Abstract
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.
Highlights
IntroductionThat is so old way and takes a lot of effort and time is not with the fact, we are a computer college
In many years the Information technology department of the girl’s section at Qassim University doing many graduation projects
To solve the problem of increasing the amount of data and determining massively dispersed data, here is a comparison between four modules to determine the efficiency (Hierarchical Clustering, Dewey Decimal Classification (DDC): Dewey decimal classification, k-Mean Clustering, and SVM: Support Vector Machine), The results pointed out that DDC offer to the most accuracy (75.02%), followed by the Hierarchical models (74.66%), while both K-Mean and SVM offer to the similar accuracy (72.66%)
Summary
That is so old way and takes a lot of effort and time is not with the fact, we are a computer college. This way of dealing with graduation projects deprived us of know the future direction of the IT department and their alignment with the 2030 vision on the offered projects ideas and it is with trending of the technical world or not. Which makes the process of taking benefit from projects difficult To solve this problem, we need to create an ML system that allows us to analyze and classified graduation projects. The result well show is the idea of a graduation project it is with trending of the technical world or not is it with the vision 2030 of Saudi Arabia
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