Journal of Computing and Natural Science

Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization

Journal of Computing and Natural Science

Received On : 20 January 2022

Revised On : 10 April 2022

Accepted On : 08 May 2022

Published On : 05 July 2022

Volume 02, Issue 03

Pages : 120-131


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.


Artificial Intelligence-Aided Model (AIAM), Artificial Intelligence (AI), Human-Computer Interaction (HCI), Deep Neural Network Models (DNN).

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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.


© 2022 Christopher Neilson and Price Grigore. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.