Airtime Credit Banking: From Two Applications to One Application

This paper proposes how airtime credit could be used for banking purposes. The aim is to provide a means of converting airtime credit of any network service provider to a credit alert for a particular bank account user. This paper shows a simple implementation of the proposed system. The advantage of the proposed system is that it allows customers the right to convert their purchased airtime credit to a credit alert at anytime when they no longer wish to use the airtime credit again. Furthermore, it explains the limitations of the proposed system considering regulations in different countries of deployment. This approach could be extended to cover other vouchers for banking applications as well.

Application of Machine Learning Techniques for Okra Shelf Life Prediction

The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.