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Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics

Yıl 2024, Cilt: 8 Sayı: 4, 656 - 665, 31.10.2024
https://doi.org/10.31127/tuje.1471445

Öz

In this study, the generalizability and distributivity of three different chaotic systems within an industrial robotics time series dataset are explored using an annotated artificial intelligence algorithm. A time series dataset derived from industrial robotics processes was constructed and transformed into the Runge-Kutta system, comprising fourth-order differential equations for normalization. Among the processed data, variables related to x-y-z positions underwent chaotic transformations through Lorenz, Chen, and Rossler chaos systems. The x variable and angle variables from the transformed x-y-z data were inputted into the InterpretML model, an annotated artificial intelligence model, to elucidate the effects of angle variables on the x position variable. As a result of this analysis, InterpretML Local analysis revealed a sensitivity of 0.05 for the Rossler chaos system, 0.15 for Chen, and 0.25 for Lorenz. Furthermore, global analysis indicated precision rates of 0.17 for Rossler, 0.255 for Chen, and 0.35 for Lorenz chaos systems. These sensitivity results suggest that the Rossler chaos system consistently provides more accurate results in both InterpretML local and global analyses compared to other chaotic systems. This study contributes significantly to the literature by analyzing the distributive and generalization properties of chaos systems and enhancing understanding of these systems.

Teşekkür

The author would like to thank all the data sets, materials, information sharing and support used in the assembly of this article.

