Assessment of geotechnical behavior of gypseous soil under leaching effect using machine learning by Saif M. Hassan Al-Riahi, Nur Irfah Mohd Pauzi, Mohammed Y. Fattah, Hasan Ali Abbas

“…Model performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). …”
Get full text

Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…Model performance was evaluated using Normalized Mean Squared Error (NMSE) as the evaluation metric. The results indicated that the RF model achieved an NMSE of approximately 0.35, reflecting a moderate level of prediction error relative to data variance. …”
Get full text

Air Quality Prediction Using Neural Networks with Improved Particle Swarm Optimization by Juxiang Zhu, Zhaoliang Zhang, Wei Gu, Chen Zhang, Jinghua Xu, Peng Li

“…The experimental results show that the RFAWPSO-BP model reduces the root mean square error and mean absolute error by 9.17 μg/m³, 5.7 μg/m³, 2.66 μg/m³; and 9.12 μg/m³, 5.7 μg/m³, 2.68 μg/m³ compared with the BP, PSO-BP, and AWPSO-BP models, respectively; furthermore, the goodness of fit of the proposed model was 14.8%, 6.1%, and 2.3% higher than that of the aforementioned models, respectively, demonstrating good prediction accuracy.…”
Get full text

Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…The results have shown excellent predictions of the ANN model, as demonstrated in the high coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn><mo> </mo></mrow></msup></mrow></semantics></math></inline-formula> = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). …”
Get full text

xAAD&#x2013;Post-Feedback Explainability for Active Anomaly Discovery by Damir Kopljar, Vjekoslav Drvar, Jurica Babic, Vedran Podobnik

“…We evaluate xAAD on both synthetic and real-world datasets, demonstrating improved performance in terms of binary classification accuracy and root mean square error (RMSE) compared to traditional AWS. The results show that xAAD consistently outperforms the baseline in both simulated and real-world scenarios, suggesting a positive impact on the interpretability of anomaly detection systems across multiple industries.…”
Get full text

Development of mobile application for tree height measurement using geometric principle: Establishing global database of tree height and data by Mubarak Mahmud, Jianhong Lin, Mojtaba Houballah, Ibrahim Garba Buba, Laure Barthes

“…Accurate measurement of tree height is essential for ecological research, forest management, and carbon sequestration assessments. …”
Get full text

Interpretable Machine Learning for High-Accuracy Reservoir Temperature Prediction in Geothermal Energy Systems by Mohammadali Ahmadi

“…Results demonstrate that RF outperforms other models, achieving the lowest mean squared error (MSE = 66.16) and highest R² score (0.977), which is attributed to its ensemble learning approach and robust handling of nonlinear relationships. …”
Get full text

Modeling the Thermal Regime of Road Pavement and Roadbed of Logging Roads by Vladimir I. Kleveko

“…According to the results of experimental studies, the freezing value has been 173 cm, and according to the results of numerical simulation – 190 cm. The average error in the results of numerical simulation of the freezing process of the pavement and the upper zone of the forest roadbed has been 8–10 % compared to the experimental data.…”
Get full text

Real-Time State Evaluation System of Antenna Structures in Radio Telescopes Based on a Digital Twin by Hanwei Cui, Binbin Xiang, Shike Mo, Wei Wang, Shangmin Lin, Peiyuan Lian, Wei Wang, Congsi Wang

“…Secondly, the quadric error metrics (QEM) mesh-simplification algorithm and mesh-reconstruction technology are employed to obtain a lightweight twin model of the antenna. …”
Get full text

Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…Random forest demonstrated superior generalization capabilities compared to decision trees; however, it encountered challenges with mid-range data variability. …”
Get full text

Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China by Zhonghe Zhao, Yuyang Li, Kun Liu, Chunsheng Wu, Bowei Yu, Gaohuan Liu, Youxiao Wang

“…By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. …”
Get full text

Comparing supervised classification algorithm–feature combinations for Spartina alterniflora extraction: a case study in Zhanjiang, China by Qiujie Chen, Qiujie Chen, Chunyan Shen, Hong Du, Danling Tang

