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Year 2025, Volume: 8 Issue: 4, 701 - 717

Osman Güler , Taha Etem

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

Abstract

References

  • A. Gong, Y. Cheng, J. Su, and L. Zhang, “Research on hybrid synchronization methods in multi-user collaborative VR simulation medical surgery training system,” Concurr Comput, vol. 36, no. 11, p. e8008, May 2024, doi: 10.1002/CPE.8008.
  • Y. Ye, H. Liu, W. Kuang, and W. Chen, “Virtual reality modeling application based on multi-perspective and deep learning in the new media presentation and brand building of Dongguan City memory,” Concurr Comput, vol. 36, no. 11, p. e8015, May 2024, doi: 10.1002/CPE.8015.
  • H. Namrouti, C. Sik-Lányi, and T. Guzsvinecz, “Exploring measurement tools for color perception in virtual reality: A systematic review,” Displays, vol. 87, p. 102937, Apr. 2025, doi: 10.1016/J.DISPLA.2024.102937.
  • P. Campo-Prieto, J. M. Cancela-Carral, and G. Rodríguez-Fuentes, “Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson’s Disease Patients,” Sensors (Basel), vol. 22, no. 9, May 2022, doi: 10.3390/S22093302.
  • O. Güler and S. Savaş, “All Aspects of Metaverse Studies, Technologies and Future,” Gazi Journal of Engineering Sciences, vol. 8, no. 2, pp. 292–319, Sep. 2022, doi: 10.30855/GMBD.0705011.
  • M. Jansen, J. Donkervliet, A. Trivedi, and A. Iosup, “Can My WiFi Handle the Metaverse? A Performance Evaluation Of Meta’s Flagship Virtual Reality Hardware,” ICPE 2023 - Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, pp. 297–303, Apr. 2023, doi: 10.1145/3578245.3585022.
  • T. Erol Akar and S. Ünver, “Effectiveness of Virtual Reality Glasses on Surgical Fear and Anxiety in Patients Before Open-heart Surgery: A Double-blind Randomized Controlled Trial,” J Perianesth Nurs, 2024, doi: 10.1016/J.JOPAN.2024.08.011.
  • B. Xie et al., “A Review on Virtual Reality Skill Training Applications,” Front Virtual Real, vol. 2, p. 645153, Apr. 2021, doi: 10.3389/FRVIR.2021.645153/PDF.
  • W. H. Wan, A. Ho, Y. Lam, W. H. Wan, and A. H. Y. Lam, “The Effectiveness of Virtual Reality-Based Simulation in Health Professions Education Relating to Mental Illness: A Literature Review,” Health N Hav, vol. 11, no. 6, pp. 646–660, Jun. 2019, doi: 10.4236/HEALTH.2019.116054.
  • T. Li, “The Art Foundation of Virtual Reality Interactivity,” in Application of Intelligent Systems in Multi-modal Information Analytics, Springer Science and Business Media Deutschland GmbH, 2022, pp. 952–958. doi: 10.1007/978-3-031-05484-6_127/FIGURES/4.
  • C. Chung and S. H. Lee, “Continuous Prediction of Pointing Targets With Motion and Eye-Tracking in Virtual Reality,” IEEE Access, vol. 12, pp. 5933–5946, 2024, doi: 10.1109/ACCESS.2024.3350788.
  • L. Wang, X. Li, J. Wu, D. Zhou, I. Sio Kei, and V. Popescu, “AVICol: Adaptive Visual Instruction for Remote Collaboration Using Mixed Reality,” Int J Hum Comput Interact, vol. 41, no. 2, pp. 1260–1279, 2025, doi: 10.1080/10447318.2024.2313920.
  • J. Liebers, S. Brockel, U. Gruenefeld, and S. Schneegass, “Identifying Users by Their Hand Tracking Data in Augmented and Virtual Reality,” Int J Hum Comput Interact, vol. 40, no. 2, pp. 409–424, 2024, doi: 10.1080/10447318.2022.2120845.
  • L. Delbes, N. Mascret, C. Goulon, and G. Montagne, “Validation of an immersive virtual reality device accepted by seniors that preserves the adaptive behavior produced in the real world,” Front Bioeng Biotechnol, vol. 10, Sep. 2022, doi: 10.3389/FBIOE.2022.917486.
