Abstract--This paper considers the adaptation of the e-continuously learn the user's stress pattern in order to adjust The measure of usefulness includes accuracy, among others. As stated in In this paper, two-stage intelligent processing, as seen in [3], e-coaching "may contribute to a better understanding of Figure 1, is used: people's affective responses to the COVID-19 crisis. Stage I is aimed at gathering physiological information legal, and social implications are addressed appropriately, from a subject for human decision-making (reasoning). Stage II is aimed at supporting the human decisionmaker society by monitoring and improving people's mental health". Typical symptoms include anxiety, panic, avoidance, and stress.

In this study of the face recognition on masked versus unmasked faces generated using Flickr-Faces-HQ and SpeakingFaces datasets, we report 36.78% degradation of recognition performance caused by the mask-wearing at the time of pandemics, in particular, in border checkpoint scenarios. We have achieved better performance and reduced the degradation to 1.79% using advanced deep learning approaches in the cross-spectral domain.

Contemporary Epidemiological Surveillance (ES) relies heavily on data analytics. These analytics are critical input for pandemics preparedness networks; however, this input is not integrated into a form suitable for decision makers or experts in preparedness. A decision support system (DSS) with Computational Intelligence (CI) tools is required to bridge the gap between epidemiological model of evidence and expert group decision. We argue that such DSS shall be a cognitive dynamic system enabling the CI and human expert to work together. The core of such DSS must be based on machine reasoning techniques such as probabilistic inference, and shall be capable of estimating risks, reliability and biases in decision making.