R2 of 0.9144, as well as a small error.…”
Get full text

Normalized Difference Red-Edge Estimation With Modified DiscoGAN Model by Hyeon-Beom Choi, Kwon-Hee Han, Jeongwook Seo

“…Vegetation information is important to study the health and productivity of farmlands and forest ecosystems and investigate the types and severity of threats to them. …”
Get full text

Precise prediction of choke oil rate in critical flow condition via surface data by Qing Wang, Muntadher Abed Hussein, Bhavesh Kanabar, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Mohammad Mahtab Alam, Hojjat Abbasi

“…The findings indicate that Random Forest outperforms the other algorithms, reaching a coefficient of determination (R2) of 0.96127 during evaluation, with the lowest error metrics. …”
Get full text

Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning–Based Multicohort (RIGIPREV) Study by Iván Cavero-Redondo, Arturo Martinez-Rodrigo, Alicia Saz-Lara, Nerea Moreno-Herraiz, Veronica Casado-Vicente, Leticia Gomez-Sanchez, Luis Garcia-Ortiz, Manuel A Gomez-Marcos

“…Model performance was evaluated using the coefficient of determination (R2) and mean squared error. ResultsThe random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. …”
Get full text

Simulating Land Use and Evaluating Spatial Patterns in Wuhan Under Multiple Climate Scenarios: An Integrated SD-PLUS-FD Modeling Approach by Hao Yuan, Xinyu Li, Meichen Ding, Guoqiang Shen, Mengyuan Xu

“…The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). …”
Get full text

Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques by Bailin Yue, Yong Jin, Shangrong Wu, Jieyang Tan, Youxing Chen, Hu Zhong, Guipeng Chen, Yingbin Deng

“…Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. …”
Get full text

iFLOW: A Framework and GUI to Quantify Effective Thermal Diffusivity and Advection in Permeable Materials From Temperature Time Series by A. Bertagnoli, C. Luce, R. vanKampen, U. Schneidewind, M. vanBerkel, A. W. Tranmer, G. Vandersteen, S. Krause, D. Tonina

Get full text

Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
Get full text