  • V. Angelov, E. Petkov, G. Shipkovenski, and T. Kalushkov, “Modern Virtual Reality Headsets,” HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, Jun. 2020, doi: 10.1109/HORA49412.2020.9152604.
  • G. Goncalves, P. Monteiro, M. Melo, J. Vasconcelos-Raposo, and M. Bessa, “A Comparative Study between Wired and Wireless Virtual Reality Setups,” IEEE Access, vol. 8, pp. 29249–29258, 2020, doi: 10.1109/ACCESS.2020.2970921.
  • K. Aslan, Z. Özer, and M. K. Yöntem, “Effect of Virtual Reality on Pain, Anxiety, and Vital Signs in Endoscopy,” Pain Management Nursing, Jan. 2025, doi: 10.1016/J.PMN.2024.11.009.
  • A. A. Laghari, A. K. Jumani, K. Kumar, and M. A. Chhajro, “Systematic Analysis of Virtual Reality & Augmented Reality,” International Journal of Information Engineering and Electronic Business, vol. 13, no. 1, pp. 36–43, Feb. 2021, doi: 10.5815/IJIEEB.2021.01.04.
  • J. Xiong, E. L. Hsiang, Z. He, T. Zhan, and S. T. Wu, “Augmented reality and virtual reality displays: emerging technologies and future perspectives,” Light Sci Appl, vol. 10, no. 1, Dec. 2021, doi: 10.1038/S41377-021-00658-8.
  • O. Güler and S. Savaş, “Stereoscopic 3D teaching material usability analysis for interactive boards,” Comput Animat Virtual Worlds, vol. 33, no. 2, p. e2041, Mar. 2022, doi: 10.1002/CAV.2041.
  • M. Guo, H. Gao, S. Yang, K. Yue, Y. Liu, and Y. Wang, “Evaluation of stereoscopic visual fatigue in virtual reality with exploration of brain dynamics,” Displays, vol. 87, p. 102898, Apr. 2025, doi: 10.1016/J.DISPLA.2024.102898.
  • D. Cui and C. Mousas, “Effects of Tactile Interruption on Hand-Eye Coordination Task Performance,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 104–111, 2024, doi: 10.1109/VRW62533.2024.00024.
  • J. Louca, K. Eder, J. Vrublevskis, and A. Tzemanaki, “Impact of Haptic Feedback in High Latency Teleoperation for Space Applications,” ACM Trans Hum Robot Interact, vol. 13, no. 2, p. 21, Jun. 2024, doi: 10.1145/3651993/ASSET/6BA95049-F946-4101.
  • Z. Hou, C. She, Y. Li, D. Niyato, M. Dohler, and B. Vucetic, “Intelligent Communications for Tactile Internet in 6G: Requirements, Technologies, and Challenges,” IEEE Communications Magazine, vol. 59, no. 12, pp. 82–88, Dec. 2021, doi: 10.1109/MCOM.006.2100227.
  • Z. Zou, D. Prasetyawan, H. H. Wu, K. Cheng, and T. Nakamoto, “Extension of Wearable Olfactory Display for Multisensory VR Experience,” in International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments, 2024. doi: 10.2312/egve.20241374.
  • K. Ragozin, X. Meng, R. Lalintha Peiris, K. Wolf, G. Chernyshov, and K. Kunze, “Thermoquest-awearable head mounted display to augment realities with thermal feedback,” ACM International Conference Proceeding Series, pp. 62–66, May 2021, doi: 10.1145/3490632.3490649.
  • A. Sarkar, J. Murray, M. Dasari, M. Zink, and K. Nahrstedt, “L3BOU: Low Latency, Low Bandwidth, Optimized Super-Resolution Backhaul for 360-Degree Video Streaming,” Proceedings - 23rd IEEE International Symposium on Multimedia, ISM 2021, pp. 138–147, 2021, doi: 10.1109/ISM52913.2021.00031.
  • J. de Souza and R. Tartz, “Visual perception and user satisfaction in video see-through head-mounted displays: a mixed-methods evaluation,” Front Virtual Real, vol. 5, p. 1368721, Jun. 2024, doi: 10.3389/FRVIR.2024.1368721/BIBTEX.
  • S. Palmisano, V. Morrison, R. S. Allison, R. G. Davies, and J. Kim, “Effects of Constant and Sinusoidal Display Lag on Sickness During Active Exposures to Virtual Reality,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 757–758, 2024, doi: 10.1109/VRW62533.2024.00175.
