Analyzing the Influence of Climate and Anthropogenic Development on Vegetation Cover in the Coastal Ecosystems of GCC by Abhilash Dutta Roy (22466830)

“…We used Landsat satellite imagery and a Random Forest classification algorithm to map various land cover classes along the GCC coastline. …”

Decreased Analgesic Requirements in Super Morbidly Versus Morbidly Obese Patients Undergoing Laparoscopic Sleeve Gastrectomy by Hamed Elgendy (14148846)

L1-CAM knock-down radiosensitizes neuroblastoma IMR-32 cells by simultaneously decreasing MycN, but increasing PTEN protein expression by Rached, Johnny

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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) by Khaled F. Alkaraki (22051967)

“…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. …”

Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning by Far, Reza Davoodi

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Assessment and Performance Analysis of Machine Learning Techniques for Gas Sensing E-nose Systems by Mahmood, Lubna Syeda

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Metabolomics-based prediction model for diabetes: A comprehensive analysis of biomarkers and machine learning approaches by Doaa Farid (22565300)

“…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.…”

Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram (22224541)

“…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. …”

Visitors off the trail: Impacts on the dominant plant, bryophyte and lichen species in alpine heath vegetation in sub-arctic Sweden by Monika, Rawat

“…With a greater decrease in taller forbs and shrubs than in graminoids and prostrate plants, a greater decrease in lichen than in bryophyte species, and a change in vegetation composition. …”
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AI-based remaining useful life prediction and modelling of seawater desalination membranes by Al Ali, Fajer

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Effect of consanguinity on birth weight for gestational age in a developing country by Wakim, Gerard

“…No significant difference was observed in the decrease in birth weight between the first- and second-cousin marriages. …”
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Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM by Tadesse G. Wakjira (14779165)

“…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. …”

Ensemble-Guard IoT: A Lightweight Ensemble Model for Real-Time Attack Detection on Imbalanced Dataset by Muhammad Usama Tanveer (22225360)

“…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. …”

Predicting and Interpreting Student Performance Using Machine Learning in Blended Learning Environments in a Jordanian School Context by SALIM, MAHA JAWDAT

“…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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Predictive modelling in times of public health emergencies: patients’ non-transport decisions during the COVID-19 pandemic by Hassan Farhat (9000509)

“…</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. …”

Predictive factors for giftedness among Syrian refugee students: A focus on academic achievement, gender, and school context by Ali M. Alodat (22508036)

“…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. …”

Explainable phishing website detection for secure and sustainable cyber infrastructure by Tanzila Kehkashan (20748842)

“…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. …”

Optimizing the number of players and training bout durations in soccer small‐sided games: Effects on mood balance and technical performance by Zouhaier Farhani (22330693)

“…Therefore, coaches should consider longer continuous bouts when planning SSGs‐based training to significantly decrease TMD and enhance technical‐tactical performance in soccer SSGs.…”

Study of the Impact of Land Use and Land Cover (LULC) Changes on local climate in Six Mega Global Cities in the 21st Century by Sabobeh, Lara Ali

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Peripheral inflammatory and metabolic markers as potential biomarkers in treatment-resistant schizophrenia: Insights from a Qatari Cohort by Mohamed Adil Shah Khoodoruth (14589828)

“…Linear regression analysis revealed that MLR and clozapine treatment were significantly correlated with the severity of schizophrenia symptoms. …”