The internet has rapidly evolved in its communication, commerce and information sharing making it a huge platform for cyber threats, particularly malicious URLs. They pose a serious threat to individuals and to organisations. Phishing attacks, malware distribution and other types of cybercrime frequently are carried out through malicious URLs. In this research, we have created and tested the machine learning models to detect malicious URLs. The labeled URLs used were obtained from a public dataset with more than 651,000 labeled URLs. The dataset was prepared for classification by applying data pre-processing techniques like stratified sampling, label encoding and Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To train and test the algorithms, five machine learning were used: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), which were trained and evaluated by the metrics of accuracy, precision, recall and F1 score. The results indicated that the Random Forest model had the highest classification accuracy (95%) as compared to the other models. Moreover, a web based malicious URL detection system was developed to demonstrate the actual application of the developed models in real time cyber security scenarios. The study provides a conclusion that the machine learning techniques, particularly ensemble learning techniques can be considered an effective and reliable technique to detect malicious URL.
Oludele Adeleke, Jimoh Abdulhakeem Kuranga, Samuel Adeolu Ogunbiyi et al.· American Journal of Interdis...· 0 citations
Air pollution remains one of the major concerns of health crisis in most developing countries with Africa facing a silent health crisis as air pollution worsens, yet predictive tools remain scarce. Pollutants such as PM2.5, NO₂, CO, and O₃ increase the risk of respiratory and cardiovascular diseases. This study develops a Machine Learning (ML) model to predict the Air Quality Index (AQI) and assess health risks across urbanized, industrialized, and rural regions using climatic parameters. A quantitative approach was applied to 23,463 AQI datasets obtained from Kaggle World AQI database. The data was pre-processed and feature engineering was used to remove null values and outliners then splitted into ratio 70:30 for training and testing. Four algorithms namely; Linear Regression, k-Nearest Neighbours, Decision Tree and Random Forest were evaluated using metrics such as R-Squared (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The result shows that Random forest model (R2 = 0.997316, RMSE = 2.865823, MAE = 0.2955499) demonstrated superior predictive performance followed by KNN (R2 = 0.996820, RMSE = 3.119500, MAE = 0.588252) while Decision Trees (R2 = 0.995046, RMSE = 3.893819, MAE = 0.302845) produced high accuracy with slightly higher error. SVR (R2 = 0.980160, RMSE = 7.792268, MAE = 1.301302) and Linear Regression (R2 = 0.975279, RMSE = 8.968122, MAE = 4.831951) showed moderate accuracy. This research confirms that Machine Learning models are valuable tools for predicting Air quality thus offering a powerful tool for mitigating the impact of deteriorating Air Quality in Africa.
O. A. Oduah, Oluseyi E. Ogunsola, Oludele Adeleke· Nigerian Journal of Physics· 0 citations
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