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Analyzing the Influence of Climate and Anthropogenic Development on Vegetation Cover in the Coastal Ecosystems of GCC
Published 2025“…We used Landsat satellite imagery and a Random Forest classification algorithm to map various land cover classes along the GCC coastline. …”
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning
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Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems
Published 2021Get full text
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Metabolomics-based prediction model for diabetes: A comprehensive analysis of biomarkers and machine learning approaches
Published 2025“…Five machine learning models (Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Neural Network) were evaluated for their predictive performance using metrics including accuracy, precision, recall, F1 score, and ROC AUC.…”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning
Published 2024“…These extracted features are then transferred into a refined probabilistic feature set, upon which we construct an ensemble model leveraging the strengths of Logistic Regression (LR), Random Forest (RF), and Gaussian Naive Bayes (GNB) classifiers. …”
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Optimisation of PV Cleaning Practices: Comparison Between Performance Based and Periodic Based Approaches
Published 2018“…Solar energy has the largest untapped reserve in energy and is one of the fastest emerging energy markets. …”
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Effect of consanguinity on birth weight for gestational age in a developing country
Published 2007“…No significant difference was observed in the decrease in birth weight between the first- and second-cousin marriages. …”
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AI-based remaining useful life prediction and modelling of seawater desalination membranes
Published 2024Get full text
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β-2-himachalen-6-ol protects against skin cancer development in vitro and in vivo
Published 2017“…Also, there was a significant decrease in p-Erk and p-Akt protein levels. …”
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Renewable Energy, Coal as a Baseload Power Source, and Greenhouse Gas Emissions: Evidence from U.S. State-Level Data
Published 2017“…After controlling for other sources of emissions, U.S. states that produce a larger share of renewable energy are found to have lower GHG emissions. …”
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Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
Published 2022“…A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. …”
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Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset
Published 2024“…To overcome these limitations, we have developed Ensemble-Guard IoT; an innovative ensemble model combining Gaussian Naive Bayes (GNB), Logistic Regression (LR) and Random Forest (RF) through soft voting classifiers. …”
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Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context
Published 0024“…Various ML algorithms, such as Support Vector Machines, Logistic Regression, K-Nearest Neighbors, Naïve Bayes, Decision Trees, Random Forest, AdaBoost, Bagging, and Artificial Neural Networks are applied to predict student performance. …”
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Effects of habitat complexity on the abundance, species richness and size of darkling beetles (Tenebrionidae) in artificial vegetation
Published 2016“…Increasing complexity appeared to lead to decreasing beetle widths. 9 beetle species were relatively rare in, or absent from, the higher complexity treatments, including 5 of the 6 largest species. 2 rare, small beetle species were only found in higher complexity treatments. …”
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Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic
Published 2025“…</p><h3>Methods</h3><p dir="ltr">Using Python® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. …”
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Predictive factors for giftedness among Syrian refugee students: A focus on academic achievement, gender, and school context
Published 2023“…A dataset comprising 13,598 students assessed using the Arabic version of the HOPE Teacher Rating Scale was analyzed. Logistic regression and random forest analyses examined the influence of GPA, gender, school stage (elementary, middle, secondary), and school location (in-camp and out-of-camp) on identification patterns. …”
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Explainable phishing website detection for secure and sustainable cyber infrastructure
Published 2025“…Then, the models, namely support vector machine (SVM), random forest (RF), decision tree (DT), logistic regression(LR), and K-nearest neighbor, were trained and tested. …”
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Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort
Published 2024“…Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. …”
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Stemming cardiovascular diseases in Qatar
Published 2015“…“Both Qatari nationals and expatriates should adopt healthier lifestyles to reduce the prevalence of these risk factors,” says Christos. Diabetes was the largest preventable risk factor to decrease heart attacks and strokes.…”