In this study, the three-shape parameter modified exponential, also called the Kumaraswamy Modified Fréchet Exponential, is developed to ‎model time to pain relief. Some important properties of this new model are derived: These include the moments, moment generating function, ‎inequality measures, and mean residual life. The parameter estimates are computed under the maximum likelihood estimator. A simulation study ‎is carried out to investigate the behavior of parameters under the maximum likelihood estimation method. The quantile function is used to generate random numbers for the simulation study. We applied the Monte-Carlo simulation technique. The results show that the parameters with ‎their average bias (AB) and root mean square error (RMSE) decreased alongside increasing sample sizes: 50, 150, 250, 500, and 1000. ‎The results show that the three-shape parameter modified exponential behaves well with the maximum likelihood estimator. The three-‎shape parameter modified exponential model is applied to time-to-pain relief data of patients who received analgesic (pain killer medication). ‎Smaller Akaike and Bayesian Information criteria values are achieved better than all the competitive models; thus, the three-shape parameter ‎modified exponential model demonstrates a better fit for the data used‎.

Given the rising prevalence of obesity and its associated health complications, including metabolic and cardiovascular disorders, this study ‎addresses a critical research gap concerning the most reliable indices that support Body Mass Index (BMI) for early obesity screening ‎among young adults. The objectives examine students’ sociodemographic characteristics and anthropometric parameters, explore correlations among obesity indices, evaluate sensitivity and specificity in predicting obesity, and identify the most effective indicators by gender ‎and age group. A cross-sectional research design is adopted, involving a sample of 219 students selected through stratified random sampling. Data is collected using structured questionnaires and physical measurements, and analyzed through descriptive and inferential statistics and Receiver Operating Characteristic (ROC) analysis. The findings reveal that in terms of support for Body Mass Index (BMI) in ‎screening for obesity, the Body Adiposity Index (BAI) and Waist-to-Height Ratio (WaHtR) exhibit strong correlations and high diagnostic ‎accuracy, whereas the Waist-to-Hip Ratio (WaHpR). The study concludes that a combined use of BAI, WaHtR, BMI, and CI tailored to ‎gender differences offers a robust method for early obesity detection. It is recommended that universities institutionalize routine health ‎screenings utilizing these indices, enhance awareness of obesity-related risks, and promote healthy lifestyle behaviors among students to ‎curb the increasing burden of obesity‎.

The contribution of Foreign Direct Investment (FDI) to economic growth remains a central issue in development economics, particularly for ‎emerging economies that are undergoing macroeconomic adjustment. This study examines the relationship between FDI and Ghana’s ‎Gross Domestic Product (GDP) growth by conducting a comparative assessment of Ordinary Least Squares Regression (OLSR) and Support Vector Regression (SVR). Annual data spanning 1996–2023 were obtained from the World Bank and OECD national accounts databases. Model performance was evaluated using out-of-sample Root Mean Square Error (RMSE) and coefficient of determination (R²) to ‎distinguish predictive accuracy from explanatory power. The results indicate that FDI exerts a statistically significant and positive effect on ‎GDP growth under the OLSR framework, with an R² value of 0.47, suggesting moderate explanatory strength. In contrast, the SVR model ‎, implemented with a radial basis function kernel and tuned via cross-validation, achieved a marginally lower prediction error (RMSE = 0.754) ‎, but lower explanatory power (R² = 0.32). These findings highlight a clear trade-off between predictive accuracy and interpretability. The study emphasizes that the single-predictor specification serves a methodological purpose rather than a comprehensive economic representation, and that results should be interpreted within this constrained framework. Generally, the analysis shows the complementary roles of machine ‎learning and econometric approaches in applied economic modeling and provides evidence-based guidance for model selection in macroeconomic forecasting and policy analysis in Ghana‎.

Selecting a machine learning algorithm with optimal precision, accuracy, and recall has been a major challenge in cybersecurity and digital ‎forensic analysis. Common challenges include difficulty in impact visualization, deterioration in efficiency when datasets are large, the discretized nature of datasets, complex relationships among variables, linearity assumptions, overfitting, and other related issues. In an attempt ‎to mitigate these challenges in practice, this study aims to compare the classification performance of machine learning algorithms applied to ‎web browser extracts in digital forensics. Consequently, the most efficient algorithm is proposed for forensic analysis. The study utilized ‎data from 20 computers, each installed with web browsers including Microsoft Internet Explorer, Microsoft Edge, Google Chrome, Mozilla ‎Firefox, and Opera. Browser extracts were obtained using the Web Browser Forensic Analyzer (WEFA) tool (version 1.2). Browser artefacts were extracted and categorized into history, cache, cookies, typed URLs, sessions, most visited sites, screenshots, downloaded files, ‎favorites, bookmarks, and thumbnails. The dataset consisted of counts of artefacts extracted from the browsers. Data collection was further ‎supported by Firefox Forensic Analyzer and Google Chrome Analyzer tools. The Python programming language was used as the primary ‎tool for implementing and evaluating the performance of the machine learning algorithms. During the implementation process, the study ‎assessed the performance of the Linear Discriminant Algorithm against five competing classification algorithms: Logistic Regression, Decision Tree Classifier, K-Nearest Neighbors, Naive Bayes Classifier, and Support Vector Classifier. The findings revealed that the Linear ‎Discriminant Algorithm outperformed the competing algorithms in terms of accuracy, precision, recall, and F1-score. The study therefore ‎concludes that the Linear Discriminant Algorithm is an enhanced and effective approach for classifying browser extracts (artefacts) in digital ‎forensic investigations‎.