After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.

The study aims to identify the impact of the implementation of the integrated education strategy in the curriculum of the Arabic language for the seventh grade on the academic achievement in the schools of the capital Amman. The researcher adopted the experimental method, where two divisions of the seventh grade students were chosen from the secondary school for girls. The sample of the study was 60 students divided into two equal groups: 30 students represented the experimental group (A) and (30) students represented the control group. To collect the needed data, a test of (40) Multiple Choices was used. The results showed statistically significant differences between the mean scores of the experimental group who were taught acc

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

The research included five sections containing the first section on the introduction of the research and its importance and was addressed to the importance of the game of gymnastic and skilled parallel effectiveness and the importance of learning, but the problem of research that there is a difference in learning this skill and difficulty in learning may be one of the most important reasons are fear and fear of falling and injury, And a lack of sense of the movement of the movement is one of the obstacles in the completion of the skill and the goal of research to design a device that helps in learning the skill of descending Almtor facing with half a cycle according to the typical locomotor track on the parallel device of the technical men'

Natural honey is well known for its therapeutic value and has been used in traditional medicine of different cultures throughout the world. The aim of this study was to investigate the anti-inflammatory effect of Malaysian Gelam honey in inflammation-induced rats. Paw edema was induced by a subplantar injection of 1% carrageenan into the rat right hind paw. Rats were treated with the nonsteroidal anti-inflammatory drug (NSAID) Indomethacin (10 mg/kg, p.o.) or Gelam honey at different doses (1 or 2 g/kg, p.o.). The increase in footpad thickness was considered to be edema, which was measured using a dial caliper. Plasma and paw tissue were collected to analyze the production of inflammatory mediators, such as NO, PGE₂

In this paper, an Anti-Disturbance Compensator is suggested for the stabilization of a 6-DoF quadrotor Unmanned Aerial vehicle (UAV) system, namely, the Improved Active Disturbance Rejection Control (IADRC). The proposed Control Scheme rejects the disturbances subjected to this system and eliminates the effect of the uncertainties that the quadrotor system exhibits. The complete nonlinear mathematical model of the 6-DoF quadrotor UAV system has been used to design the four ADRCs units for the attitude and altitude stabilization. Stability analysis has been demonstrated for the Linear Extended State Observer (LESO) of each IADRC unit and the overall closed-loop system using Hurwitz stability criterion. A minimization to a

In this paper we investigate the automatic recognition of emotion in text. We propose a new method for emotion recognition based on the PPM (PPM is short for Prediction by Partial Matching) character-based text compression scheme in order to recognize Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method is very effective when compared with traditional word-based text classification methods. We have also found that our method works best if the sizes of text in all classes used for training are similar, and that performance significantly improves with increased data.

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