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 features from

     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

Background:SARS-CoV-2 infection has caused a global pandemic that continues to negatively impact human health. A large group of microbial domains including bacteria co-evolved and interacted in complex molecular pathogenesis along with SARS-CoV-2. Evidence suggests that periodontal disease bacteria are involved in COVID-19, and are associated with chronic inflammatory systemic diseases. This study was performed to investigate the association between bacterial loads of Porphyromonas gingivalis and pathogenesis of SARS-CoV-2 infection. Fifty patients with confirmed COVID-19 by reverse transcriptase-polymerase chain reaction, their age ranges between 20-76 years, and 35 healthy volunteers (matched accordingly with age and sex to th

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

Cutaneous Leishmaniasis (CL) is an endemic disease and one of the major health problems in Iraq. Leishmania tropica is known as the causative agent of Cutaneous Leishmaniasis in Baghdad.The classical serological methods of diagnosing leishmaniasis is a poor sensitivity especially for the sub genus and time consuming Here we have investigated two primer pairs, one specific for Leishmania as genus and the primer specific for the species of L. tropica to be detected by polymerase chain reaction (PCR).Samples were collected from (AL-karama Teaching Hospital) and whole genomic DNA was extracted from axenic promastigotes.The extracted DNA was amplified by PCRwith two KDNA primer pairs, for genus specific (13A/13B) and (Lmj4/Uni21) to identify

Illiteracy has spread in the last years, although it was eliminated in the 1980s. The return of illiteracy brings ignorance, illness, backwardness and regression among nations. It has taken many types, mainly alphabetical, scientific and computer illiteracy. Hence, the increasing nature of illiteracy has attracted the attention of governments and societies alike. This may touch the reality of societies starting with their youth unless those, who are in charge, will find workable solutions for the existing problems. The results of the study revealed that there is a real disaster awaiting the next generation after years of stray, and ignorance of the people in charge who are too engaged in getting their privileges to care about this proble

Through this research, We have tried to evaluate the health programs and their effectiveness in improving the health situation through a study of the health institutions reality in Baghdad to identify the main reasons that affect the increase in maternal mortality by using two regression models, "Poisson's Regression Model" and "Hierarchical Poisson's Regression Model". And the study of that indicator (deaths) was through a comparison between the estimation methods of the used models. The "Maximum Likelihood" method was used to estimate the "Poisson's Regression Model"; whereas the "Full Maximum Likelihood" method were used for the "Hierarchical Poisson's Regression Model