Kaynakça

  • Tsiknakis, N., Trivizakis, E., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Tsatsakis, A., Sánchez-García, J., López-González, R., Papanikolaou, N., Karantanas, A. H., & Marias, K. (2020). Interpretable artificial intelligence framework for covid-19 screening on chest x-rays. Experimental and Therapeutic Medicine, 20(2), 1351–1357. https://doi.org/10.3892/etm.2020.8820
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322-330. https://doi.org/10.31127/tuje.1180931
  • Kharkov, Y. A., Sotskov, V. E., Karazeev, A. A., Kiktenko, E. O., & Fedorov, A. K. (2019). Revealing quantum chaos with machine learning. arXiv-quant-ph.
  • Bhattacharya, C., & Ray, A. (2020). Data-driven detection and classification of regimes in chaotic systems via hidden Markov modeling. Unknown.
  • Pappu, C. S., Carroll, T. L., & Flores, B. C. (2020). Simultaneous radar-communication systems using controlled chaos-based frequency modulated waveforms. IEEE Access.
  • Ikizoglu, S., & Atasoy, B. (2020). Chaotic approach based feature extraction to implement in gait analysis. Unknown.
  • Mukhopadhyay, S., & Banerjee, S. (2020). Learning dynamical systems in noise using convolutional neural networks. Chaos (Woodbury, N.Y.).
  • Sadler, C. R., Grassby, T., Hart, K., Raats, M. M., Sokolović, M., & Timotijevic, L. (2021). Processed food classification: Conceptualisation and challenges. Trends in Food Science and Technology.
  • Hadi, R. N., Mahmoud, R. O., & Tag Eldien, A. S. (2021). Feature selection method based on chaotic salp swarm algorithm and extreme learning machine for network intrusion detection systems. WeboLogy.
  • Altay, O., Ulas, M., & Alyamac, K. E. (2021). Dcs-elm: A novel method for extreme learning machine for regression problems and a new approach for the sfrscc. PeerJ. Computer Science.
  • Gilpin, W. (2021). Chaos as an interpretable benchmark for forecasting and data-driven modelling. arXiv-cs.LG.
  • Liedji, D. W., & Mbé, J. H. T., Kenne, G. (2023). Classification of hyperchaotic, chaotic, and regular signals using single nonlinear node delay-based reservoir computers. Chaos (Woodbury, N.Y.).
  • Corbetta, A., & Jong, T. G. (2023). How neural networks learn to classify chaotic time series. Chaos (Woodbury, N.Y.).
  • Kawabata, K., Xiao, Z., Ohtsuki, T., & Shindou, R. (2023). Singular-value statistics of non-Hermitian random matrices and open quantum systems. arXiv-cond-mat.mes-hall.
  • Avvaru, S., & Parhi, K. K. (2023). Effective brain connectivity extraction by frequency domain convergent cross-mapping (FDCCM) and its application in Parkinson’s disease classification. IEEE Transactions on Biomedical Engineering.
  • Khodadadi, V., Rahatabad, F. N., Sheikhani, A., & Dabanloo, N. J. (2023). A dataset of a stimulated biceps muscle of electromyogram signal by using Rossler chaotic equation. Data in Brief.
  • Palanisamy, P., Urooj, S., Arunachalam, R., & Lay-Ekuakille, A. (2023). A novel prognostic model using chaotic CNN with hybridized spoofing for enhancing diagnostic accuracy in epileptic seizure prediction. Diagnostics (Basel, Switzerland).
  • García-García, A. M., Sá, L., Verbaarschot, J. J. M., & Yin, C. (2023). Emergent topology in many-body dissipative quantum chaos. arXiv-cond-mat.str-el.
  • Xiao, Z., & Shindou, R. (2024). Universal hard-edge statistics of non-Hermitian random matrices. arXiv-cond-mat.mes-hall.
  • Raman, P., & Chelliah, B. J. (2023). Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model. PeerJ.
  • Huyut, M. T., & Velichko, A. (2023). Lognnet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of covid-19. MethodsX.
  • Payot, N., Pasquato, M., Travan, A., Marsili, E., & Bianconi, G. (2023). Active learning in fractal decision boundaries. arXiv-cs.LG.
  • Yamaguchi, T., Takahashi, H., Nakagawa, Y., & Arai, T. (2001). Speeding up reinforcement learning using chaotic evolutionary computation for a driver’s support display system. Unknown.
  • Pathak, J., Hunt, B. R., Girvan, M., Lu, Z., & Ott, E. (2018). Model-free prediction of large spatiotemporally chaotic systems from data: A hybrid multiple model framework. Chaos (Woodbury, N.Y.).
  • Yasuda, K., Matsumoto, Y., Iwata, K., & Hasegawa, T. (2020). Data-driven modeling for chaotic origami dynamics prediction with machine learning. Unknown.
  • Yang, Y., Huang, D.-S., Huang, H., Guo, J.-Y., Li, Y., & Fang, H. (2022). Hybrid method using Havok analysis and machine learning for chaotic time series prediction. IEEE Access.
  • Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
  • Abdullah, T. A. A., Zahid, M. S. M., & Ali, W. (2021). A review of interpretable ML in healthcare: Taxonomy, applications, challenges, and future directions. Symmetry, 13, 2439. https://doi.org/10.3390/sym13122439
  • Yu, H. Q., Alaba, A., & Eziefuna, E. (2024). Evaluation of integrated XAI frameworks for explaining disease prediction models in healthcare. In: Qi, J., & Yang, P. (Eds.) Internet of Things of Big Data for Healthcare. IoTBDH 2023. Communications in Computer and Information Science, vol 2019. Springer, Cham. https://doi.org/10.1007/978-3-031-52216-1_2
  • Karim, A., Mishra, A., Newton, M. A., & Sattar, A. (2018). Machine learning interpretability: A science rather than a tool. arXiv preprint arXiv:1807.06722.
  • Zhang, C., Jiang, J., Qu, S.-X., & Lai, Y.-C. (2020). Predicting phase and sensing phase coherence in chaotic systems with machine learning. Chaos, 30(8), 083114. https://doi.org/10.1063/5.000630
Yıl 2024, Cilt: 8 Sayı: 4, 656 - 665, 31.10.2024
https://doi.org/10.31127/tuje.1471445

Öz

Kaynakça

  • Tsiknakis, N., Trivizakis, E., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Tsatsakis, A., Sánchez-García, J., López-González, R., Papanikolaou, N., Karantanas, A. H., & Marias, K. (2020). Interpretable artificial intelligence framework for covid-19 screening on chest x-rays. Experimental and Therapeutic Medicine, 20(2), 1351–1357. https://doi.org/10.3892/etm.2020.8820
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322-330. https://doi.org/10.31127/tuje.1180931
  • Kharkov, Y. A., Sotskov, V. E., Karazeev, A. A., Kiktenko, E. O., & Fedorov, A. K. (2019). Revealing quantum chaos with machine learning. arXiv-quant-ph.
  • Bhattacharya, C., & Ray, A. (2020). Data-driven detection and classification of regimes in chaotic systems via hidden Markov modeling. Unknown.
  • Pappu, C. S., Carroll, T. L., & Flores, B. C. (2020). Simultaneous radar-communication systems using controlled chaos-based frequency modulated waveforms. IEEE Access.
  • Ikizoglu, S., & Atasoy, B. (2020). Chaotic approach based feature extraction to implement in gait analysis. Unknown.
  • Mukhopadhyay, S., & Banerjee, S. (2020). Learning dynamical systems in noise using convolutional neural networks. Chaos (Woodbury, N.Y.).
  • Sadler, C. R., Grassby, T., Hart, K., Raats, M. M., Sokolović, M., & Timotijevic, L. (2021). Processed food classification: Conceptualisation and challenges. Trends in Food Science and Technology.
  • Hadi, R. N., Mahmoud, R. O., & Tag Eldien, A. S. (2021). Feature selection method based on chaotic salp swarm algorithm and extreme learning machine for network intrusion detection systems. WeboLogy.
  • Altay, O., Ulas, M., & Alyamac, K. E. (2021). Dcs-elm: A novel method for extreme learning machine for regression problems and a new approach for the sfrscc. PeerJ. Computer Science.
  • Gilpin, W. (2021). Chaos as an interpretable benchmark for forecasting and data-driven modelling. arXiv-cs.LG.
  • Liedji, D. W., & Mbé, J. H. T., Kenne, G. (2023). Classification of hyperchaotic, chaotic, and regular signals using single nonlinear node delay-based reservoir computers. Chaos (Woodbury, N.Y.).
  • Corbetta, A., & Jong, T. G. (2023). How neural networks learn to classify chaotic time series. Chaos (Woodbury, N.Y.).
  • Kawabata, K., Xiao, Z., Ohtsuki, T., & Shindou, R. (2023). Singular-value statistics of non-Hermitian random matrices and open quantum systems. arXiv-cond-mat.mes-hall.
  • Avvaru, S., & Parhi, K. K. (2023). Effective brain connectivity extraction by frequency domain convergent cross-mapping (FDCCM) and its application in Parkinson’s disease classification. IEEE Transactions on Biomedical Engineering.
  • Khodadadi, V., Rahatabad, F. N., Sheikhani, A., & Dabanloo, N. J. (2023). A dataset of a stimulated biceps muscle of electromyogram signal by using Rossler chaotic equation. Data in Brief.
  • Palanisamy, P., Urooj, S., Arunachalam, R., & Lay-Ekuakille, A. (2023). A novel prognostic model using chaotic CNN with hybridized spoofing for enhancing diagnostic accuracy in epileptic seizure prediction. Diagnostics (Basel, Switzerland).
  • García-García, A. M., Sá, L., Verbaarschot, J. J. M., & Yin, C. (2023). Emergent topology in many-body dissipative quantum chaos. arXiv-cond-mat.str-el.
  • Xiao, Z., & Shindou, R. (2024). Universal hard-edge statistics of non-Hermitian random matrices. arXiv-cond-mat.mes-hall.
  • Raman, P., & Chelliah, B. J. (2023). Enhanced reptile search optimization with convolutional autoencoder for soil nutrient classification model. PeerJ.
  • Huyut, M. T., & Velichko, A. (2023). Lognnet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of covid-19. MethodsX.
  • Payot, N., Pasquato, M., Travan, A., Marsili, E., & Bianconi, G. (2023). Active learning in fractal decision boundaries. arXiv-cs.LG.
  • Yamaguchi, T., Takahashi, H., Nakagawa, Y., & Arai, T. (2001). Speeding up reinforcement learning using chaotic evolutionary computation for a driver’s support display system. Unknown.
  • Pathak, J., Hunt, B. R., Girvan, M., Lu, Z., & Ott, E. (2018). Model-free prediction of large spatiotemporally chaotic systems from data: A hybrid multiple model framework. Chaos (Woodbury, N.Y.).
  • Yasuda, K., Matsumoto, Y., Iwata, K., & Hasegawa, T. (2020). Data-driven modeling for chaotic origami dynamics prediction with machine learning. Unknown.
  • Yang, Y., Huang, D.-S., Huang, H., Guo, J.-Y., Li, Y., & Fang, H. (2022). Hybrid method using Havok analysis and machine learning for chaotic time series prediction. IEEE Access.
  • Mogaraju, J. K. (2024). Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India. Turkish Journal of Engineering, 8(1), 31-45. https://doi.org/10.31127/tuje.1223779
  • Abdullah, T. A. A., Zahid, M. S. M., & Ali, W. (2021). A review of interpretable ML in healthcare: Taxonomy, applications, challenges, and future directions. Symmetry, 13, 2439. https://doi.org/10.3390/sym13122439
  • Yu, H. Q., Alaba, A., & Eziefuna, E. (2024). Evaluation of integrated XAI frameworks for explaining disease prediction models in healthcare. In: Qi, J., & Yang, P. (Eds.) Internet of Things of Big Data for Healthcare. IoTBDH 2023. Communications in Computer and Information Science, vol 2019. Springer, Cham. https://doi.org/10.1007/978-3-031-52216-1_2
  • Karim, A., Mishra, A., Newton, M. A., & Sattar, A. (2018). Machine learning interpretability: A science rather than a tool. arXiv preprint arXiv:1807.06722.
  • Zhang, C., Jiang, J., Qu, S.-X., & Lai, Y.-C. (2020). Predicting phase and sensing phase coherence in chaotic systems with machine learning. Chaos, 30(8), 083114. https://doi.org/10.1063/5.000630
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Pekiştirmeli Öğrenme
Bölüm Articles
Yazarlar

Cem Özkurt 0000-0002-1251-7715

Erken Görünüm Tarihi 28 Ekim 2024
Yayımlanma Tarihi 31 Ekim 2024
Gönderilme Tarihi 20 Nisan 2024
Kabul Tarihi 27 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 4

Kaynak Göster

APA Özkurt, C. (2024). Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. Turkish Journal of Engineering, 8(4), 656-665. https://doi.org/10.31127/tuje.1471445
AMA Özkurt C. Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. TUJE. Ekim 2024;8(4):656-665. doi:10.31127/tuje.1471445
Chicago Özkurt, Cem. “Interpretable AI Analysis of Chaos Systems Distribution in Time Series Data from Industrial Robotics”. Turkish Journal of Engineering 8, sy. 4 (Ekim 2024): 656-65. https://doi.org/10.31127/tuje.1471445.
EndNote Özkurt C (01 Ekim 2024) Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. Turkish Journal of Engineering 8 4 656–665.
IEEE C. Özkurt, “Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics”, TUJE, c. 8, sy. 4, ss. 656–665, 2024, doi: 10.31127/tuje.1471445.
ISNAD Özkurt, Cem. “Interpretable AI Analysis of Chaos Systems Distribution in Time Series Data from Industrial Robotics”. Turkish Journal of Engineering 8/4 (Ekim 2024), 656-665. https://doi.org/10.31127/tuje.1471445.
JAMA Özkurt C. Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. TUJE. 2024;8:656–665.
MLA Özkurt, Cem. “Interpretable AI Analysis of Chaos Systems Distribution in Time Series Data from Industrial Robotics”. Turkish Journal of Engineering, c. 8, sy. 4, 2024, ss. 656-65, doi:10.31127/tuje.1471445.
Vancouver Özkurt C. Interpretable AI analysis of chaos systems distribution in time series data from industrial robotics. TUJE. 2024;8(4):656-65.
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