  • Y. Sung, D. K. Kwak, T. Kim, W. Woo, and S. H. Yoon, “Deep-Texture: A Lightweight Wearable Ring for Shape and Texture Rendering in Virtual Reality,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 911–912, 2024, doi: 10.1109/VRW62533.2024.00252.
  • H. Liu et al., “A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance,” Front Neurosci, vol. 18, p. 1367932, Apr. 2024, doi: 10.3389/FNINS.2024.1367932/BIBTEX.
  • S. W. Baek et al., “Systematic analysis of anatomy virtual reality (VR) apps for advanced education and further applications,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–34, Dec. 2024, doi: 10.1038/s41598-024-82945-z.
  • Z. Wang, R. He, and K. Chen, “Thermal comfort and virtual reality headsets,” Appl Ergon, vol. 85, p. 103066, May 2020, doi: 10.1016/J.APERGO.2020.103066.
  • S. Y. Park and D. K. Koo, “The Impact of Virtual Reality Content Characteristics on Cybersickness and Head Movement Patterns,” Sensors (Basel), vol. 25, no. 1, p. 215, Jan. 2025, doi: 10.3390/S25010215.
  • S. Hickson, V. Kwatra, N. Dufour, A. Sud, and I. Essa, “Eyemotion: Classifying facial expressions in VR using eye-tracking cameras,” Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, pp. 1626–1635, Mar. 2019, doi: 10.1109/WACV.2019.00178.
  • K. Kargut, C. Gutwin, and A. Cockburn, “Efects of Device Environment and Information Layout on Spatial Memory and Performance in VR Selection Tasks,” Conference on Human Factors in Computing Systems - Proceedings, May 2024, doi: 10.1145/3613904.3642486/.
  • H. J. Jang, J. Y. Lee, G. W. Baek, J. Kwak, and J. H. Park, “Progress in the development of the display performance of AR, VR, QLED and OLED devices in recent years,” Journal of Information Display, vol. 23, no. 1, pp. 1–17, 2022, doi: 10.1080/15980316.2022.2035835.
  • Ö. Akbulut, A. Kaygısız, and İ. Yılmaz, “A Comparative Research on Data Analysis with Factorial ANOVA, Logistic Regression and CHAID Classification Tree Methods,” Black Sea Journal of Agriculture, vol. 5, no. 3, pp. 314–322, Jul. 2022, doi: 10.47115/BSAGRICULTURE.1087820.
  • S. Angadi and V. S. Reddy, “Multimodal sentiment analysis using reliefF feature selection and random forest classifier,” International Journal of Computers and Applications, vol. 43, no. 9, pp. 931–939, Oct. 2021, doi: 10.1080/1206212X.2019.1658054.
  • L. Jovanovic et al., “Improving Phishing Website Detection Using a Hybrid Two-level Framework for Feature Selection and XGBoost Tuning,” Journal of Web Engineering, vol. 22, no. 3, pp. 543–574–543–574, Jul. 2023, doi: 10.13052/JWE1540-9589.2237.
  • S. Buyrukoğlu and A. Akbaş, “Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS,” Balkan Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 110–117, Apr. 2022, doi: 10.17694/BAJECE.973129.
  • S. Buyrukoğlu and S. Savaş, “Stacked-Based Ensemble Machine Learning Model for Positioning Footballer,” Arab J Sci Eng, vol. 48, no. 2, pp. 1371–1383, Feb. 2023, doi: 10.1007/s13369-022-06857-8.
  • M. Teke and F. Duran, “The design and implementation of road condition warning system for drivers,” Measurement and Control (United Kingdom), vol. 52, no. 7–8, pp. 985–994, Sep. 2019, doi: 10.1177/0020294019858088/ASSET/IMAGES/LARGE/10.1177_0020294019858088-FIG6.JPEG.
  • G. Buyrukoğlu, S. Buyrukoğlu, and Z. Topalcengiz, “Comparing Regression Models with Count Data to Artificial Neural Network and Ensemble Models for Prediction of Generic Escherichia coli Population in Agricultural Ponds Based on Weather Station Measurements,” Microb Risk Anal, vol. 19, Dec. 2021, doi: 10.1016/J.MRAN.2021.100171.

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

Year 2025, Volume: 8 Issue: 4, 701 - 717

Osman Güler , Taha Etem

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

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

Virtual Reality Performance Prediction Machine Learning Synthetic Dataset Overheating

References

  • A. Gong, Y. Cheng, J. Su, and L. Zhang, “Research on hybrid synchronization methods in multi-user collaborative VR simulation medical surgery training system,” Concurr Comput, vol. 36, no. 11, p. e8008, May 2024, doi: 10.1002/CPE.8008.
  • Y. Ye, H. Liu, W. Kuang, and W. Chen, “Virtual reality modeling application based on multi-perspective and deep learning in the new media presentation and brand building of Dongguan City memory,” Concurr Comput, vol. 36, no. 11, p. e8015, May 2024, doi: 10.1002/CPE.8015.
  • H. Namrouti, C. Sik-Lányi, and T. Guzsvinecz, “Exploring measurement tools for color perception in virtual reality: A systematic review,” Displays, vol. 87, p. 102937, Apr. 2025, doi: 10.1016/J.DISPLA.2024.102937.
  • P. Campo-Prieto, J. M. Cancela-Carral, and G. Rodríguez-Fuentes, “Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson’s Disease Patients,” Sensors (Basel), vol. 22, no. 9, May 2022, doi: 10.3390/S22093302.
  • O. Güler and S. Savaş, “All Aspects of Metaverse Studies, Technologies and Future,” Gazi Journal of Engineering Sciences, vol. 8, no. 2, pp. 292–319, Sep. 2022, doi: 10.30855/GMBD.0705011.
  • M. Jansen, J. Donkervliet, A. Trivedi, and A. Iosup, “Can My WiFi Handle the Metaverse? A Performance Evaluation Of Meta’s Flagship Virtual Reality Hardware,” ICPE 2023 - Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, pp. 297–303, Apr. 2023, doi: 10.1145/3578245.3585022.
  • T. Erol Akar and S. Ünver, “Effectiveness of Virtual Reality Glasses on Surgical Fear and Anxiety in Patients Before Open-heart Surgery: A Double-blind Randomized Controlled Trial,” J Perianesth Nurs, 2024, doi: 10.1016/J.JOPAN.2024.08.011.
  • B. Xie et al., “A Review on Virtual Reality Skill Training Applications,” Front Virtual Real, vol. 2, p. 645153, Apr. 2021, doi: 10.3389/FRVIR.2021.645153/PDF.
  • W. H. Wan, A. Ho, Y. Lam, W. H. Wan, and A. H. Y. Lam, “The Effectiveness of Virtual Reality-Based Simulation in Health Professions Education Relating to Mental Illness: A Literature Review,” Health N Hav, vol. 11, no. 6, pp. 646–660, Jun. 2019, doi: 10.4236/HEALTH.2019.116054.
  • T. Li, “The Art Foundation of Virtual Reality Interactivity,” in Application of Intelligent Systems in Multi-modal Information Analytics, Springer Science and Business Media Deutschland GmbH, 2022, pp. 952–958. doi: 10.1007/978-3-031-05484-6_127/FIGURES/4.
  • C. Chung and S. H. Lee, “Continuous Prediction of Pointing Targets With Motion and Eye-Tracking in Virtual Reality,” IEEE Access, vol. 12, pp. 5933–5946, 2024, doi: 10.1109/ACCESS.2024.3350788.
  • L. Wang, X. Li, J. Wu, D. Zhou, I. Sio Kei, and V. Popescu, “AVICol: Adaptive Visual Instruction for Remote Collaboration Using Mixed Reality,” Int J Hum Comput Interact, vol. 41, no. 2, pp. 1260–1279, 2025, doi: 10.1080/10447318.2024.2313920.
  • J. Liebers, S. Brockel, U. Gruenefeld, and S. Schneegass, “Identifying Users by Their Hand Tracking Data in Augmented and Virtual Reality,” Int J Hum Comput Interact, vol. 40, no. 2, pp. 409–424, 2024, doi: 10.1080/10447318.2022.2120845.
  • L. Delbes, N. Mascret, C. Goulon, and G. Montagne, “Validation of an immersive virtual reality device accepted by seniors that preserves the adaptive behavior produced in the real world,” Front Bioeng Biotechnol, vol. 10, Sep. 2022, doi: 10.3389/FBIOE.2022.917486.
  • V. Angelov, E. Petkov, G. Shipkovenski, and T. Kalushkov, “Modern Virtual Reality Headsets,” HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings, Jun. 2020, doi: 10.1109/HORA49412.2020.9152604.
  • G. Goncalves, P. Monteiro, M. Melo, J. Vasconcelos-Raposo, and M. Bessa, “A Comparative Study between Wired and Wireless Virtual Reality Setups,” IEEE Access, vol. 8, pp. 29249–29258, 2020, doi: 10.1109/ACCESS.2020.2970921.
  • K. Aslan, Z. Özer, and M. K. Yöntem, “Effect of Virtual Reality on Pain, Anxiety, and Vital Signs in Endoscopy,” Pain Management Nursing, Jan. 2025, doi: 10.1016/J.PMN.2024.11.009.
  • A. A. Laghari, A. K. Jumani, K. Kumar, and M. A. Chhajro, “Systematic Analysis of Virtual Reality & Augmented Reality,” International Journal of Information Engineering and Electronic Business, vol. 13, no. 1, pp. 36–43, Feb. 2021, doi: 10.5815/IJIEEB.2021.01.04.
  • J. Xiong, E. L. Hsiang, Z. He, T. Zhan, and S. T. Wu, “Augmented reality and virtual reality displays: emerging technologies and future perspectives,” Light Sci Appl, vol. 10, no. 1, Dec. 2021, doi: 10.1038/S41377-021-00658-8.
  • O. Güler and S. Savaş, “Stereoscopic 3D teaching material usability analysis for interactive boards,” Comput Animat Virtual Worlds, vol. 33, no. 2, p. e2041, Mar. 2022, doi: 10.1002/CAV.2041.
  • M. Guo, H. Gao, S. Yang, K. Yue, Y. Liu, and Y. Wang, “Evaluation of stereoscopic visual fatigue in virtual reality with exploration of brain dynamics,” Displays, vol. 87, p. 102898, Apr. 2025, doi: 10.1016/J.DISPLA.2024.102898.
  • D. Cui and C. Mousas, “Effects of Tactile Interruption on Hand-Eye Coordination Task Performance,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 104–111, 2024, doi: 10.1109/VRW62533.2024.00024.
  • J. Louca, K. Eder, J. Vrublevskis, and A. Tzemanaki, “Impact of Haptic Feedback in High Latency Teleoperation for Space Applications,” ACM Trans Hum Robot Interact, vol. 13, no. 2, p. 21, Jun. 2024, doi: 10.1145/3651993/ASSET/6BA95049-F946-4101.
  • Z. Hou, C. She, Y. Li, D. Niyato, M. Dohler, and B. Vucetic, “Intelligent Communications for Tactile Internet in 6G: Requirements, Technologies, and Challenges,” IEEE Communications Magazine, vol. 59, no. 12, pp. 82–88, Dec. 2021, doi: 10.1109/MCOM.006.2100227.
  • Z. Zou, D. Prasetyawan, H. H. Wu, K. Cheng, and T. Nakamoto, “Extension of Wearable Olfactory Display for Multisensory VR Experience,” in International Conference on Artificial Reality and Telexistence Eurographics Symposium on Virtual Environments, 2024. doi: 10.2312/egve.20241374.
  • K. Ragozin, X. Meng, R. Lalintha Peiris, K. Wolf, G. Chernyshov, and K. Kunze, “Thermoquest-awearable head mounted display to augment realities with thermal feedback,” ACM International Conference Proceeding Series, pp. 62–66, May 2021, doi: 10.1145/3490632.3490649.
  • A. Sarkar, J. Murray, M. Dasari, M. Zink, and K. Nahrstedt, “L3BOU: Low Latency, Low Bandwidth, Optimized Super-Resolution Backhaul for 360-Degree Video Streaming,” Proceedings - 23rd IEEE International Symposium on Multimedia, ISM 2021, pp. 138–147, 2021, doi: 10.1109/ISM52913.2021.00031.
  • J. de Souza and R. Tartz, “Visual perception and user satisfaction in video see-through head-mounted displays: a mixed-methods evaluation,” Front Virtual Real, vol. 5, p. 1368721, Jun. 2024, doi: 10.3389/FRVIR.2024.1368721/BIBTEX.
  • S. Palmisano, V. Morrison, R. S. Allison, R. G. Davies, and J. Kim, “Effects of Constant and Sinusoidal Display Lag on Sickness During Active Exposures to Virtual Reality,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 757–758, 2024, doi: 10.1109/VRW62533.2024.00175.
  • Y. Sung, D. K. Kwak, T. Kim, W. Woo, and S. H. Yoon, “Deep-Texture: A Lightweight Wearable Ring for Shape and Texture Rendering in Virtual Reality,” Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024, pp. 911–912, 2024, doi: 10.1109/VRW62533.2024.00252.
  • H. Liu et al., “A comparative study of stereo-dependent SSVEP targets and their impact on VR-BCI performance,” Front Neurosci, vol. 18, p. 1367932, Apr. 2024, doi: 10.3389/FNINS.2024.1367932/BIBTEX.
  • S. W. Baek et al., “Systematic analysis of anatomy virtual reality (VR) apps for advanced education and further applications,” Scientific Reports 2024 14:1, vol. 14, no. 1, pp. 1–34, Dec. 2024, doi: 10.1038/s41598-024-82945-z.
  • Z. Wang, R. He, and K. Chen, “Thermal comfort and virtual reality headsets,” Appl Ergon, vol. 85, p. 103066, May 2020, doi: 10.1016/J.APERGO.2020.103066.
  • S. Y. Park and D. K. Koo, “The Impact of Virtual Reality Content Characteristics on Cybersickness and Head Movement Patterns,” Sensors (Basel), vol. 25, no. 1, p. 215, Jan. 2025, doi: 10.3390/S25010215.
  • S. Hickson, V. Kwatra, N. Dufour, A. Sud, and I. Essa, “Eyemotion: Classifying facial expressions in VR using eye-tracking cameras,” Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, pp. 1626–1635, Mar. 2019, doi: 10.1109/WACV.2019.00178.
  • K. Kargut, C. Gutwin, and A. Cockburn, “Efects of Device Environment and Information Layout on Spatial Memory and Performance in VR Selection Tasks,” Conference on Human Factors in Computing Systems - Proceedings, May 2024, doi: 10.1145/3613904.3642486/.
  • H. J. Jang, J. Y. Lee, G. W. Baek, J. Kwak, and J. H. Park, “Progress in the development of the display performance of AR, VR, QLED and OLED devices in recent years,” Journal of Information Display, vol. 23, no. 1, pp. 1–17, 2022, doi: 10.1080/15980316.2022.2035835.
