The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM
technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user
inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile
application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing
and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as
close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces
to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity
may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test
the efficacy of the suggested method by implementing the improved design.
Keywords
Artificial Intelligence-Aided Model (AIAM), Artificial Intelligence (AI), Human-Computer Interaction (HCI), Deep Neural
Network Models (DNN).
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Acknowledgements
Author(s) thanks to Pan-Atlantic University for research lab and equipment support.
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Cite this article
Christopher Neilson and Price Grigore, “Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization", vol.2, no.3, pp. 120-131, July 2022. doi: 10.53759/181X/JCNS202202015.
