Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a

Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 %  1.66 %. This

Background: Psoriasis is a common inflammatory condition worldwide, with an average prevalence ranging from 2% to 3%. However, the incidence of psoriasis varies among different ethnic groups and regions. Elevated leptin levels have been associated with increased cellular proliferation, including T-cells. Additionally, high leptin levels may stimulate the synthesis of proinflammatory cytokines such as ILـ6 and TNFـα. Objectives: To evaluate the effect of Apremilast on Leptin in obese psoriatic patients. Subjects and Methods: Thirty patients with psoriasis were included in This prospective cohort study to measure the levels of serum Leptin by using the ELISA technique, before and after receiving Apremilast. Result: The present work

The Quiet American could be considered as one of Graham Greene’s most distinguished books; it is an epochal novel written during the phase of the cold war between the United States and the Soviet Union. The novel deals with the interference of the United States in Vietnam ten years before Vietnam’s war. The role the Americans played in arousing an inner political crisis in the country previous to her military invention. The book reflects that this action was not out of American government concern about Vietnamese people themselves but merely a political foreign affair. They wanted to stop communism from spreading widely and reducing its role in the East. This paper attempts to analyse the novel concentrating on the message Greene intend

KE Sharquie, AA Noaimi, ZT Burhan, Journal of Cosmetics, Dermatological Sciences and Applications, 2016 - Cited by 9

         The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work  is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The s