  • Ö. Akbulut, A. Kaygısız, and İ. Yılmaz, “A Comparative Research on Data Analysis with Factorial ANOVA, Logistic Regression and CHAID Classification Tree Methods,” Black Sea Journal of Agriculture, vol. 5, no. 3, pp. 314–322, Jul. 2022, doi: 10.47115/BSAGRICULTURE.1087820.
  • S. Angadi and V. S. Reddy, “Multimodal sentiment analysis using reliefF feature selection and random forest classifier,” International Journal of Computers and Applications, vol. 43, no. 9, pp. 931–939, Oct. 2021, doi: 10.1080/1206212X.2019.1658054.
  • L. Jovanovic et al., “Improving Phishing Website Detection Using a Hybrid Two-level Framework for Feature Selection and XGBoost Tuning,” Journal of Web Engineering, vol. 22, no. 3, pp. 543–574–543–574, Jul. 2023, doi: 10.13052/JWE1540-9589.2237.
  • S. Buyrukoğlu and A. Akbaş, “Machine Learning based Early Prediction of Type 2 Diabetes: A New Hybrid Feature Selection Approach using Correlation Matrix with Heatmap and SFS,” Balkan Journal of Electrical and Computer Engineering, vol. 10, no. 2, pp. 110–117, Apr. 2022, doi: 10.17694/BAJECE.973129.
  • S. Buyrukoğlu and S. Savaş, “Stacked-Based Ensemble Machine Learning Model for Positioning Footballer,” Arab J Sci Eng, vol. 48, no. 2, pp. 1371–1383, Feb. 2023, doi: 10.1007/s13369-022-06857-8.
  • M. Teke and F. Duran, “The design and implementation of road condition warning system for drivers,” Measurement and Control (United Kingdom), vol. 52, no. 7–8, pp. 985–994, Sep. 2019, doi: 10.1177/0020294019858088/ASSET/IMAGES/LARGE/10.1177_0020294019858088-FIG6.JPEG.
  • G. Buyrukoğlu, S. Buyrukoğlu, and Z. Topalcengiz, “Comparing Regression Models with Count Data to Artificial Neural Network and Ensemble Models for Prediction of Generic Escherichia coli Population in Agricultural Ponds Based on Weather Station Measurements,” Microb Risk Anal, vol. 19, Dec. 2021, doi: 10.1016/J.MRAN.2021.100171.
There are 44 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Osman Güler 0000-0003-3272-5973

Taha Etem 0000-0003-1419-5008

Early Pub Date October 13, 2025
Publication Date October 16, 2025
Submission Date April 22, 2025
Acceptance Date September 16, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

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 Güler O, Etem T. Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. SAUCIS. October 2025;8(4):701-717. doi:10.35377/saucis.1681525
Chicago 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 8, no. 4 (October 2025): 701-17. https://doi.org/10.35377/saucis. 1681525.
EndNote Güler O, Etem T (October 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 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, 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 (October2025), 701-717. https://doi.org/10.35377/saucis. 1681525.
JAMA 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, 2025, pp. 701-17, doi:10.35377/saucis. 1681525.
Vancouver Güler O, Etem T. Predictive Machine Learning Modeling of Performance Issues in Virtual Reality Devices Using Synthetic Dataset. SAUCIS. 2025;8(4):701-17.
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