Performance of different membership functions in stress classification with fuzzy logic

Year 2022, Volume: 12 Issue: 2, 60 - 63, 30.12.2022

Mehmet Akif Bülbül

https://doi.org/10.17678/beuscitech.1190436

Cited By: 2

Abstract

Stress has become an indispensable part of today's world. Stress can have a very serious negative impact on human health. Knowing the intensity of stress on people is important in order to cope with it. In this study, 4 different Fuzzy Logic (FL) structures were used to classify human stress through sleep. In the established structures, the human stress detection data set in sleep and through sleep obtained from Kaggle was used. In the FL structures created, blood oxygen level and respiratory rate were taken as input and stress classification was made accordingly. Their performance in the classification of sleep stress was evaluated by using different membership functions in 4 different structures. As a result of experimental studies, the F model established with the generalized bell showed more successful results than the models established with other membership functions.

Keywords

Fuzzy logic, Multiclass classification, Decision support system

References

• Adem, K., Kiliçarslan, S., and Cömert, O., 2019. Classification and diagnosis of cervical cancer with stacked autoencoder and softmax classification. Expert Systems With Applications, 115, 557–564. https://doi.org/10.1016/j.eswa.2018.08.050
• Awasthi, A. K., Dubey, O. P., Awasthi, A., and Sharma, S., 2005. A fuzzy logic model for estimation of groundwater recharge. Annual Conference of the North American Fuzzy Information Processing Society – NAFIPS, Detroit, MI, USA, 26-28 June, p. 809-813. https://doi.org/10.1109/NAFIPS.2005.1548644
• Baumgartl, H., Fezer, E., and Buettner, R. 2020., Two-level classification of chronic stress using machine learning on resting-state EEG recordings. 26th Americas Conference on Information Systems, AMCIS, 15-17 August.
• Bülbül, M. A., Harirchian, E., Işık, M. F., Aghakouchaki Hosseini, S. E., and Işık, E., 2022. A Hybrid ANN-GA Model for an Automated Rapid Vulnerability Assessment of Existing RC Buildings. Applied Sciences, 12(10), 5138. https://doi.org/10.3390/app12105138
• Bülbül, M. A., and Öztürk, C., 2022. Optimization, Modeling and Implementation of Plant Water Consumption Control Using Genetic Algorithm and Artificial Neural Network in a Hybrid Structure. Arabian Journal for Science and Engineering, 47(2), 2329-2343. https://doi.org/10.1007/s13369-021-06168-4
• Bülbül, M. A., Öztürk, C., Ilçi, V., and Ozulu, I. M., 2019. Two-Dimensional Error Estimation in Point Positioning with Fuzzy Logic. 2018 International Conference on Artificial Intelligence and Data Processing IDAP 2018, Malatya-Turkey, 28-30 September, p. 1-4 . https://doi.org/10.1109/IDAP.2018.8620901
• Chen, D., Lu, Y., & Hsu, C. Y., 2022. Measurement Invariance Investigation for Performance of Deep Learning Architectures. IEEE Access, 10: 78070-78087. https://doi.org/10.1109/ACCESS.2022.3192468
• Deveci, B., 2017. İş Stresi Ve Turizm İşletmelerinde Yapilan Araştirmalara İlişkin Bir Değerlendirme. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(20), 39-53. https://doi.org/10.20875/makusobed.306671
• Heydarian, M., Doyle, T. E., and Samavi, R., 2022. MLCM: Multi-Label Confusion Matrix. IEEE Access, 10, 19083-19095. https://doi.org/10.1109/ACCESS.2022.3151048
• Human Stress Detection in and through Sleep. (n.d.). Retrieved October 17, 2022, from https://www.kaggle.com/datasets/laavanya/human-stress-detection-in-and-through-sleep
• Işik, E., Işik, M. F., and Bülbül, M. A., 2017. Web based evaluation of earthquake damages for reinforced concrete buildings. Earthquake and Structures, 13(4), 423-432. https://doi.org/10.12989/eas.2017.13.4.387
• Işık, M. F., Işık, E., & Bülbül, M. A., 2018. Application of iOS/Android based assessment and monitoring system for building inventory under seismic impact. Gradjevinar, 70 (12), 1043-1056. https://doi.org/10.14256/JCE.1522.2015
• Kumar, M. G. S., and Dhulipala, V. R. S., 2016. Fuzzy Logic Based Stress Level Classification using Physiological Parameters. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 697-713. https://doi.org/10.5958/2249-7315.2016.00990.4
• Naqvi, S., Shaikh, A. Z., Altaf, T., and Singh, S., 2021. Fuzzy Logic Enabled Stress Detection Using Physiological Signals. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Virtual Event, 04 November p. 161–173. https://doi.org/10.1007/978-3-030-90016-8_11
• Pacal, I., and Karaboga, D., 2021. A robust real-time deep learning based automatic polyp detection system. Computers in Biology and Medicine, 134, 104519. https://doi.org/10.1016/j.compbiomed.2021.104519
• Rachakonda, L., Bapatla, A. K., Mohanty, S. P., and Kougianos, E., 2021. SaYoPillow: Blockchain-Integrated Privacy-Assured IoMT Framework for Stress Management Considering Sleeping Habits. IEEE Transactions on Consumer Electronics, 67(1), 20-29. https://doi.org/10.1109/TCE.2020.3043683
• Rastgoo, M. N., Nakisa, B., Maire, F., Rakotonirainy, A., and Chandran, V., 2019. Automatic driver stress level classification using multimodal deep learning. Expert Systems with Applications, 138, 112793. https://doi.org/10.1016/j.eswa.2019.07.010
• Shin, J. W., Seongo, H. M., Cha, D. I., Yoon, Y. R., and Yoon, H. R., 1998. Estimation of stress status using biosignal and fuzzy theory. International Conference of the IEEE Engineering in Medicine and Biology Society, 01 November, Vol. 3 p. 1393-1394. https://doi.org/10.1109/iembs.1998.747141
• Yildirim, E., Avci, E., and Yilmaz, B., 2021. Serbest Basinç Dayaniminin Tahmininde Sugeno Bulanik Mantik Yaklaşimi. Uludağ University Journal of The Faculty of Engineering, 26(1), 97-108. https://doi.org/10.17482/uumfd.863121
• Yildirim, S., 2008. Muhasebe Öğretim Elemanları ve Meslek Mensuplarının Mesleki Stres Düzeyi Üzerine Bir Araştırma. Muhasebe ve Finansman Dergisi, 38, 153–162.
• Zalabarria, U., Irigoyen, E., Martínez, R., and Arechalde, J., 2018. Acquisition and fuzzy processing of physiological signals to obtain human stress level using low cost portable hardware. In International Joint Conference SOCO’17-CISIS’17-ICEUTE’17, León-Spain, 6–8 September, p. 68-78. https://doi.org/10.1007/978-3-319-67180-2_7