Technological advances in recent years have significantly changed the way an operating room works. This work aims to create a platform to solve the problems of operating room occupancy and prepare the rooms with an environment that is favorable for all operations. Using this system, a doctor can control all operation rooms, especially before an operation, and monitor their temperature and humidity to prepare for the operation. Also, in the event of a problem, an alert is sent to the nurse responsible for the room and medical stuff so that the problem can be resolved. The platform is tested using a Raspberry PI card and sensors. The sensors are connected to a cloud layer that collects and analyzes the temperature and humidity values obtained from the environment during an operation. The result of experimentations is visualized through a web application and an Android application. The platform also considers the security aspects such as authorization to access application functionalities for the Web and the mobile applications. We can also test and evaluate the system's existing problems and vulnerabilities using the IEEE and owasp IoT standards. Finally, the proposed framework is extended with a model based testing technique that may be adopted for validating the security aspects.
This paper focuses on identifying the Arabic Language learners. The main contribution of the proposed method is to use a deep learning model based on the Gated Recurrent Unit Network (GRUN). The proposed model explores a multitude of stylistic features such as the syntax, the lexical and the ngrams ones. To the best of our awareness, the obtained results outperform those obtained by the best existing systems. Our accuracy is the best comparing with the pioneers (45% vs 41%), considering the limited data and the unavailability of accurate tools dedicated to the Arabic language.
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