Enhancing The Randomness Of Symmetric Key Using Genetic Algorithm

The focus of network security is to provide the secure, effective and private communication between the sender and the receiver. To achieve the aim of high security of sending information, the improvement in cryptography is needed to make sure the protection of the information against unauthorized users. Symmetric-key cryptography satisfies the constraint of resources in computational complexity performances, but it offers weak security since it is not resilient against physical compromise. One of the way to overcome the issue is by providing a cryptographic key that is strong, hard to break and almost unpredictable by the intruder. As the advancement of technology in Artificial Intelligence (AI), Genetic Algorithm (GA) is implemented to generate the best-fit key in symmetric-key cryptography. Due to natural selection of GA process, the generated key is found to be the most random and non-repeating as possible. Moreover, the fitness test shows the average fitness value of a generated key increases when the key length increases

The most potential decision tree technique to classify the large dataset of students

Education is one of the important fields in this challenging world. The researchers come out with the new perceptive, which is learning analytics that is a new invention for helping out the instructors, learners, and administrators. The use of learning analytics can be the medium for increasing the productivity of education for producing capable leaders in the future. Machine learning comes out with any type of techniques such as Decision Tree, Support Vector Machine, Naïve Bayes, and Ensemble Classifiers. However, both Decision Tree and Ensemble Classifiers are chosen as the best potential machine learning techniques to cope with the large database of students. The Boosted Tree of Ensemble Classifiers managed to get 99.6% accuracy of training 378,005 data of students regarding the Virtual Learning Environment (VLE)