This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy, and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A simple matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded

The analysis of a large amount of data with high dimensionality of rows and columns increases the load of machine learning algorithms. Such data are likely to have noise and consequently, obstruct the performance of machine learning algorithms. Feature selection (FS) is one of the most essential machine learning techniques that can solve the above-mentioned problem. It tries to identify and eliminate irrelevant information as much as possible and only maintain a minimum subset of appropriate features. It plays an important role in improving the accuracy of machine-learning algorithms. It also reduces computational complexity, run time, storage, and cost. In this paper, a new feature selection algorithm based on feature stability and correlation is proposed to select the effective minimum subset of appropriate features. The efficiency of the proposed algorithm was evaluated by comparing it with other state-of-the-art dimensionality reduction (DR) algorithms using benchmark datasets. The evaluation criteria included the size of the minimum subset, the classification accuracy, the F-measure, and the area under curve (AUC). The results showed that the proposed algorithm is the pioneer in reducing a given dataset with high predictive accuracy.

Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attack based on clustering and classification ML methods (CMLM): Deep Learning (DL) and Decision Tree (DT). Simulation results show that the proposed technique achieves high percentage of accuracy in detecting phishing

Literature is focusing on identifying factors that influence students’ initial choice of major and few have studied students’ involvements after registration in a selected major and this study is one of the few. This study aims to determine the important factors that influence high school students’ choice of major based on data mining techniques. A questionnaire was designed to collect data from students in different universities in Kuwait and in different faculties such as science, literature, medicine and engineering. Rough set theory for feature selection was used to highlight and explain the significant factors related to students’ skills and preferences awareness as well as their experience reflection that are responsible for the development of their satisfaction with the choice of their university majors. The findings of the study revealed that the calculated reducts have a significant influence on the students’ choice of the university and collage major. This research contributes to literature by identifying the relationship between the conditional factors of the reduct (also known as the independent variables) and the classification attribute (also known as the dependent variable). The results of the study give valuable information to the high school students so they know the best majors which suite their skills, preference and experiences. This research also help students not to continually change their major because of the wrong choice of major they made which accordingly lead them to dissatisfaction of their major.