Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). …”
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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. The random forest model has the best predictive ability (determination coefficient (<italic>R</italic>²) is 0.995; root mean square error (<italic>E</italic>_RMS) and mean absolute error (<italic>E</italic>_MA) are 0.174 mm and 0.124 mm, respectively), followed by the stepwise regression model (<italic>R</italic>² is 0.989; <italic>E</italic>_RMS<italic> </italic>and <italic>E</italic>_MA<italic> </italic>are 0.192 mm and 0.151 mm). …”
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Supervised and unsupervised machine learning approaches for tree classification using multiwavelength airborne polarimetric LiDAR by Zhong Hu, Songxin Tan

“…Current studies have mainly employed commercial non-polarimetric LiDAR for forest surveying and monitoring using point cloud data. …”
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Evaluating the RMR correlation with the rock mass wave velocity using the meta-heuristics algorithms by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…Deep estimation capability analyses of the proposed GA, TRR and GA-TRR models were performed using the performance evaluation metrics, scatter plots, error histogram, Taylor diagram and regression error characteristic curve. …”
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Machine Learning Classifiers Based Classification For IRIS Recognition by Bahzad Taha Chicho, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dilovan Assad Zebari

“…The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. …”
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Machine learning modeling for thermochemical biohydrogen production from biomass by Yingju Chang, Wei Wang, Jo-Shu Chang, Duu-Jong Lee

“…Input features were analyzed using Random Forest (RF) and eXtreme Gradient Boosting (XGB) models, interpreted through SHapley Additive exPlanations (SHAP) and Partial Dependence Plot analyses. …”
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Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction by Tri Yulian, Erliyan Redy Susanto

“…This study aims to analyze the performance of Support Vector Machine (SVM) and Random Forest algorithms in predicting breast cancer using oversampling and SMOTEENN preprocessing techniques. …”
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Studi Pembuatan Kelas Bonita pada Tegakan Acacia mangium Willd. di PT. Musi Hutan Persada, Sumatera Selatan by Heru Budi Santoso, Ronggo Sadono, Ari Susanti

“…Study on the Determination of  Site Quality Index for Acacia mangium Willd. in PT Musi Hutan Persada, South Sumatra Site index is required to estimate forest productivity. This study was conducted to generate a diameter-height model and use it to construct a direct site quality index for Acacia mangium Willd. stands without thinning by dominant height approach in PT. …”
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Improved epigenetic age prediction models by combining sex chromosome and autosomal markers by Zhong Wan, Peter Henneman, Huub C. J. Hoefsloot, Ate D. Kloosterman, Pernette J. Verschure

“…Results We employed random forest regression (RFR) to construct age prediction models with publicly available DNAm Infinium 450 K microarray data of sex chromosomes from human whole blood and buffy coat samples and assessed the RFR model performance based on the root-mean squared error (RMSE) and the mean absolute deviation (MAD) of cross-validation. …”
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Spatial Quality Control Method for Surface Temperature Observations Based on Multiple Elements by Xiaoling Ye, Xing Yang, Xiong Xiong, Shuai Yang, Yang Chen

“…Therefore, a Random Forest quality control algorithm based on the Principal Component Analysis (PCA-RF) is proposed in this paper. …”
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Predicting and Mapping of Soil Organic Matter with Machine Learning in the Black Soil Region of the Southern Northeast Plain of China by Yiyang Li, Gang Yao, Shuangyi Li, Xiuru Dong

“…The SOM content of cultivated land was lower than that of forest land.…”
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Environmental conditions explain variability in concentrations of nutrients but not emerging contaminants by Megan L. Fork, Jerker Fick, Alexander J. Reisinger, Peter M. Groffman, Emma J. Rosi

“…As such, the ability to predict pharmaceutical concentrations over space and time using easier‐to‐monitor water quality parameters would expand our understanding of the scope and consequences of pharmaceutical contamination in aquatic ecosystems. We applied random forest models to data from the Baltimore Ecosystem Study to investigate how well routinely monitored water quality parameters could be used to predict concentrations of nutrients and pharmaceuticals. …”
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Federated Learning With Sailfish-Optimized Ensemble Models for Anomaly Detection in IoT Edge Computing Environment by Aravam Babu, A. Bagubali

“…To overcome this, the Sailfish Optimization Algorithm (SFO) is incorporated to fine-tune the Isolation Forest model&#x2019;s parameters dynamically, balancing exploration and exploitation. …”
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Engine Mass Flow Estimation through Neural Network Modeling in Semi-Transient Conditions: A New Calibration Approach by T. Savioli, M. Pampanini, G. Visani, L. Esposito, C. A. Rinaldini

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Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya by Mary C. Henry, John K. Maingi

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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

“…Fifteen combinations of linguistic features were trained using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Model performance was evaluated by prediction accuracy and the mean absolute error between predicted and actual readability. …”
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Jurisprudential Study of Wasteland Detection Operations by Hosein Rasoli Asiabi, Aliakbar Izadifard, Mahdi Zarei

“…By comparing the reason for the legitimacy of land assessment operations based on Article 1 of the Executive By-Law of Wastelands Identification Reference Law with the reason for the legitimacy of land assessment operations based on the provisions of Article 1 of the Executive By-Laws of the Nationalization of Forests, it is concluded that the view of the jurists in the position of recognizing lands is in accordance with the provisions of Article 1 of the Executive By-Law of the Wastelands Identification Reference Law, which has not been successful in the field of wastelands due to errors in the text of the law and the legislator's limited and purposeful will. …”
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Assessing the Inter-Rater and Inter-Trial Reliability of the NeurOptics Pupillary Light Response-3000 Pupillometer by Deborah A Jehu, Lori A Bolgla, Samantha Armas, Forest Dutton

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Predictive analysis of Somalia’s economic indicators using advanced machine learning models by Bashir Mohamed Osman, Abdillahi Mohamoud Sheikh Muse

“…This paper evaluates the performance of three machine learning models—Random Forest Regression (RFR), XGBoost, and Prophet—in predicting Somalia's GDP. …”
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Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach by Zeyue Li, Jianzhao Bi, Yang Liu, Xuefei Hu

“…In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA’s Goddard Earth Observing System “Composing Forecasting” (GEOS-CF) product. …”
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