Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…Validated on two diverse datasets, namely, Luxembourg (LuST) and Monaco (MoST), the framework achieves high precision (∼72% and ∼73% in single-city scenarios) and robust cross-city performance (∼70% for LuST → MoST and ∼66% for MoST → LuST). Random Forest and Gradient Boosting emerged as the top-performing models, consistently delivering the best precisions and lowest number of errors. …”
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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. …”
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Study on infrasonic leakage monitoring and signal processing for product oil pipeline by Yuanbo YIN, Yuxing LI, Wen YANG, Shu LU, Chen ZHANG, Cuiwei LIU, Kai YANG, Wuchang WANG

“…Subsequently, a random forest classification model was established, incorporating fifteen time-domain features and four frequency-domain features of the signals. …”
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Precision Estimation of Rice Nitrogen Fertilizer Topdressing According to the Nitrogen Nutrition Index Using UAV Multi-Spectral Remote Sensing: A Case Study in Southwest China by Lijuan Wang, Qihan Ling, Zhan Liu, Mingzhu Dai, Yu Zhou, Xiaojun Shi, Jie Wang

“…The RF surpassed BPNN/PLSR by 6.14–10.10% in R² and 13.71–33.65% in error reduction across the critical rice growth stages. …”
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Optimizing collection methods for noninvasive genetic sampling of neotropical felids by Claudia Wultsch, Lisette P. Waits, Eric M. Hallerman, Marcella J. Kelly

“…Additionally, we tested fecal samples collected from 4 different locations on the scat (top, side, bottom, inside) at 2 different tropical forest types (tropical broadleaf and tropical pine forests). …”
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Activity prediction of anti-cancer drug candidate ERα inhibitor by XIA Yulan, XIE Jiming, WANG Yajing, LU Mengyuan, WANG Jinrui, QIN Yaqin

“…The results show that compared with the GBDT integrated learning method, the prediction effect of Mul-BHO-Bi-LSTM integrated machine learning prediction model is better, and the model error indexes MSE, NRMSE, error mean, and error std are less than 0.15, and the correlated indicators R2 and r are above 0.99, indicating that the integrated machine learning predictionmodel of Mul-BHO-Bi-LSTM has the good robustness and generalization, and the model can provide a method for the screening and design of anti-breast cancer drugs.…”
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Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE by Waad Abuouelezz, Nazar Ali, Zeyar Aung, Ahmed Altunaiji, Shaik Basheeruddin Shah, Derek Gliddon

“…Performance metrics including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Percent Bias (PBIAS) were applied. …”
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Influence of artificial intelligence on higher education reform and talent cultivation in the digital intelligence era by Limin Qian, Weiran Cao, Lifeng Chen

“…The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. …”
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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO by Bo Qiu, Jian Zhang, Yun Yang, Guangyuan Qin, Zhongyi Zhou, Cunrui Ying

“…The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. …”
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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…Experiments are conducted on meteorological datasets from Seattle and Ukraine, and the results are verified using mean absolute error (MAE), root mean square error (RMSE), and bias evaluation indicators. …”
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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). …”
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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…After data preprocessing, two primary machine learning algorithms were employed: Support Vector Regression (SVR) and Random Forest (RF), both implemented using Scikit-learn. …”
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Air Quality Prediction Using Neural Networks with Improved Particle Swarm Optimization by Juxiang Zhu, Zhaoliang Zhang, Wei Gu, Chen Zhang, Jinghua Xu, Peng Li

“…To address this challenge, we propose a novel prediction model that integrates an adaptive-weight particle swarm optimization (AWPSO) algorithm with a back propagation neural network (BPNN). First, the random forest (RF) algorithm is used to scree the influencing factors of AQI concentration. …”
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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 ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). …”
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xAAD–Post-Feedback Explainability for Active Anomaly Discovery by Damir Kopljar, Vjekoslav Drvar, Jurica Babic, Vedran Podobnik

“…This paper introduces xAAD, a novel approach that combines Active Anomaly Discovery (AAD) with the Assist-Based Weighting Scheme (AWS) explainability metric for Isolation Forest-based anomaly detection. Our method enhances model interpretability and reduces false positives by incorporating expert feedback and providing post-feedback feature importance values. …”
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Interpretable Machine Learning for High-Accuracy Reservoir Temperature Prediction in Geothermal Energy Systems by Mohammadali Ahmadi

“…This study conducts a comprehensive comparative analysis of advanced machine learning models, including support vector regression (SVR), random forest (RF), Gaussian process regression (GP), deep neural networks (DNN), and graph neural networks (GNN), to evaluate their predictive performance for reservoir temperature estimation. …”
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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.…”
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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

“…Furthermore, a random forest (RF) regression surrogate model is established using finite element point cloud data samples. …”
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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. …”
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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. …”
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