FACULTY OF PHYSICALSCIENCES

This study assessed the levels of selected heavy metals and polycyclic aromatic hydrocarbons (PAHs) in suya meat sold in Ugbowo, Benin City, to evaluate its safety for consumers. Ugbowo is a busy area with dense traffic and open-air vending that exposes food to contamination from vehicle emissions, dust, and smoke. Two composite suya samples were analyzed—one obtained from roadside vendors along major express routes and the other from residential areas including 19th Street, BDPA, and Edo Street in Ekosodin. The samples were oven-dried, homogenized, digested with nitric acid and hydrogen peroxide, and analyzed for heavy metals using Atomic Absorption Spectrophotometry (AAS). Polycyclic aromatic hydrocarbons (PAHs) were extracted and determined using Gas Chromatography–Mass Spectrometry (GC–MS). The AAS results showed the presence of lead (Pb), cadmium (Cd), copper (Cu), and nickel (Ni), while chromium (Cr) was below detection in both samples. Concentrations (mg/kg) for express road samples and residential sample were Pb (4.348, 5.002), Cr (<0.001, <0.001), Cu (1.635, 0.654), Cd (4.381, 3.988), and Ni (9.578, 5.230), all exceeding WHO/FAO permissible limits, indicating contamination likely from road dust, metal grills, or fuel combustion. GC–MS analysis revealed that all sixteen priority PAHs were below the detection limit (0.000 mg/kg) in both samples, suggesting minimal formation during grilling. Overall, the results indicate that while PAH contamination was negligible, heavy metal levels were significantly high, posing potential health risks to frequent consumers. The study recommends regular monitoring of street-vended foods and improved hygiene and roasting practices among suya vendors in Benin City.

The growing sophistication of phishing attacks continues to threaten digital security, causing financial losses, data compromise, and reduced trust in online platforms. Traditional blacklist and rule-based systems struggle to detect newly emerging phishing patterns, highlighting the need for adaptive, intelligent, and automated approaches. This study addresses this challenge by developing a machine learning-driven phishing vulnerability prediction model capable of accurately distinguishing phishing websites from legitimate ones. The objectives include dataset acquisition, feature preprocessing, model development, and performance evaluation. A labelled URL-based phishing dataset sourced from Kaggle was cleaned, encoded, normalized, and split using an 80:20 train-test ratio. Four supervised learning algorithms: Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost), were implemented and assessed using Accuracy, Precision, Recall, and F1-score. Among all models, XGBoost achieved the best performance with an Accuracy of 0.96, Precision of 0.97, Recall of 0.96, and an F1-score of 0.96, demonstrating strong predictive ability and robustness. Random Forest also performed well but with slightly lower scores. The final XGBoost model was deployed in a Flask-based web application to provide realtime phishing detection and user awareness. The findings confirm that machine learning, particularly ensemble-based methods, provides a scalable and highly effective approach to phishing vulnerability prediction. However, limitations include reliance on static URL features, dataset-specific bias, and the absence of behavioural or real-time network indicators. Future work should incorporate hybrid features, adversarial robustness testing, and explainable AI techniques to improve transparency and adaptability to evolving phishing attacks.