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Recent years have witnessed a revolution in the social communication. Nowadays, millions of people are connected through different kinds of social networks where they discuss multiple aspects of their life including business, economical, social, religious, educational, and political subjects. These interactions have a direct effect on attitudes in many communities. In the past few years, social networks in Iraq have experienced the sectarian war among the major Iraqi sects (e.g., Sunni, Shia, and Kurd). After the war against ISIS, it can be argued that Iraqis have changed their attitude on social networks (and in the real world). Therefore, in this article, we will investigate the contents of Iraqi's most popular Facebook pages and see if ideology has changed over the past few years. We also aim to reveal untitled current patterns of interaction among people in their respective provinces. Moreover, we survey the most frequent topics discussed by Iraqi Facebook users, and find that attitudes have changed for the better through discussions on social networks since the war. Key words: Iraqi social settings, social behavior, sectarian war, Introduction With the advent of social networks, societies have become more vulnerable to different kinds of sensitive issues (1). Nowadays, social networks act as a bridge between the real and [...]

The area of forgery detection of documents is considered an active field of research in digital forensics. One of the most common issues that investigators struggle with is circled around the selection of the approach in terms of accuracy, complexity, cost, and ease of use. The literature includes many approaches that are based on either image processing techniques or spectrums analysis. However, most of the available approaches have issues related to complexity and accuracy. This article suggests an unsupervised forgery detection framework that utilizes the correlations among the spectrums of documents' matters in generating a weighted network for the tested documents. The network, then, is clustered using several unsupervised clustering algorithms. The detection rate is measured according to the number of network clusters. Based on the obtained results, our approach provides high accuracy using the Louvain clustering algorithms, while the use of the updated version of the DBSAN was more successful when testing many documents at the same time. Additionally, the suggested framework is considered simple to implement and does not require professional knowledge to use. [ABSTRACT FROM AUTHOR]

The human genome is a set of humans' nucleic acid sequences that are encoded as DNA in the human chromosome pairs. These are usually treated separately as the nuclear genome and the mitochondrial genome. Studying and understanding genetic systems has a great impact on health. Therefore, in the last few decades, the world has witnessed a great revolution in genetic engineering that aims to identify new phenomena and support mitigating the effect of diseases or even find ultimate solutions. The first step in studying genetics data is modelling this data and use some analysis approaches. The main problem of researchers is finding appropriate approaches for modelling their genetics data. This work comes to review the literature and present a variety of approaches used by researchers in modelling genetics data and genes interactions. This review tries to make it easy for researchers when adopting particular modelling approaches by presenting the state-of-the-art in terms of the dataset used, strengths, and limitations. [ABSTRACT FROM AUTHOR]