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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
Published 2024Get full text
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Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar
Published 2019“…Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. …”
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Metabolomics-based prediction model for diabetes: A comprehensive analysis of biomarkers and machine learning approaches
Published 2025“…</p><h3>Conclusion</h3><p dir="ltr">Metabolomics data can effectively predict diabetes status, with logistic regression providing the optimal balance of performance and interpretability. …”
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Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis
Published 2019“…The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. …”
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Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation
Published 2024“…Moreover, the proposed algorithm demonstrated excellent prediction accuracy with a mean absolute error (MAE) of <b>3.89</b> years, <b>3.64 </b>years, and <b>4.49</b> years for GM, WM, and CSF regions, confirming that changes in WM volume are significantly associated with normal brain aging. …”
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Modeling of photovoltaic soiling loss as a function of environmental variables
Published 2017“…The ANN model performed significantly better in predicting daily ΔCIas well as cumulative CI than the linear model in term of R2 values and statistical error indexes. …”
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Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach
Published 2022“…A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. …”
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Deep learning-based modeling of land use/land cover changes impact on land surface temperature in Greater Amman Municipality, Jordan (1980–2030)
Published 2024“…This study aimed to model past, present, and future LULCC on Land Surface Temperatures in the Greater Amman Municipality (GAM) in Jordan between 1980 and 2030. …”
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Role of nutritional indices in predicting outcomes of vascular surgery
Published 2019“…We aimed to assess the combined use of recent significant weight loss (>10% body mass) and serum albumin levels as a nutritional status index to predict outcomes. …”
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Prediction of the Backwater Level Due to Bridge Constriction in Waterways
Published 2019Get full text
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Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach
Published 2025“…BackgroundStroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Additive exPlanations (SHAP) analysis to identify key predictive factors. …”
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Contrasting and Predicting Social Media’s Role in Addictive Use and Well-Being
Published 2025“…Competence in using social media significantly predicted both PSMU and SM-WB in the Arab sample. …”
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Hyperspectral-physiological based predictive model for transpiration in greenhouses under CO<sub>2</sub> enrichment
Published 2023“…The results demonstrated the inclusion of hyperspectral-based vegetation indices significantly increased the performance of the three machine learning models in predicting transpiration. …”
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Factors Determining SMEs Intention to Access Alternative Financing in Emerging Markets: Evidence from the UAE
Published 2023“…The research findings strongly support the significance of innovation, credit terms of alternative finance, visibility of alternative finance, and SME owner/manager financial literacy within the TOE framework in predicting the intention of SME owners/managers to utilize alternative finance. …”
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Machine learning-based prediction of one-year mortality in ischemic stroke patients
Published 2024“…</p><h3>Methods</h3><p dir="ltr">Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model’s outcomes and defined the influential predictive factors. …”