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

An ultrasonic treatment was applied to the vacuum gas oil at intervals of 5 to 30 minutes, at 70°C.  In this work, the improvement of the important properties of Iraqi vacuum gas oil, such as carbon residue, was studied with several parameter conditions that affect vacuum efficiency, such as sonication time (5, 10, 15, 20, 25, and 30) min, power amplitude (10–50%). After ultrasonic treatment, the carbon residue of vacuum gas oil was evaluated using a Conradson carbon residue meter (ASTM D189). The experiment revealed that the oil's carbon residue had decreased by 16%. As a consequence of the experiment It was discovered that ultrasonic treatment might reduce the carbon residual and density of oil samples being studied. It also notice

Background: The aims of the study were to evaluate the unclean/clean root canal surface areas with a histopathological cross section view of the root canal and the isthmus and to evaluate the efficiency of instrumentation to the isthmus using different rotary instrumentation techniques. Materials and Methods:The mesial roots of thirty human mandibular molars were divided into six groups, each group was composed of five roots (10 root canals)which prepared and irrigated as: Group one A: Protaper system to size F2 and hypodermic syringe, Group one B: Protaper system to size F2 and endoactivator system, Group two A:Wave One small then primary file and hypodermic syringe, Group two B:Wave One small then primary file and endoactivator system, Gr

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducte

The Frequency-hopping Spread Spectrum (FHSS) systems and techniques are using in military and civilianradar recently and in the communication system for securing the information on wireless communications link channels, for example in the Wi-Fi 8.02.X IEEE using multiple number bandwidth and frequencies in the wireless channel in order to hopping on them for increasing the security level during the broadcast, but nowadays FHSS problem, which is, any Smart Software Defined Radio (S-SDR) can easily detect a wireless signal at the transmitter and the receiver for the hopping sequence in both of these, then duplicate this sequence in order to hack the signal on both transmitter and receiver messages using the order of the se

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Polymers have the ability to extract water after they have been added to the mortar or concrete mixture. They provide the absorbed water during hydration functioning as internal water source. Absorption polymers can absorb up to hundred times of their own weight of pure water.This research deals with the use of water absorption polymer balls in concrete and study the volumetric change of these mixes and compared the results with reference mix (without polymers). Samples were cured both in air and in water for the mixes to compare results which show that samples in air behave for expansion while sample in water acted for shrinkage.

The field work to study morbidity & nutritional status of 3828 child aged between 659 months in Basra, Misan and Thi Qar governorate.  The results indicated that 14%, 11.7% and 5.4% of the children suffer from diarrhea, Fever & Acute Respiratory Infection (API) respectively. While the prevalence of malnutrition for the children at the three governorate indicated that the chronic was 24.8%, 25.1% and 23%, general malnutrition was 17.1%, 21.4% and 22% where as the acute malnutrition was 9.2, 9.4 and 14.2 respectively.

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database