The Investigation of Feasibility Related to AI algorithms in VR for Improving Customer Satisfaction and Immersion

DOI

10.2991/978-94-6463-300-9_26How to use a DOI?

Keywords

Machine Learning; Virtual Reality; Deep Learning

Abstract

After years of extensive development, the accessibility of Virtual Reality (VR) technology has reached a point where it is now within reach of ordinary individuals, allowing them to engage and derive pleasure from immersive experiences. This rapid proliferation of VR brands in the market has prompted businesses to explore avenues for enhancing customer immersion in order to elevate overall satisfaction. Consequently, this scholarly article investigates the feasibility of employing various Artificial Intelligence (AI) algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Deep Neural Networks (DNN), Naive Bayes, and Random Forest, to analyze and improve VR applications. By evaluating crucial metrics such as F1-score, Recall, Precision, and accuracy for each model, the findings of this study reveal that the accuracy of these algorithms consistently hovered around 0.21. Notably, XGBoost analysis was supplemented with a feature importance table, which identified duration, age, and motion sickness as the primary influencing factors. Furthermore, the Learning Curve analysis demonstrated that KNN and Random Forest models exhibited signs of overfitting, whereas Naive Bayes and SVM models exhibited signs of underfitting. In light of these results, it is apparent that none of the individual AI models explored in this research are well-suited for the comprehensive analysis of VR applications.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yiren Shen
PY  - 2023
DA  - 2023/11/27
TI  - The Investigation of Feasibility Related to AI algorithms in VR for Improving Customer Satisfaction and Immersion
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 253
EP  - 263
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_26
DO  - 10.2991/978-94-6463-300-9_26
ID  - Shen2023
ER  -