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Home All issues Volume 11 (2024) Manufacturing Rev., 11 (2024) 11 References
Open Access
Issue
Manufacturing Rev.
Volume 11, 2024
Article Number 11
Number of page(s) 15
DOI https://doi.org/10.1051/mfreview/2024008
Published online 19 April 2024
  1. P. Chommuangpuck, T. Wanglomklang, S. Tantrairatn, J. Srisertpol, Fault tolerant control based on an observer on PI servo design for a high-speed automation machine, Machines 8 (2020) 22 [CrossRef] [Google Scholar]
  2. P. Chommuangpuck, T. Wanglomklang, J. Srisertpol. Fault detection and diagnosis of linear bearing in auto core adhesion mounting machines based on condition monitoring, Syst. Sci. Control Eng. 9 (2021) 290–303 [CrossRef] [Google Scholar]
  3. T. Wanglomklang, P. Chommaungpuck, K. Chomniprasart, J. Srisertpol. Using fault detection and classification techniques for machine breakdown reduction of the HGA process caused by the slider loss defect, Manufactur. Rev. 9 (2022) 21 [CrossRef] [EDP Sciences] [Google Scholar]
  4. C. Sapapporn, S. Seangsri, J. Srisertpol, Classifying and optimizing spiral seed self-servo writer parameters in manufacturing process using artificial intelligence techniques, Systems 11 (2023) 268 [CrossRef] [Google Scholar]
  5. S.J. Wagh, M.S. Bhende, A.D. Thakare, Fundamentals of data science, 1st edn. Taylor & Francis Group, London, 2021, 296 pp [Google Scholar]
  6. M.U. Malik, Python scikit-learn for beginners: Scikit-learn specialization for data scientist, 1st edn. AI Publishing, Michigan, 2021, 405 pp [Google Scholar]
  7. A.D. Mauro, Data analytics made easy, 1st edn. Packt Publishing, UK, 2021, 406pp [Google Scholar]
  8. G. Wang, L. Zhang, W. Xu, What can we learn from four years of data center hardware failures? in 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, IEEE (2017) [Google Scholar]
  9. A. Lawi, S.L. Wungo, S. Manjang, Identifying irregularity electricity usage of customer behaviors using logistic regression and linear discriminant analysis, in 2017 3rd International Conference on Science in Information Technology (ICSITech), IEEE (2017) [Google Scholar]
  10. G.B.G. Pereira, L.P. Fernandes, J.M.R. d.S., Neto, H.D d.M.., Braz, L. da Silva Sauer, A comparative study of linear discriminant analysis and an artificial neural network performances in breast cancer diagnosis, in 2020 IEEE Andean Conference, IEEE (2020) [Google Scholar]
  11. A. Singh, B.S. Prakash, K. Chandrasekaran, A comparison of linear discriminant analysis and ridge classifier on Twitter data, in International Conference on Computing, Communication and Automation (ICCCA2016), IEEE (2016) [Google Scholar]
  12. R.K. Grace, M.I. Priyadharshini, Wind speed prediction using extra tree classifier, in International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), IEEE (2023) [Google Scholar]
  13. B. Dhananjay, N.P. Venkatesh, A. Bhardwaj, J. Sivaraman, Cardiac signals classification based on extra trees model, in 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE (2021). [Google Scholar]
  14. J. Shen, J. Wan, S.J. Lim, L. Yu, Random-forest-based failure prediction for hard disk drives, Int. J. Distrib. Sens. Netw. 14 (2018) [Google Scholar]
  15. O. Alshboul, A. Shehadeh, G. Almasabha, A.S. Almuflih, Extreme gradient boosting-based machine learning approach for green building cost prediction, Sustainability 14 (2022) 6651 [CrossRef] [Google Scholar]
  16. K. M, V.S. Akshaya, A XGBOOST Based algorithm for early prediction of human sepsis, in 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE (2022) [Google Scholar]
  17. M.E. Irawati, H. Zakaria, Classification model for Covid-19 detection through recording of cough using XGboost classifier algorithm, in 2021 International Symposium on Electronics and Smart Devices (ISESD), IEEE (2021) [Google Scholar]
  18. Q. Li, H. Li, K. Zhang, Prediction of HDD failures by ensemble learning, in 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), IEEE (2020) [Google Scholar]
  19. Z. Miller, O. Medaiyese, M. Ravi, A. Beatty, F. Lin, Hard disk drive failure analysis and prediction: an industry view, in 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks − Supplemental Volume (DSN-S), IEEE (2023) [Google Scholar]
  20. P. Bruce, A. Bruce, P. Gedeck, Practical statistics for data scientists, Published by O’Reilly Media, Inc. 2020 [Google Scholar]
  21. Z.H. Nasiruddin, W.M. Diyana W Zaki, S.A. Hudaibah, A.H. Nur Asyiqin, Automated retinal blood vessel feature extraction in digital fundus images, in 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), IEEE (2022) [Google Scholar]
  22. I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, N. Doulamis, Multiclass confusion matrix reduction method and its application on net promoter score classification problem, Technologies 9 (2021) 81 [CrossRef] [Google Scholar]
  23. T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 861–874 (2006) 861–874 [Google Scholar]
  24. S. Yang, G. Berdine, The receiver operating characteristic (ROC) curve, Southwest Respirat. Critical Care Chronicles 5 (2017) 34–36 [Google Scholar]
  25. L. Zhang and N. Hu, ROC analysis based condition indicator threshold optimization method, in 2017 Prognostics and System Health Management Conference (PHM-Harbin), IEEE (2017) [Google Scholar]
  26. H. Wan, Q. Liu, Y. Ju, Utilize a few features to classify presynaptic and postsynaptic neurotoxins, Comput. Biol. Med. 152 (2023) 106380 [CrossRef] [Google Scholar]
  27. P. Kumar, M. Sharma, Feature-importance feature-interactions (FIFI) graph: A graph-based novel visualization for interpretable machine learning, in 2021 International Conference on Intelligent Technologies (CONIT), IEEE (2021) [Google Scholar]
  28. J. Yu, C. Xia, H. Zhang, Research on feature importance of gait mechanomyography signal based on random forest, in 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), IEEE (2020) [Google Scholar]

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