Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

The antibacterial activity of some extracts of A. eupatoria (aqueous and ethanolic) against some pathogenic bacteria (Staphylococcus aureus, Pseudomonas aeruginosa and Escherichia coli ) and their activity on wound healing in rats , also the presence of some active compounds in both extracts were detected . The results showed that the ethanolic extract was more effective on inhibiting tested bacteria than the aqueous extract . P.aeruginosa was the most resistant bacteria, while highest inhibition zone appeared on E.coli (20 mm) .There was a moderate activity against S.aureus with inhibition zone 15 mm. by using ethanolic extract (10 mg/ml) . The phytochemical analysis for detection of active compounds revealed the presence of Carbohydrate

The cost of microalgae harvesting constitutes a heavy burden on the commercialization of biofuel production. The present study addressed this problem through economic and parametric comparison of electrochemical harvesting using a sacrificial electrode (aluminum) and a nonsacrificial electrode (graphite). The harvesting efficiency, power consumption, and operation cost were collected as objective variables as a function of applied current and initial pH of the solution. The results indicated that high harvesting efficiency obtained by using aluminum anode is achieved in short electrolysis time. That harvesting efficiency can be enhanced by increasing the applied current or the electrolysis time for both electrode materials, where 98

Lead selenide PbSe thin films of different thicknesses (300, 500, and 700 nm) were deposited under vacuum using thermal evaporation method on glass substrates. X-ray diffraction measurements showed that increasing of thickness lead to well crystallize the prepared samples, such that the crystallite size increases while the dislocation density decreases with thickness increasing. A.C conductivity, dielectric constants, and loss tangent are studied as function to thickness, frequency (10kHz-10MHz) and temperatures (293K-493K). The conductivity measurements confirm confirmed that hopping is the mechanism responsible for the conduction process. Increasing of thickness decreases the thermal activation energy estimated from Arhinus equation is

In the present investigation, 24 adult dipteran species with forensic importance belonging to 13 genera and 8 families that were collected from different localities of Iraq. The specimens were identified by different taxonomical keys; in addition the date and localities of collecting specimens were recorded.

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

The use of appropriate and accurate language is of utmost importance when describing people on the move and their dilemma, particularly refugees and displaced persons who have unique legal protection. So, there are a lot of scholars who have investigated the United Nations High Commissioner for Refugees’ (UNHCR) reports, but no one has examined representational and interactional meanings of UNHCR reports. Accordingly, this research aims to explore the role of UNHCR reports in enhancing the value of the humanitarian, which is attributed to the uniqueness of its use of language. The current study investigates the manner in which the textual content interacts with the images that are associated with the category of the UNHCR reports. Four re

This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance