A facial expression recognition system that can provide quick assistance to the healthcare system and exceptional services to the patients is proposed in this article. The implementation of this work is divided into three components. In the first component, landmark points on the facial region are detected; a fixed-sized rectangular box is obtained by normalizing the detected face region, and then, down sampled to its varying sizes producing multiresolution images. Different convolution neural network architectures are proposed in the second component for analyzing the textual information within the multiresolution facial images. To extract more discriminating features and enhance the proposed system’s performance, some amalgamation of transfer learning, progressive image resizing, data augmentation, and fine tuning of parameters are employed in the third component. For experimental purposes, three benchmark databases, static facial expressions in the wild, Cohn-Kanade, and Karolinska directed emotional faces, are employed with some existing methods concerning these databases. The comparison with these databases shows the superiority of the proposed system.

Cloud platforms encompass a large number of storage services that can be used to manage the needs of customers. Each of these services, offered by a different provider, is characterized by specific features, limitations and prices. In presence of multiple options, it is crucial to select the best solution fitting the customer requirements in terms of quality of service and costs. Most of the available approaches are not able to handle uncertainty in the expression of subjective preferences from customers, and can result in wrong (or sub-optimal) service selections in presence of rational/selfish providers, exposing untrustworthy indications concerning the quality of service levels and prices associated to their offers. In addition, due to its multi-objective nature, the optimal service selection process results in a very complex task to be managed, when possible, in a distributed way, for well-known scalability reasons. In this work, we aim at facing the above challenges by proposing three novel contributions. The fuzzy sets theory is used to express vagueness in the subjective preferences of the customers. The service selection is resolved with the distributed application of fuzzy inference or Dempster-Shafer theory of evidence. The selection strategy is also complemented by the adoption of a game theoretic approach for promoting truth-telling ones among service providers. We present empirical evidence of the proposed solution effectiveness through properly crafted simulation experiments.

A key benefit of connecting edge and cloud computing is the capability to achieve high-throughput under high concurrent accesses, mobility support, real-time processing guarantees, and data persistency. For example, the elastic provisioning and storage capabilities provided by cloud computing allow us to cope with scalability, persistency and reliability requirements and to adapt the infrastructure capacity to the exacting needs based on the amount of generated data.