Cryptography has been widely used as a mean to secure message communication. A cryptosystem is made up of a publicly available algorithm and a secretly kept key. The algorithm is responsible for transforming the original message into something unintelligible. The result of losing the key or cracked algorithm can be catastrophic, where all secret communications will be known to adversaries. One way to find the key is by brute-force attacks which try every possible combination of keys. The only way to prevent this is by having the key of sufficiently large enough such that finding the right key cannot be made in a reasonable time frame. However, large key size imposes extra computational works which result in larger energy consumption and thus more heat dissipation to the environment. Therefore, the selection of key size does not only depends on the required security level, but also factors such as the ability of the processor and the available memory resources. The advent of multi-core technology promises some improvements in the utilization of computational resources. Many reports support the idea that multi-core technology brought a significant improvement over the single core technology. In this study, we investigate this hypothesis on the RSA cryptosystem in relation to the key size. Earlier studies reported multi-core efficiency in normal applications, but the question arises if multi-core architecture remains superior to a single core architecture when dealing with applications involving large integers. From our experimentation, we observe that the higher the number of cores, the better the performance of the encryption and decryption processes. The quad-core technology can smoothly handle operations involving 8192 bits key.

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Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.

Customer care plays an important role in a company especially in Telecommunication Company. Churn is perceived as the behaviour of a customer to leave or to terminate a service. This behaviour causes the loss of profit to companies because acquiring new customer requires higher investment compared to necessary to consider an efficient classification model to reduce the rate of churn. Hence, the purpose of this paper is to propose a new classification model based on the Rough Set Theory to classify customer churn. The results of the study show that the proposed Rough Set classification model outperforms the existing models and contributes to significant accuracy improvement.

This paper analyses the performance of classification models using single classification and combination of ensemble method, which are Breast Cancer Wisconsin and Hepatitis data sets as training datasets. This paper presents a comparison of different classifiers based on a 10-fold cross validation using a data mining tool. In this experiment, various classifiers are implemented including three popular ensemble methods which are boosting, bagging and stacking for the combination. The result shows that for the classification of the Breast Cancer Wisconsin data set, the single classification of Naïve Bayes (NB) and a combination of bagging+NB algorithm displayed the highest accuracy at the same percentage (97.51%) compared to other combinations of ensemble classifiers. For the classification of the Hepatitisdata set, the result showed that the combination of stacking+Multi-Layer Perception (MLP) algorithm achieved a higher accuracy at 86.25%. By using the ensemble classifiers, the result may be improved. In future, a multi-classifier approach will be proposed by introducing a fusion at the classification level between these classifiers to obtain classification with higher accuracies.