RESUMEN
The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.
RESUMEN
In the model-driven security domain, access control systems provide an application for handling access of persons through controlled gates. A gate, such as a door, can have a lock mechanism for securing the area from unauthorized access. Most commercial solutions for access control management offer pre-packaged software systems where customization of the authorization logic is either not allowed or subject to payment. Moreover, cross-platform development is a barrier for solution providers due to the high cost of development and maintenance that it implies. To overcome these limitations and further optimize the entire access control systems development process, we propose a model-driven approach that supports automatic code generation to enable communication between an IoT infrastructure and platforms for Facility Access Management. Specifically, the approach combines the benefits of Near-Field Communication (NFC) and Tinkerforge (i.e., an open-source hardware platform) with model-driven techniques. This allows the approach to exploit both behavioral and structural models for the modeling and the consequent code generation of part of the authorization mechanism, thus providing complete coverage of the code generated for the whole system. We implemented and evaluated our approach in a real-world case study within the premises of a fitness center with an IoT infrastructure consisting of several heterogeneous sensors by showing its practical applicability. Experimental results demonstrate the effectiveness of our approach in supporting abstraction and automation concerning traditional code-centric development through code generation features. Consequently, our approach makes the whole development process less time-consuming and error-prone, thus reducing the system's time to market.