ABSTRACT
Current society is facing remarkable changes in last decade directed by several processes: gains in life expectancy leading to ageing population;essential transformation caused by different epidemic situations including the recent COVID-19 pandemic;on-line work and isolation that leads to different levels of mental disorders;stressful working environments and everyday life. All these circumstances generate significant risk factors that drastically influence peoples' bad health status and conditions.On the other hand, in a lot of, even highly developed countries, health and medical support is getting more and more limited and out of normal working conditions due to different reasons.So, a very important question in this highly technologically developed society is if and how information communication technologies, artificial intelligence, and virtual agent technologies can help in changing and improving the current situation.One important scientific and research direction is oriented towards the development of high quality and reliable personalized medical and health services. The development of sophisticated, intelligent virtual agents i.e. a specific kind of medical e-coaching can help people in getting adequate advices and recommendations in order to improve their health conditions and quality of key life indicators.The use of agents in personalized and social medical and health platforms can open new possibilities for producing tailored recommendations during human-intelligent virtual agents' conversation and support. Some opportunities and challenges of human-agent communication are considered in order to increase human living and health conditions. Several paradigmatic cases of intelligent virtual agents are presented and challenges for future development. © 2022 IEEE.
ABSTRACT
Research on the pandemic situation of COVID-19 is very important for delivering detailed risk analyzes based on estimating the peak of the pandemic. The machine learning approach has a major role to play in predicting the number of COVID-19 cases. Most research on COVID-19 uses polynomial regression for analysis. When a regression model is build, often, the model fails to generalize on unseen data. For instance, the model might end up becoming too complex, having significantly high variance due to over-fitting, thereby impacting the model performance on new data sets. To avoid over-fitting of the polynomial regression, a regularization method can be used to suppress the coefficients of the higher order polynomial, a principle that allows the smoothness of the regression function. The aim of this paper is to formulate a mathematical model for regularization coefficient in polynomial regression and evaluate this approach to enable obtaining meaningful results on a COVID-19 data set. Therefore we believe that our results will contribute to a better understanding of the over-fitting process in polynomial regression. Our methodology consists of following major steps: i) optimizing the model using k-fold cross-validation for finding an optimal regularization coefficient and ii) comparing the performance of ridge regression and lasso regression using accuracy metrics. Moreover, our approach could also have a potential impact in machine learning education, regarding the understanding of the underlying mathematical machinery behind polynomial regression algorithms. The obtained results show that the polynomial model built using lasso regression, outperforms the ridge regression. © 2022 IEEE.