“…Mangrove forests are vital blue carbon ecosystems whose security is increasingly threatened by the non-native species Spartina alterniflora. …”
Get full text

A novel machine learning-based approach to determine the reduction factor for punching shear strength capacity of voided concrete slabs by Alireza Mahmoudian, Mussa Mahmoudi, Mohammad Yekrangnia, Nima Tajik, Mostafa Mohammadzadeh Taleshi

“…The efficacy of the approach is showcased using the Random Forest Regressor model, finely tuned through a Grid Search technique, with performance evaluated using the R-squared coefficient and Root Mean Squared Error metrics. …”
Get full text

Artificial intelligence-driven near-infrared spectrophotometry model for rapid quantification of anti-nutritional factors in soybean (Glycine max.) by Norberto Jose Palange, Tonny Obua, Julius Pyton Sserumaga, Enoch Wembabazi, Mildred Ochwo-Ssemakula, Ephraim Nuwamanya, Isaac Onziga Dramadri, Moses Matovu, Richard Edema, Phinehas Tukamuhabwa

“…However, conventional methods available for quantifying anti-nutritional factors such as phytate and trypsin inhibitors in feeds are laboratory-intensive, time-consuming, expensive, and error-prone. This study developed near-infrared spectrophotometry (NIRS)-based models to quantify phytate and trypsin inhibitors in soybean. …”
Get full text

Improved estimation of stomatal conductance by combining high-throughput plant phenotyping data and weather variables through machine learning by Junxiao Zhang, Kantilata Thapa, Geng (Frank) Bai, Yufeng Ge

“…Three supervised ML methods (Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR)) were employed to train the estimation models for gs, and model performance was evaluated by Coefficient of Determination (R2) and Root Mean Squared Error (RMSE). …”
Get full text

Estimation of soil temperature for agricultural applications in South Africa using machine-learning methods by Lindumusa Myeni, Tlotlisang Nkhase, Ramontsheng Rapolaki, Zaid Bello, Mokhele E. Moeletsi

“…The results showed that soil temperature at various depths can be reasonably estimated by different generic machine-learning models, with average Nash–Sutcliffe efficiency values ranging from 0.74 for decision tree to 0.87 for random forest models and root mean square error values of less than 2.79 °C for all models. …”
Get full text

Wastewater-based epidemiology of influenza A virus in Shenzhen: baseline values and implications for multi-pathogen surveillance by Xiuyuan Shi, Shisong Fang, Chen Du, Guixian Luo, Yanpeng Cheng, Zhen Zhang, Qiuying Lv, Xin Wang, Zhigao Chen, Bincai Wei, Ziqi Wu, Bingchan Guo, Panpan Yang, Miaomiao Luo, Weihua Wu, Liping Zhou, Ting Huang, Xuan Zou, Xiaolu Shi, Songzhe Fu, Zhanwei Du, Xinxin Han, Yinghui Li, Qinghua Hu

“…The optimized random forest model (mean absolute error = 2,307, R2 = 0.988) integrated IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen, and phosphorus. …”
Get full text

GNSS-R-Based wildfire detection: a novel and accurate method by Xuke Wang, Wei Yao

“…In recent years, global forests have faced frequent wildfires due to climate change, leading to significant ecological and economic losses. …”
Get full text

High resolution soil moisture mapping in 3D space and time using machine learning and depth functions by Mo Zhang, Yong Ge, Gerard B.M. Heuvelink, Yuxin Ma

Get full text

Prediction of Insulator ESDD Based on Meteorological Feature Mining and AdaBoost-MEA-ELM Model by Yaoping WANG, Te LI, Kaihua JIANG, Wenhui LI, Qiang WU, Yu WANG

“…The results show that the average absolute error of ESDD prediction of AdaBoost-MEA-ELM model is 0.0032 mg/cm2, which is 58.97% lower than that of the original ELM model. …”
Get full text