M-Banking is a mobile technology innovation that is compatible with banking financial services. This study aims to determine the factors that affect the customer's acceptance of using mobile banking services. The UTAUT model was used in this study. The data collection process was carried out through a hard copy and electronic questionnaire through which 318 usable answers were collected from users of mobile banking services in the Qassim region in the Kingdom of Saudi Arabia. The study results indicate that there is a statistically significant relationship between performance expectancy and behavioral intention to use. The results, statistically speaking, demonstrate the factors that affect the customer's adoption of mobile banking services. The performance expectancy factor was the most influencing factor on the intention to use mobile banking services in Qassim.

The graduation projects (GP) are important because it reflects the academic profile and achievement of the students. For many years’ graduation projects are done by the information technology department students. Most of these projects have great value, and some were published in scientific journals and international conferences. However, these projects are stored in an archive room haphazardly and there is a very small part of it is a set of electronic PDF files stored on hard disk, which wastes time and effort and cannot benefit from it. However, there is no system to classify and store these projects in a good way that can benefit from them. In this paper, we reviewed some of the best machine learning algorithms to classify text “graduation projects”, support vector machine (SVM) algorithm, logistic regression (LR) algorithm, random forest (RF) algorithm, which can deal with an extremely small amount of dataset after comparing these algorithms based on accuracy. We choose the SVM algorithm to classify the projects. Besides, we will mention how to deal with a super small dataset and solve this problem.