Web Application Authentication Using Visual Cryptography and Cued Clicked Point Recall-based Graphical Password

Alphanumerical usernames and passwords are the most used computer authentication technique.This approach has been found to have a number of disadvantages.Users,for example,frequently choose passwords that are simple to guess.On the other side,if a password is difficult to guess,it is also difficult to remember.Graphical passwords have been proposed in the literature as a potential alternative to alphanumerical passwords,based on the fact that people remember pictures better than text.Existing graphical passwords,on the other hand,are vulnerable to a shoulder surfing assault.To address this shoulder surfing vulnerability,this study proposes an authentication system for web-applications based on visual cryptography and cued click point recall-based graphical password.The efficiency of the proposed system was validated using unit,system and usability testing measures.The results of the system and unit testing showed that the proposed system accomplished its objectives and requirements.The results of the usability test showed that the proposed system is easy to use,friendly and highly secured.

Student Performance Prediction Using A Cascaded Bi-level Feature Selection Approach

Features in educational data are ambiguous which leads to noisy features and curse of dimensionality problems.These problems are solved via feature selection.There are existing models for features selection.These models were created using either a single-level embedded,wrapper-based or filter-based methods.However single-level filter-based methods ignore feature dependencies and ignore the interaction with the classifier.The embedded and wrapper based feature selection methods interact with the classifier,but they can only select the optimal subset for a particular classifier.So their selected features may be worse for other classifiers.Hence this research proposes a robust Cascade Bi-Level(CBL)feature selection technique for student performance prediction that will minimize the limitations of using a single-level technique.The proposed CBL feature selection technique consists of the Relief technique at first-level and the Particle Swarm Optimization(PSO)at the second-level.The proposed technique was evaluated using the UCI student performance dataset.In comparison with the performance of the single-level feature selection technique the proposed technique achieved an accuracy of 94.94%which was better than the values achieved by the single-level PSO with an accuracy of 93.67%for the binary classification task.These results show that CBL can effectively predict student performance.