Background: Human leukocyte antigen-G (HLA-G)and Toll-like receptor-9 (TLR-9)play a role in the regulation of autoimmune diseases and inflammatory processes. Aim of the study: To detect the HLA-G + 3142G > C gene polymorphism that associated with the susceptibility to SLE patients and associated with Hepatitis B infection and TLR-9 serum level. Patients and methods: This study was done on 75 SLE patients and 75 healthy control groups. Genotyping of HLA-G + 3142G > C were detected by PCR and PCR-RFLP methods. In addition to the estimation of Hepatitis B surface (HBs)antigen status by immunochromatography technique and TLR-9 serum level by ELISA technique. Results: The HLA-G + 3142G > C gene polymorphism between the SLE patients and controls

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

     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