On the measurement of microclimate by Ilya M. D. Maclean, James P. Duffy, Stef Haesen, Sanne Govaert, Pieter De Frenne, Thomas Vanneste, Jonathan Lenoir, Jonas J. Lembrechts, Marcus W. Rhodes, Koenraad Van Meerbeek

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An Integrated Framework for Cryptocurrency Price Forecasting and Anomaly Detection Using Machine Learning by Hani Alnami, Muhammad Mohzary, Basem Assiri, Hussein Zangoti

“…Evaluation metrics, such as the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²), demonstrate the superior precision and reliability of the Random Forest and Gradient Boosting models. …”
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Using traffic data to identify land-use characteristics based on ensemble learning approaches by Jiahui Zhao, Zhibin Li, Pan Liu

“…The result averages improved 12.63%, 12.84%, 11.05%, 5.44%, 12.84% for Area Under ROC Curve (AUC), Classification Accuracy (CA), F-Measure (F1), Precision, and Recall, respectively, in classification tasks and 56.81%, 21.20%, 47.29% for Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively, in regression tasks than other models. …”
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation by Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, Zainab Al-Khafaji

“…Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process.…”
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Forecasting Urban Rail Transit Vehicle Interior Noise and Its Applications in Railway Alignment Design by Yifeng Wang, Ping Wang, Zihan Li, Zhengxing Chen, Qing He

“…In this study, a data-driven interior noise prediction model is developed for vehicles on an urban rail transit system based on random forest (RF) and a vehicle/track coupling dynamic model (VTCDM). …”
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. The results displayed that the two ensemble models, Extreme Gradient Boosting and Random Forest, outperformed single models in hourly, daily, and monthly energy load prediction. …”
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A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization by Zhibo Yu, Yong Wu, Ziyu Zhang, Chi Lu, Hong Wang, Zhi Liu, Xiaoli Zhang, Lei Bao, Jie Pan, Guanglong Ou, Hongbin Luo

“…Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of Pinus kesiya var. langbianensis, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. …”
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Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm by Xubo Huang, Chen Li, Yanbin Wang, Jinrong Jiang, Weizhi Wu, Shifeng Wang, Ming Lin, Liang He

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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…The RBF algorithm with highest Coefficient of Determination (R2) value of 0.81 and the lowest Mean Absolute Errors (MAE) and Root Mean Square Error (RMSE) values of 148.61 kg.ha-1, and 185.31 kg.ha-1, respectively, was the most accurate algorithm and, therefore, selected as the metaClassifier for the E-B algorithm. …”
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Rapid prediction algorithm for flow field in fully mechanized excavation face based on POD and machine learning by JIN Bing, ZHANG Lang, LI Wei, ZHENG Yi, LIU Yanqing, ZHANG Yibin

“…The results showed that the numerical simulation model for the excavation face had a relative error within 3%, accurately reflecting the actual air flow and dust distribution. …”
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Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva, Lourenço Lázaro Magaia

“…The results revealed strong trends towards the adoption of artificial neural network (ANN), random forest (RF), and simple linear regression (SLR) models for a sample taken from the Nipepe station in Niassa, validated by a PF model with errors of 0.10, 0.11, and 0.15. …”
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The combination of miniaturized GPS/VHF tags and soluble backpack harness as an effective method for tracking lightweight and elusive birds by Pedro Sáez‐Gómez, Cristian Pérez-Granados, Germán M. López-Iborra

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Fuzzy PD control for a quadrotor with experimental results by Anh T. Nguyen, Nam H. Nguyen, Mien L. Trinh

“…Quadrotor is an unmanned aerial vehicle widely used in traffic construction monitoring, volcano monitoring, forest fire, power line inspection, missing person search and disaster relief. …”
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Multi-Fidelity Machine Learning for Identifying Thermal Insulation Integrity of Liquefied Natural Gas Storage Tanks by Wei Lin, Meitao Zou, Mingrui Zhao, Jiaqi Chang, Xiongyao Xie

“…The results of the data experiments demonstrate that the multi-fidelity framework outperforms models trained solely on low- or high-fidelity data, achieving a coefficient of determination of 0.980 and a root mean square error of 0.078 m. Three machine learning algorithms—Multilayer Perceptron, Random Forest, and Extreme Gradient Boosting—were evaluated to determine the optimal implementation. …”
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An LSTM neural network prediction model of ultra-short-term transformer winding hotspot temperature by Kun Yan, Jingfu Gan, Yizhen Sui, Hongzheng Liu, Xincheng Tian, Zehan Lu, Ali Mohammed Ali Abdo

“…Utilizing principal component analysis (PCA) to reduce data correlation and employing long short-term memory (LSTM) neural networks for predicting winding hotspots can lead to reduced errors and improved prediction accuracy, enabling quicker and more efficient forecasts. …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…The performance was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Garbage prediction using regression analysis for municipal corporations of Indian cities by Raj Kumar Sharma, Manisha Jailia

“…Random Forest Regression (RFR) with (MSE: 100,078.749 & MAE: 182.212) shows that it has the lowest MSE among all the models, which provides the most accurate predictions on average and the fit values of 8.85 and 316.23 obtained from the error distribution with a bin value 25. …”
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Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning by Zhang Xiumei, Ma Bo, Zhang Yijie

“…Coefficient of determination (R2), mean absolute error (MAE), and mean relative error (MRE) were used as error indicators to evaluate the potential of the six machine learning algorithms for predicting NDVI changes. …”
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Remotely geolocating static events observed by citizens using data collected by mobile devices by Jacinto Estima, Ismael Jesus, Cidália C. Fonte, Alberto Cardoso

“…While most research has focused on GNSS-based positioning errors, compass-based orientation errors have received far less attention. …”
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Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation by Rong Mei, ChengJiang Yu

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