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

To assess cultural competence among nursing students from nine countries to provide an international perspective on cultural competence.

Experimental and numerical investigations of the centrifugal pump performance at non-cavitating and cavitating flow conditions were carried out in the present study. Experiments were performed by applying a vacuum to a closed-loop system to investigate the effects of the net positive suction head available (NPSHa), flow rate, water temperature and pump speed on the centrifugal pump performance. Accordingly, many of the important parameters concerning cavitation phenomenon were calculated. Also, the noise which is accompanied by cavitation was measured. Numerical analysis was implemented for two phase flow (the water and its vapor) using a 2-D simulation by ANSYS FLUENT software to investigate the internal flow of centrifugal pump under c

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima