In this paper, a microcontroller-based electronic circuit have been designed and implemented for dental curing system using 8-bit MCS-51 microcontroller. Also a new control card is designed while considering advantages of microcontroller systems the time of curing was controlled automatically by preset values which were input from a push-button switch. An ignition based on PWM technique was used to reduce the high starting current needed for the halogen lamp. This paper and through the test result will show a good performance of the proposed system.

The spread of Coronavirus has forced populations around the globe to adopt strict measures such as lockdown, home quarantine, and home office. Moreover, in the current development of network communications, people can exploit internet and intranet features in many systems that need to be faster, more efficient, and available on time. Furthermore, with the benefits of using internet-of-things (IoT), through which things are generated, gained, discovered, and proposed without interference, the user could receive the last status without exertion and direct contact (i.e., in a contactless manner). These specifications can be used in a transaction system. This paper proposes an electronic transaction system (ETS) as a replacement for the curr

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

      The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than othe

Communication is one of the vast and rapidly growing fields of engineering, where
increasing the efficiency of communication by overcoming the external
electromagnetic sources and noise is considered a challenging task. To achieve
confidentiality for color image transmission over the noisy communication channels
a proposed algorithm is presented for image encryption using AES algorithm. This
algorithm combined with error detections using Cyclic Redundancy Check (CRC) to
preserve the integrity of the encrypted data. This paper presents an error detection
method uses Cyclic Redundancy Check (CRC), the CRC value can be generated by
two methods: Serial and Parallel CRC Implementation. The proposed algorithm for
the

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