This paper investigates the use of fractal geometry for analyzing ECG time series signals. A technique of identifying cardiac diseases is proposed which is based on estimation of Fractal Dimension (FD) of ECG recordings. Using this approach, variations in texture across an ECG signal can be characterized in terms of variations in the FD values. An overview of methods for computing the FD is presented focusing on the Power Spectrum Method (PSM) that makes use of the characteristic of Power Spectral Density Function (PSDF) of a Random Scaling Fractal Signal. A 20 dataset of ECG signals taken from MIT-BIH arrhythmia database has been utilized to estimate the FD, which established ranges of FD for healthy person and persons with various heart diseases. The obtained ranges of FD are presented in tabular fashion with proper analysis. Moreover, the experimental results showing comparison of Normal and Abnormal (arrhythmia) ECG signals and demonstrated that the PSM shows a better distinguish between the ECG signals for healthy and non-healthy persons versus the other methods.

There are many models in the literature that consider the main characteristics of Internet traffic either by processing the real measurements of traffic in the time-or frequencydomain. In this paper we use the latter approach and introduce a novel method to capture the fractal behavior of Internet traffic in which we adopt a random scaling fractal model to simulate the self-affine characteristics of the Internet traffic. In this paper we utilize the self-affine nature of Internet traffic in order to disguise the transmission of a digital file by splitting the file into a number of binary blocks (files) whose size and submission times are compatible with the bursty lengths of Internet traffic. We shall examine two different time series. The Sizes series consists of the actual packet sizes as individual packet arrive, and the inter-arrival series consists of timestamps differences between consecutive packets.

Conventionally, the reliability of a web portal is validated with generalized conventional methods, but they fail to provide the desired results. Therefore, we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability testing. Actually, the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient way. In this paper, we employ testing profiles to measure the reliability through software operational profile, input space profile and usability profile along with qualitative measures of reliability and usability. Moreover, the case study used for verification is based on a web application that facilitates information and knowledge sharing among its online members.The proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and reliability. The final results unveil that the computation of reliability by using the traditional method (utilizing failure points with the assistance of Mean Time Between Failures (MTBF) and Mean Time To Failure (MTTF) becomes ineffective under certain situations. Under such situations, the proposed scheme helps to compute the reliability in an effective way. Moreover, the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

As smartphones have become a part of our daily lives, including payment and banking transactions; therefore, increasing current data and privacy protection models is essential. A continuous authentication model aims to track the smartphone user's interaction after the initial login. However, current continuous authentication models are limited due to dynamic changes in smartphone user behavior. This paper aims to enhance smartphone user privacy and security using continuous authentication based on touch dynamics by proposing a framework for smartphone devices based on user touch behavior to provide a more accurate and adaptive learning model. We adopt a hybrid model based on the Hyper Negative Selection Algorithm (HNSA) as an artificial immune system (AIS) and the random forest ensemble classifier to instantly classify a user behavior. With the new approach, a decision model could detect normal/abnormal user behavior and update a user profile continuously while using his/her smartphone. The proposed approach was compared with the v-detector and HNSA, where it shows a high average accuracy of 98.5%, a low false alarm rate, and an increased detection rate. The new model is significant as it could be integrated with a smartphone to increase user privacy instantly. It is concluded that the proposed approach is efficient and valuable for smartphone users to increase their privacy while dynamic user behaviors evolve to change.

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