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

This study is conducted to carry out a straightforward way appropriate for quality monitoring and stability of arc stud welding process, followed by a number of procedures to control the quality of welded samples, namely torque destructive testing and visual inspection context.  Those procedures were being performed to support the monitoring system and verify its validity. Thus, continuous on-line monitoring guarantees earlier discovering stud welding defects and avoiding weld repeatability. On-line welding electronic monitoring system is for non destructive determining if a just completed weld is satisfactory or unsatisfactory, depending on welding current peak value detected by the system. Also, it has been observed significant ha

The author addresses the issue of the linguoculturological component in the process of teaching Russian to Arabic students, focuses on the peculiarities of the national character of students. The author also refers to the long-standing ties of Russian and Arab cultures, thus emphasizing the relevance of this aspect for the current state and situation of the Russian language in Arab countries.

Автор статьи обращается к вопросу лингвокультурологической составляющей в процессе преподавания русского языка арабским студентам, останавливается на особенностях национального хара

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

Image quality plays a vital role in improving and assessing image compression performance. Image compression represents big image data to a new image with a smaller size suitable for storage and transmission. This paper aims to evaluate the implementation of the hybrid techniques-based tensor product mixed transform. Compression and quality metrics such as compression-ratio (CR), rate-distortion (RD), peak signal-to-noise ratio (PSNR), and Structural Content (SC) are utilized for evaluating the hybrid techniques. Then, a comparison between techniques is achieved according to these metrics to estimate the best technique. The main contribution is to improve the hybrid techniques. The proposed hybrid techniques are consisting of discrete wavel

Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to i