Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

     In this paper, the botnet detection problem is defined as a feature selection problem and the genetic algorithm (GA) is used to search for the best significant combination of features from the entire search space of set of features. Furthermore, the Decision Tree (DT) classifier is used as an objective function to direct the ability of the proposed GA to locate the combination of features that can correctly classify the activities into normal traffics and botnet attacks. Two datasets  namely the UNSW-NB15 and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017), are used as evaluation datasets. The results reveal that the proposed DT-aware GA can effectively find the relevant

     In the task of detecting intrinsic plagiarism, the cases where reference corpus is absent are to be dealt with. This task is entirely based on inconsistencies within a given document. Detection of internal plagiarism has been considered as a classification problem. It can be estimated through taking into consideration self-based information from a given document.

The core contribution of the work proposed in this paper is associated with the document representation. Wherein, the document, also, the disjoint segments generated from it, have been represented as weight vectors demonstrating their main content. Where, for each element in these vectors, its average weight has been considered instead of its frequency.

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The subject matter of the article Prediction of COVID-19 disease and infection rate based on a pre-trained model that supports deep learning. The goal is to build a system to diagnose people as infected or not with covid disease with the percentage of infection and the affected site and to present it with interactive interfaces to facilitate the use of the system for anyone not specialized in the software field. The task is to detect or predict the Coronavirus that affects the airways, lungs, and breathing. It is the cause of many deaths and is still in the process of transformation and development, but with less media exposure. From this standpoint, a medical system was proposed to detect the presence of the Coronavirus in the lung based o

    The examination of gills of the common carp Cyprinus carpio revealed the presence of two species of the family Trichodinidae belonging to the genus Dipartiella (Raabe, 1959) Stein, 1961 namely D. indiana Saha and Bandyopadhyay, 2017 and D. kazubski Mitra and Bandyopadhyay, 2009 for the first time in Iraq from Al-Graiat location on the Tigris River at Baghdad city. This also represents the first record of the genus Dipartiella from fishes of Iraq. The descriptions and measurements of these two parasite species as well as their illustrations were given.

The Zubair Formation is the most significant sandstone reservoir in Iraq which deposited during the Barremian. The study area is located in the central part of Iraq at the Kifl oil field, in the northern part of the Mesopotamian Zone.

The petrographic study showed that quartz mineral is the main component of the sandstone in Zubair Formation with very low percentage of feldspar and rare rock fragments to classified as quartz arenite sandtone. There are five lithologic changes (lithofacies) that have characterized the studied succession: - well sorted quartz arenite, poorly sorted quartz arenite, poorly sorted graywacke, sandy shale, and shale.  These lithofacieses were deposited in the deltaic environments as three associate

Shot boundary detection is the process of segmenting a video into basic units known as shots by discovering transition frames between shots. Researches have been conducted to accurately detect the shot boundaries. However, the acceleration of the shot detection process with higher accuracy needs improvement. A new method was introduced in this paper to find out the boundaries of abrupt shots in the video with high accuracy and lower computational cost. The proposed method consists of two stages. First, projection features were used to distinguish non boundary transitions and candidate transitions that may contain abrupt boundary. Only candidate transitions were conserved for next stage. Thus, the speed of shot detection was improved by r

   This study aims to determine the petrophysical characteristics of the three wells in the Kifl Oilfield, central Iraq. The well logs were used to characterize hydrocarbon reservoirs to assess the hydrocarbon prospectivity, designate hydrocarbon and water-bearing zones, and determine the Nahr Umr Formation's petrophysical parameters. The Nahr Umr reservoir mainly consists of sandstone at the bottom and has an upper shale zone containing a small proportion of oil. The geophysical logs data from three oil wells include gamma-ray, resistivity, neutron, density, acoustic, and spontaneous potential logs. A gamma-ray log was employed for lithology differentiation, and a resistivity log was used to determine the response of distinct zones