Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset

Osman Güler; Taha Etem

doi:10.35377/saucis...1681525

EN

Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset

Abstract

This study presents a novel approach to predicting and analyzing performance problems in standalone Virtual Reality (VR) devices through the development of a comprehensive synthetic dataset and machine learning methodology. The research created a synthetic dataset simulating the performance of ten different standalone VR devices, incorporating both technical specifications and real-time performance metrics. The dataset generation process considered realistic device behavior patterns, including temperature variations under different load conditions, performance degradation factors, and network-related issues. The methodology employed seven different machine learning models. The dataset comprised 14,400 samples, with data collected at 5-second intervals over 120-minute sessions. Results demonstrated exceptional performance from tree-based models, with Random Forest and Decision Tree achieving near-perfect accuracy (99.97%). Extreme Gradient Boosting (99.69%) and Neural Network (98.92%) also showed strong performance. The study found that Overheating and Packet Loss predictions were particularly accurate across most models, while High Latency classification proved more challenging for some algorithms due to class imbalance. The synthetic dataset and methodology offer a foundation for future research in VR system optimization and real-time performance monitoring. The study addresses a significant gap in the literature by integrating both hardware specifications and performance metrics into a comprehensive analysis framework.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Osman Güler ^*
0000-0003-3272-5973
Türkiye

Taha Etem
0000-0003-1419-5008
Türkiye

Early Pub Date

October 13, 2025

Publication Date

December 29, 2025

Submission Date

April 22, 2025

Acceptance Date

September 16, 2025

Published in Issue

Year 2025 Volume: 8 Number: 4

DOI

https://doi.org/10.35377/saucis...1681525

IZ

https://izlik.org/JA27ML87SA

Cite

RIS / Bibtex

APA

Güler, O., & Etem, T. (2025). Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. Sakarya University Journal of Computer and Information Sciences, 8(4), 701-717. https://doi.org/10.35377/saucis...1681525

AMA

1.Güler O, Etem T. Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. SAUCIS. 2025;8(4):701-717. doi:10.35377/saucis.1681525

Chicago

Güler, Osman, and Taha Etem. 2025. “Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset”. Sakarya University Journal of Computer and Information Sciences 8 (4): 701-17. https://doi.org/10.35377/saucis. 1681525.

EndNote

Güler O, Etem T (December 1, 2025) Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. Sakarya University Journal of Computer and Information Sciences 8 4 701–717.

IEEE

[1]O. Güler and T. Etem, “Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset”, SAUCIS, vol. 8, no. 4, pp. 701–717, Dec. 2025, doi: 10.35377/saucis...1681525.

ISNAD

Güler, Osman - Etem, Taha. “Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset”. Sakarya University Journal of Computer and Information Sciences 8/4 (December 1, 2025): 701-717. https://doi.org/10.35377/saucis. 1681525.

JAMA

1.Güler O, Etem T. Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. SAUCIS. 2025;8:701–717.

MLA

Güler, Osman, and Taha Etem. “Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, Dec. 2025, pp. 701-17, doi:10.35377/saucis. 1681525.

Vancouver

1.Osman Güler, Taha Etem. Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. SAUCIS. 2025 Dec. 1;8(4):701-17. doi:10.35377/saucis. 1681525