DETECTION

Wastewater generated from restaurants often contains a mixture of organic matter and microbial contaminants that may pose environmental and public health risks. This research focuses on the detection and isolation of Escherichia coli (E. coli) from wastewater collected from selected restaurants within the University of Benin, Benin City, Nigeria. For this study, wastewater samples were obtained from three restaurants: Helena’s Kitchen, Home and Away, and Buka— during peak operation hours. The samples were collected aseptically and analysed using standard microbiological methods. The pour plate technique was employed for total heterotrophic bacterial counts, while selective media such as Eosin Methylene Blue (EMB) agar were used for the isolation of E. coli. Biochemical tests including indole, methyl red, citrate, urease, and triple sugar iron (TSI) were used to confirm the isolates. The results showed high microbial loads across all samples, with E. coli being consistently present, indicating faecal contamination of the wastewater. The identification of other bacterial species suggest contamination from multiple sources such as food residues, human handling, and the environment. The findings reveal poor wastewater management and hygiene practices in the studied restaurants. In conclusion, the consistent presence of E. coli in restaurant wastewater signifies potential health and environmental hazards within the University of Benin. It is therefore recommended that wastewater from restaurants be regularly monitored, and that adequate sanitation infrastructure and treatment systems be put in place to prevent contamination and safeguard public health.

The exponential rise in digital banking transactions has heightened the risk of fraudulent and anomalous bank transfers, necessitating intelligent and automated mechanisms for its detection. Traditional rule-based systems often fail to capture evolving fraud patterns, motivating the adoption of machine learning-based anomaly detection techniques. This study aims to develop and evaluate robust unsupervised learning models for detecting anomalies in bank transaction datasets. Specifically, the research applies three state-of-the-art algorithms; Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM) to identify irregular transaction behaviors indicative of potential fraud. The methodology involves comprehensive data preprocessing, scaling, encoding, and feature selection to improve model learning. Real-world bank transfer datasets from kaggle were utilized, for this training. Each model’s performance was assessed using standard evaluation metrics, including; Silhouette Score, Anomaly Ratio, and Average Decision Score. Results show that OCSVM performed best (Silhouette = 0.635, strong decision scores), reliably flagging about 5% of records as anomalies. Analysis of flagged transactions revealed consistent patterns high transaction amounts or balance-change ratios, very short or very long intervals between transactions, and activity at unusual hours making the alerts interpretable and practically useful.