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

Face recognition is a crucial biometric technology used in various security and identification applications. Ensuring accuracy and reliability in facial recognition systems requires robust feature extraction and secure processing methods. This study presents an accurate facial recognition model using a feature extraction approach within a cloud environment. First, the facial images undergo preprocessing, including grayscale conversion, histogram equalization, Viola-Jones face detection, and resizing. Then, features are extracted using a hybrid approach that combines Linear Discriminant Analysis (LDA) and Gray-Level Co-occurrence Matrix (GLCM). The extracted features are encrypted using the Data Encryption Standard (DES) for security

In this study 100 samples were collected from infected children with acute and chronic tonsillitis who attended to Al-Yarmook Teaching Hospital (ENT consultation clinic) from 5/12/2013 to 1/3/2014. The result of laboratory culture was positive in 67 samples. Depending on their cultural, morphological and biochemical characterization of bacterial isolate of them were identified as (37.31%) belonged to Streptococcus pyogenes and the diagnosis is confirmed by the use of Remel Rapid STR System, (34.32%) belonged to S.parasanguinis, (11.94%) S.mitis, (11.94%) S.oralis and (4.47%) S.thoraltensis . Results confirmed that cup assay gave highest inhibition zone after 24 hrs compare with well diffusion methods for suspension of L.

Abstract This study investigated the treatment of textile wastewater contaminated with Acid Black 210 dye (AB210) using zinc oxide nanoparticles (ZnO NPs) through adsorption and photocatalytic techniques. ZnO NPs were synthesized using a green synthesis process involving eucalyptus leaves as reducing and capping agents. The synthesized ZnO NPs were characterized using UV-Vis spectroscopy, SEM, EDAX, XRD, BET, Zeta potential, and FTIR techniques. The BET analysis revealed a specific surface area and total pore volume of 26.318 m2/g. SEM images confirmed the crystalline and spherical nature of the particles, with a particle size of 73.4 nm. A photoreactor was designed to facilitate the photo-degradation process. The study investigated the inf

Introduction: Although soap industry is known from hundreds of years, the development accompanied with this industry was little. The development implied the mechanical equipment and the additive materials necessary to produce soap with the best specifications of shape, physical and chemical properties. Objectives: This research studies the use of vacuum reactive distillation VRD technique for soap production. Methods: Olein and Palmitin in the ratio of 3 to 1 were mixed in a flask with NaOH solution in stoichiometric amount under different vacuum pressures from -0.35 to -0.5 bar. Total conversion was reached by using the VRD technique. The soap produced by the VRD method was compared with soap prepared by the reaction - only method which