Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

In cyber security, the most crucial subject in information security is user authentication. Robust text-based password methods may offer a certain level of protection. Strong passwords are hard to remember, though, so people who use them frequently write them on paper or store them in file for computer .Numerous of computer systems, networks, and Internet-based environments have experimented with using graphical authentication techniques for user authentication in recent years. The two main characteristics of all graphical passwords are their security and usability. Regretfully, none of these methods could adequately address both of these factors concurrently. The ISO usability standards and associated characteristics for graphical

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

In this work, results of a mathematical analysis of the role of workpiece preheating in laser keyhole welding were presented. This analysis considered the steady-state welding as well as certain range of boundary conditions over which preheating effect would be indicated. This work is an attempt to interpret the role of preheating to increase welding depth and perform keyhole welding with high quality using physical and thermal properties of steel alloys.

In this research is to study the influence of the aging heat treatment on the pitting corrosion resistance of martensitic stainless steel (MSS), where a number of specimens from martensitic stainless steel were subjected to solution treatment at 1100 ^oC for one hour followed by water quenching then aging in the temperatures range (500-750) ^oC for different holding times (1,5,10,15&20) hr. Accelerated chemical corrosion test and immersion chemical corrosion test were performed on samples after heat treatment. The results of the research showed that the pitting corrosion resistance is significantly affected by the aging temperature. Where found that the aging samples at a temperature of 500 °C have the highest ra

The deficiency in the nurse staff  in the health organizations consider an important problem that must be studied and solved basically , not only because it affect on the health organization and it's strategic goals , but also it affect the human being and it's health which can't be substituted with anything or delayed in it's treatment ,This research aims to  necessary for health organizations to strategically help in maintaining the nurse staff and to keep that in it's strategic orientation and it's mission , moreover , the health organizations must study the reality of the nurse in the health organizations and know the causes beyond leaving the  nurse staff  the nurse job , and then remove

In this work we present a technique to extract the heart contours from noisy echocardiograph images. Our technique is based on improving the image before applying contours detection to reduce heavy noise and get better image quality. To perform that, we combine many pre-processing techniques (filtering, morphological operations, and contrast adjustment) to avoid unclear edges and enhance low contrast of echocardiograph images, after implementing these techniques we can get legible detection for heart boundaries and valves movement by traditional edge detection methods.