Drug Recommender Systems (DRS) streamline prescription process and contribute to better healthcare. Hence, this study developed a DRS that recommends appropriate drug(s) for the treatment of an ailment using Peptic Ulcer Disease (PUD) as a case study. Patients’ and drug data were elicited from MIMIC-IV and Drugs.com, respectively. These data were analysed and used in the design of the DRS model, which was based on the hybrid recommendation approach (combining the clustering algorithm, the Collaborative Filtering approach (CF), and the Knowledge-Based Filtering approach (KBF)). The factors that were considered in recommending appropriate drugs were age, gender, body weight, allergies, and drug interactions. 014The model designed was implemented in Python programming language with the Flask framework for web development and Visual Studio Code as the Integrated Development Environment. The performance of the system was evaluated using Precision, Recall, Accuracy, Root Mean Squared Error (RMSE) and usability test. The evaluation was carried out in two phases. Firstly, the CF component was evaluated by splitting the dataset from MIMIV-IV into a 70% (60,018) training set and a 30% (25,722) test set. Secondly, the KBF component was evaluated using 30 different cases. The evaluation for this was computed manually by comparing the recommendation results from the system with those of an expert. Firstly, the CF component was evaluated and had a precision score of 85.48%, a recall score of 85.58%, and a RMSE score of 0.74. Secondly, the KBF component was evaluated by using 30 different cases. This resulted in a precision of 77%, a recall of 83%, an accuracy of 81% and an RMSE of 0.24. The results from the usability test showed a high percentage of performance of the system. The addition of the KBF reduced the error rate between actual recommendations and predicted recommendations. So, the system had a high ability to recommend appropriate drug(s) for PUD.

Online stores have integrated one or more payment gateways on their website to facilitate the transaction of goods and services. In the Nigerian case, many physical stores are moving online. However, there is a gap in the payment methods which have slowed down business due to limited payment methods, charges incurred on gateways, and insecurity of websites. This paper is tailored towards the security of online transactions, reduced credit card usage and yet accentuation of cashless policy in Nigeria. Inline with the above stated, this paper proposed a secured digital wallet using cryptography to proffer a solution to exposing confidential information to different sites and curb fraudulent actions. By means of qualitative and quantitative research approach, data on user transactions were collected through google survey forms on how often they make transactions, 91.3% shop online but only 48.1% shop very often and 55% of sample questioned security of online payment, from the quantitative facts above, a larger percent of people who uses financial tools in their shopping activities have more security concern which this study solely aim at. In response to the corncern in the preceeding clauses, a mathematical model was formulated to generate keys for the wallet using an asymmetric cryptography scheme to improve the security process while using an online service. Furthermore, wallet referred in this paper uses the fund preload techniques which limits how often users needs to swipe their credit card for an handshake with the commercial banks, the end goal thus strengthen the Nigeria cashless policy. This paper generated encryption and decryption keys from the extension of the Euclid theorem and implemented a system that can generate keys for fund and asset transfer with the crypto service. The system was tested using unit testing and alpha and beta testing to assert the aims of the research. The result showed that digital wallet usage reduces the exposure of sensitive information on funds and allows using the account on different sites.