Forecasting the Academic Performance by Leveraging Educational Data Mining

The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.

Survey on Deep Learning Approaches for Detection of Email Security Threat

Emailing is among the cheapest and most easily accessible platforms,and covers every idea of the present century like banking,personal login database,academic information,invitation,marketing,advertisement,social engineering,model creation on cyber-based technologies,etc.The uncontrolled development and easy access to the internet are the reasons for the increased insecurity in email communication.Therefore,this review paper aims to investigate deep learning approaches for detecting the threats associated with e-mail security.This study compiles the literature related to the deep learning methodologies,which are applicable for providing safety in the field of cyber security of email in different organizations.Relevant data were extracted from different research depositories.The paper discusses various solutions for handling these threats.Different challenges and issues are also investigated for e-mail security threats including social engineering,malware,spam,and phishing in the existing solutions to identify the core current problem and set the road for future studies.The review analysis showed that communication media is the common platform for attackers to conduct fraudulent activities via spoofed e-mails and fake websites and this research has combined the merit and demerits of the deep learning approaches adaption in email security threat by the usage of models and technologies.The study highlighted the contrasts of deep learning approaches in detecting email security threats.This review study has set criteria to include studies that deal with at least one of the six machine models in cyber security.