The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out the best architecture, pre-processing, and training parameters for the models largely automatically. The accuracy and F1-score were both above 96% in the diagnosis of COVID-19 using CT-scan images. In addition, we applied transfer learning techniques to overcome the insufficient data and to improve the training time. The binary and multi-class classification of X-ray images tasks were performed by utilizing enhanced VGG16 deep transfer learning architecture. High accuracy of 99% was achieved by enhanced VGG16 in the detection of X-ray images from COVID-19 and pneumonia. The accuracy and validity of the algorithms were assessed on X-ray and CT-scan well-known public datasets. The proposed methods have better results for COVID-19 diagnosis than other related in literature. In our opinion, our work can help virologists and radiologists to make a better and faster diagnosis in the struggle against the outbreak of COVID-19.

The driving force behind the smart city initiative is to offer better, more specialized services which can improve the quality of life of the citizens while promoting sustainability. To achieve both of these apparently competing goals, services must be increasingly autonomous and continuously adaptive to changes in their environment and the information coming from other services. In this paper we focus on smart lighting, a relevant application domain for which we propose an intelligent street light control system based on adaptive behavior rules. We evaluate our approach by using a simulator which combines wireless sensor networks and belief-desire-intention (BDI) agents to enable a precise simulation of both the city infrastructure and the adaptive behavior that it implements. The results reveal energy savings of close to 35% when the lighting system implements an adaptive behavior as opposed to a rigid, predefined behavior.