With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

In the literature, several correlations have been proposed for hold-up prediction in rotating disk contactor. However,
these correlations fail to predict hold-up over wide range of conditions. Based on a databank of around 611
measurements collected from the open literature, a correlation for hold up was derived using Artificial Neiral Network
(ANN) modeling. The dispersed phase hold up was found to be a function of six parameters: N, vc , vd , Dr , c d m / m ,
s . Statistical analysis showed that the proposed correlation has an Average Absolute Relative Error (AARE) of 6.52%
and Standard Deviation (SD) 9.21%. A comparison with selected correlations in the literature showed that the
developed ANN correlation noticeably

Power switches require snubbing networks for driving single – phase industrial heaters. Designing these networks, for controlling the maximum allowable rate of rise of anode current (di/dt) and excessive anode – cathode voltage rise (dv/dt) of power switching devices as thyristors and Triacs, is usually achieved using conventional methods like Time Constant Method (TCM), resonance Method (RM), and Runge-Kutta Method (RKM). In this paper an alternative design methodology using Fuzzy Logic Method (FLM) is proposed for designing the snubber network to control the voltage and current changes. Results of FLM, with fewer rules requirements, show the close similarity with those of conventional design methods in such a network of a Triac drivin

The Electrical power system has become vast and more complex, so it is subjected to sudden changes in load levels. Stability is an important concept which determines the stable operation of the power system. Transient stability analysis has become one of the significant studies in the power system to ensure the system stability to withstand a considerable disturbance. The effect of temporary occurrence can lead to malfunction of electronic control equipment. The application of flexible AC transmission systems (FACTS) devices in the transmission system have introduced several changes in the power system. These changes have a significant impact on the power system protection, due to differences inline impedance, line curre

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The current study included measuring the percent of protein in the extract of nematode Ascaridia galli that infect chickens, it was 1.157% and equivalent to 11.570 mg /L., as well as the amino acid analysis in the nematode A.galli by using a high-performance liquid chromatography technique (HPLC), as was detect five types for amino acids in this extract Leucine, Threonin, Serine, Methionine and Valine as the amount of these amino acids in the extract was as follows 132.973, 26.994, 10.453, 2.243 and 1.888 mg /L., respectively, and other amino acids which Glutamic, Histidine and Tyrosin did not exist in the nematode A.galli.