suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at
Statistics in Transition New Series , ISSUE 2, 309–322
potential applications of an analytics tool of this nature and do not represent how any collaborator in this project currently uses the developed system in any real-world/production environment.
International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–18
In modern society, more and more people are suffering from some type of stress. Monitoring and timely detecting of stress level will be very valuable for the person to take counter measures. In this paper, we investigate the use of decision analytics methodologies to detect stress. We present a new feature selection method based on the principal component analysis (PCA), compare three feature selection methods, and evaluate five information fusion methods for stress detection. A driving stress
International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 1675–1699