Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

“…The study utilizes the 2020 dataset from Jersey City’s bike-sharing system, starting with the application of the Isolation Forest algorithm to detect and filter anomalous data points. …”
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Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods by Bilal Cemek, Yunus Kültürel, Emirhan Cemek, Erdem Küçüktopçu, Halis Simsek

“…Performance was evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R²). …”
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Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning by Yuning Tan, Huaixiang Liu, Yongjun Lu, Zhili Wang, Wenjie Li

“…New hydrological insights for the region: The proposed framework firstly reduced the total sedimentation error from 53.42 % to 3.44 % and the maximum group-wise error from 90.88 % to 13.46 %, highlighting the dominant influence of tributary sediment inputs and flocculation factor on reservoir sedimentation. …”
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A hybrid approach to financial big data analysis using extended ensemble learning and optimized spark streaming by Muhammad Babar

“…Empirical evaluations using the Portuguese Bank Marketing dataset demonstrate that the proposed architecture achieves a high prediction accuracy of 90.9%, outperforming individual models such as Logistic Regression, SVM, and Random Forest. The ensemble model also reports a mean absolute error (MAE) of 0.023 and a mean squared error (MSE) of 0.0018. …”
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Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines by Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen

“…The framework reduced mean absolute error by 25.1% and mean absolute percentage error by 33.4% compared to conventional methods. …”
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Snow depth estimation in Northeast China based on space-borne scatterometer data and ML model with optimal features by Wenfei Chen, Lingjia Gu, Xiaofeng Li, Xintong Fan

“…Multiple machine learning (ML) models, including support vector regression (SVR), k-nearest neighbors (KNN), XGBoost, and random forest (RF), were deployed and contrasted for SD estimation. …”
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Identifying the Peak Flowering Dates of Winter Rapeseed with a NBYVI Index Using Sentinel-1/2 by Fazhe Wu, Peng Lu, Shengbo Chen, Yucheng Xu, Zibo Wang, Rui Dai, Shuya Zhang

“…The study uses phenological characteristics and the random forest classification algorithm to create a map of winter rapeseed in parts of the middle and lower reaches of the Yangtze River Basin, achieving a Kappa coefficient of 90.57%. …”
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Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot by Pengfei Chen, Yutang Wang, Dapeng Tian

“…The leader–follower delivery error of the catheter/guidewire is less than 1 mm, and the leader–follower rotation error of the guidewire is less than 0.3° in an actual intervention task based on a human vascular model. …”
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An interpretable and stacking ensemble model for predicting heat and mass transfer of desiccant wheel by Mengyang Li, Liu Chen

“…Coefficient of Determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) were used to measure this stacking model: the process side outlet temperature (R2 = 0.9467, RMSE=1.5239, and MAE = 1.2721), the process side outlet humidity ratio (R2 = 0.9743, RMSE = 0.5728, MAE = 0.4531). …”
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Comparing the effect of pre-anesthesia clonidine and tranexamic acid on intraoperative bleeding volume in rhinoplasty: a machine learning approach by Zahra Asghari Varzaneh, Akram Hemmatipour, Hadi Kazemi-Arpanahi

“…The results revealed that the Linear and Ridge regression algorithms outperformed all other models based on three evaluation metrics: mean absolute error (MAE), mean square error (MSE), and R-squared. …”
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Predicting tilling and seeding operation times in grain production: A comparison of machine learning and mechanistic models by Luca Scheurer, Tobias Zimpel, Joerg Leukel

“…Nine ML algorithms and two conventional mechanistic models proposed by the American Society of Agricultural and Biological Engineers (ASAE EP496.3) were evaluated in a temporal external validation. Random forest (RF) models outperformed all other models, achieving a normalized root mean square error (NRMSE) of 0.215 and a coefficient of determination (R2) of 0.910. …”
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Predictive model to identify multiple synergistic effects of geriatric syndromes on quality of life in older adults: a hospital-based pilot study by Chien-Chou Su, Yung-Chen Yu, Deng-Chi Yang

“…Model performance was evaluated by 5-fold cross-validation with metrics of R-square, the mean square error of estimation and the mean absolute error of estimation. …”
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Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods by Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams

“…Results: The most important predictors of the LOS of CABG patients in the ICU were the length of intubation, body mass index (BMI), age, duration of surgery, and the number of postoperative transfusions of packed cells. The Random Forest model also performed best in predicting the effective factors (Mean square Error = 1.64, Mean absolute error = 0.93, and R2 = 0.28) Conclusion: The insights gained from the mashine learning model highlight the significance of demographic and clinical variables in predicting LOS in ICU. …”
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Evaluating the Thermohydraulic Performance of Microchannel Gas Coolers: A Machine Learning Approach by Shehryar Ishaque, Naveed Ullah, Sanghun Choi, Man-Hoe Kim

“…The developed model was validated against a wide range of experimental data and was found to accurately predict the gas cooler capacity (Q) and pressure drop (ΔP) within an acceptable margin of error. Furthermore, advanced machine learning algorithms such as extreme gradient boosting (XGB), random forest (RF), support vector regression (SVR), k-nearest neighbors (KNNs), and artificial neural networks (ANNs) were employed to analyze their predictive capability. …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

“…We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. …”
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A Hybrid Machine Learning Approach for Estimating Aboveground Biomass and Carbon Stock in Tanzania’s Miombo Woodlands by Emmanuel F. Chifunda, Ramkumar T. Balan, Peter J. Kirigiti

“…Model quality was evaluated using root-mean-square error (RMSE, Mg/tree), coefficient of determination (R2), and mean absolute error (MAE, Mg/tree). …”
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Use of Socio-economic, Climatic, and Land use Land Cover Patterns in Solid Waste Forecasting with Integrated Gradient LSTNet Based Model in Lomé, Togo by Kanlanféi Sambiani, Yendoubé Lare, Adamou Zanguina, Satyanarayana Narra

“…Moreover, the metrics such as root relative square error (RSE) and relative absolute error (RAE) were used to evaluate models’ performance. …”
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Assessing artificial intelligence ability in predicting hospitalization duration for pleural empyema patients managed with uniportal video-assisted thoracoscopic surgery: a retrosp... by Issa Alnajjar, Baraa Alshakarnah, Tasneem AbuShaikha, Tareq Jarrar, Abed Al-Raheem Ozrail, Yousef Abu Asbeh

“…The first one had a mean absolute error of 4.56 days and a negative R2 value. The literature-informed model performed similarly, with a mean absolute error of 4.53 days and an R2 below zero. …”
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Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection. by Ekramul Haque Tusher, Mohd Arfian Ismail, Abdullah Akib, Lubna A Gabralla, Ashraf Osman Ibrahim, Hafizan Mat Som, Muhammad Akmal Remli

“…Ensemble ML methods, including Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB), and Decision Tree (DT), are used to predict HCV infection. …”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…The SVR-trained model substantially outperformed other models, achieving an R-squared of 0.81, Root Mean Square Error (RMSE) of 0.33 and Mean Absolute Error (MAE) of 0.27, enabling precise integration prediction. …”
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