Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Maxillary first premolar with wide MOD cavity more susceptible to fracture. The aim of this study was to assess the influence of cavity design for cusp coverage on the fracture resistance of weakened maxillary first premolar restored with CAD/CAM hybrid ceramic versus nanohybide composite. Materials and Methods: Fifty six intact maxillary first premolars of approximately comparable sizes were divided into seven groups eight for each: Group A: Intact teeth (control group); Group B: teeth prepared for MOD inlay; Group C: teeth prepared for MOD onlay covering the lingual cusp; Group D: teeth prepared for MOD covering buccal and lingual cusps ,the previous three groups indirectly restored with nanohybrid composite (3M ESPE Z 250 X

This study included synthesizing silver nanoparticles (AgNPs) in a green method using AgNO3 solution with glucose exposed to microwave radiation. The prepared NPs were also characterized using ultraviolet and visible (UV-vis) spectroscopy and scanning electron microscopy (SEM). The UV/vis spectroscopy confirmed the production of AgNPs, while SEM analysis showed that the typical spherical AgNPs were 30 nm and 50 nm in size for the NPs prepared using black tea (B) and green tea (G) as reducing agent, respectively. The changes in some of the biochemical parameters related to the liver and kidneys have been analyzed to evaluate the probable toxic effects of AgNPs. 40 adult male mice were included in this study. To assess the probable he

Acting on the Holy Qur’an by contemplating its meanings, stopping at its commands and prohibitions, and everything in it that guides us to truth and mercy. The Holy Qur’an includes it for various purposes in one verse, and there is no doubt that this is part of the perfection of this great book, and one of the sciences of the fundamentalists is the indication of the context, and it is a great science of high rank, and it is one of the most important things that lead to the correct understanding of the miraculous and decisive book of God Almighty, and one of the best meanings of the Qur’an It was not from the context of the verses, and from here it can be said that the indication of the context is one of the origins of deriving lega

Abstract:

Objective: The study’s aim to evaluate the effectiveness of instructional program about healthy lifestyle on patients’ attitudes after undergoing percutaneous coronary intervention.

Methodology: Quasi-experimental design/ has been utilized for the current study starting from December 2018 to March 2020 to achieve the objectives of the study. Non-probability (purposive) sample of 60 patients was divided into intervention and control groups. Data were analyzed by the application of descriptive and inferential statistical methods.

Results: findings reported that before intervention both study and control groups demonstrated low total mean of score relat