Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis by Md Arifuzzaman (11471123)

“…Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. …”

Modelling the utilization rates of pedestrian crosswalks by Walid Abdullah, Al Bargi

“…This study uses a Regression Model Techniques to analyse factors influencing utilization rate of pedestrian zebra crossing. …”
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Data-driven discovery of Tsallis-like distribution using symbolic regression in high-energy physics by Nour Makke (19160749)

“…We introduce a groundbreaking application of SR on actual experimental data with an unknown underlying model, representing a significant departure from previous applications, which are primarily limited to simulated data. …”

Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

“…Our present work first fills the gap of dRVFL and edRVFL work in the field of regression. We test and evaluate the performances of the dRVFLs on regression problems. …”

The Grid Iron Patterns & Detached Villas as the Main Drivers of Sharjah City’s Urban Sprawl by MOHAMED, SHAMSELDIN SAEED

“…On the other hand, the same study proved that the grid system and the villa model are the main reason behind the urban sprawl of the city. …”
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Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning by Far, Reza Davoodi

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Modelling the utilization rates of pedestrian crosswalks by Walid Abdullah Al Bargi (17017764)

“…This study uses a Regression Model Techniques to analyse factors influencing utilization rate of pedestrian zebra crossing. …”

Kernel-Ridge-Regression-Based Randomized Network for Brain Age Classification and Estimation by Raveendra Pilli (21633287)

Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis by Rana Jawarneh (17746953)

“…Pearson correlation and Bootstrap analysis) and advanced geostatistical models, including Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR), to analyze and monitor the spatial and seasonal variations of water and electricity consumption. …”

Interpretable scientific discovery with symbolic regression: a review by Nour Makke (19160749)

“…In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. …”

Modeling of photovoltaic soiling loss as a function of environmental variables by Wasim Javed (6105866)

“…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. …”

A flexible genetic algorithm-fuzzy regression approach for forecasting: The case of bitumen consumption by Azadeh, Ali

“…Moreover, the fuzzy regression (FR) model is used for estimation. Analysis of variance (ANOVA) is used for selecting among GA, FR or conventional regression (CR). …”
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A cross-cultural study of high-altitude botanical resources among diverse ethnic groups in Kashmir Himalaya, India by Shiekh Marifatul Haq (8762763)

“…The overall trends between the indicator values and the plant species used by diverse ethnic groups were illustrated using the linear regression model.</p><h3>Results</h3><p dir="ltr">We recorded 46 species belonging to 25 different families used by the local people of the Kashmir Valley belonging to four ethnic groups (Gujjar, Bakarwal, Pahari, and Kashmiri). …”

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.…”

Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar by Amith Khandakar (14151981)

“…Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. …”

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach by Majedeh Gheytanzadeh (17541927)

“…<p dir="ltr">Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. …”

Investigating the Effect of Patient-Related Factors on Computed Tomography Radiation Dose Using Regression and Correlation Analysis by AlShurbaji, Mohammad

“…Moreover, the study investigated the correlation between the different CT dose indices. Using linear regression models and Pearson correlation, the study found that all CT dose indices correlate with BMI and weight in all CT exams with varying degrees as opposed to age, which did not demonstrate any significant correlation with any of the CT dose indices across all CT exams. …”
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Preoperative Bevacizumab Does Not Significantly Increase Postoperative Complication Rates in Patients Undergoing Hepatic Surgery for Colorectal Cancer Liver Metastases by Kesmodel, Susan

“…Univariate and multivariate logistic regression models were used to evaluate the association of patient and tumor characteristics, neoadjuvant therapy, and operative factors with postoperative complications. …”
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The epidemiology of hepatitis C virus in Central Asia: Systematic review, meta-analyses, and meta-regression analyses by Welathanthrige S. P. Botheju (18618643)

“…Meta-analyses were performed using DerSimonian-Laird random-effects models with inverse variance weighting. Random-effects meta-regression analyses were performed on general population studies. …”

The effect of microbiome-modulating therapeutics on glucose homeostasis in metabolic syndrome: A systematic review, meta-analysis, and meta-regression of clinical trials by Ridhima Kaul (12485902)

“…We pooled data using random effects meta-analyses, reporting them as mean differences (MDs) with 95 % confidence intervals (CIs), and conducting univariate linear model meta-regressions. </p><h3>Results</h3><p dir="ltr">Data from 21 trial comparisons across 19 studies (n = 911) revealed that, compared to placebo/control, microbiome-modulating therapies were associated with statistically significant changes in fasting <u>plasma glucose</u> (MD: 4.03 mg/dL [95%CI: 6.93; −1.13]; p_effect = 0.006, I² = 89.8 %), and fasting insulin (MD: 2.56 μU/mL [95%CI: 4.28; −0.84]; p_effect = 0.004, I² = 87.9 %), but not insulin resistance or sensitivity indices and <u>HbA1c</u>. …”