Shedding Light on the Black Box: Explainable AI for Predicting Household Appliance Failures

Author:

Falatouri TahaORCID,Nasseri MehranORCID,Brandtner PatrickORCID,Darbanian FarzanehORCID

Publisher

Springer Nature Switzerland

Reference30 articles.

1. Islam, M.R., Ahmed, M.U., Barua, S., Begum, S.: A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci. 12, 1353 (2022). https://doi.org/10.3390/app12031353

2. Brandtner, P.: Predictive analytics and intelligent decision support systems in supply chain risk management—research directions for future studies. In: Yang, X.-S., Sherratt, S., Dey, N., Joshi, A. (eds.) Proceedings of Seventh International Congress on Information and Communication Technology, vol. 464. Lecture Notes in Networks and Systems, pp. 549–558. Springer Nature Singapore, Singapore (2023). https://doi.org/10.1007/978-981-19-2394-4_50

3. Haque, A.B., Islam, A.N., Mikalef, P.: Explainable Artificial Intelligence (XAI) from a user perspective: a synthesis of prior literature and problematizing avenues for future research. Technol. Forecast. Soc. Chang. 186, 122120 (2023). https://doi.org/10.1016/j.techfore.2022.122120

4. Joyce, D.W., Kormilitzin, A., Smith, K.A., Cipriani, A.: Explainable artificial intelligence for mental health through transparency and interpretability for under-standability. NPJ Dig. Med. (2023). https://doi.org/10.1038/s41746-023-00751-9

5. Falatouri, T., Farzaneh, D., Brandtner, P., Udokwu, C.: Predictive analytics for demand forecasting – a comparison of SARIMA and LSTM in retail SCM. In: Proceedings of International Conference on Industry 4.0 and Smart Manufacturing (ISM). International Conference on Industry 4.0 and Smart Manufacturing (ISM) (2021). https://doi.org/10.1016/j.procs.2022.01.298

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