Aim:The main motto of the study is to detect the frauds in mobile money transactions using logistic regression and random forest algorithms and comparing their accuracy. Materials and Methods: Logistic regression (N=10) and random forest algorithm(N=10) was iterated 20 times and detected the frauds. Results and Discussion: Random forest has significantly better accuracy (99.6%) compared to logistic regression (92.6%). The statistical significance of random forest algorithm (p<0.018 Independent sample T-test) is high. Conclusion: Within the limits of this study, random forest algorithm offers better accuracy to detect frauds in mobile money transactions.

Aim:The main motto of the study is to optimize the large volume of data using data placement algorithm and online community adjustment algorithm and comparing their accuracy. Materials and Methods: Data placement algorithm (N=10) and online community adjustment (N=10) was iterated 20 times to optimize the data. Result and Discussion: Data placement algorithm has significantly better accuracy (85%) compared to online community adjustment algorithm(78%). The statistical significance of data placement (p<0.02 independent sample test) is high. Conclusion: With the limits of the study, a data placement algorithm with product manufacturing data offers the best accuracy in data optimization.