Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems | Paper Citation Record

Observation d6a9583b-39d2-4835-82bc-e6b9ceeb6fc6 · outbound

This paper cites Procedia CIRP38, 3–7 (2015) https://doi.org/10.1016/j.procir.2015.08.026.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Procedia CIRP38, 3–7 (2015) https://doi.org/10.1016/j.procir.2015.08.026

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This paper cites SSRG International Journal of Electrical and Electronics Engineering11(9), 294–315 (2024) https://doi.org/10.14445/23488379/IJEEE-V11I9P127.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems SSRG International Journal of Electrical and Electronics Engineering11(9), 294–315 (2024) https://doi.org/10.14445/23488379/IJEEE-V11I9P127

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Observation 85a400f4-d9e8-4bd0-8974-c867c74f11b9 · outbound

This paper cites Journal of Artificial Intelligence Research16, 321–357 (2002).

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Journal of Artificial Intelligence Research16, 321–357 (2002)

Reference 3

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This paper cites IEEE Transactions on Knowledge and Data Engineering21(9), 1263–1284 (2009) https://doi.org/10.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems IEEE Transactions on Knowledge and Data Engineering21(9), 1263–1284 (2009) https://doi.org/10

Reference 4

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This paper cites AIP Advances15(2), 020702 (2025) https://doi.org/10.1063/5.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems AIP Advances15(2), 020702 (2025) https://doi.org/10.1063/5

Reference 5

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This paper cites Engineering Applications of Artificial Intelligence163(Part 4), 113068 (2026) https://doi.org/10.1016/j.engappai.2025.113068.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Engineering Applications of Artificial Intelligence163(Part 4), 113068 (2026) https://doi.org/10.1016/j.engappai.2025.113068

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This paper cites Quantum2, 79 (2018) https://doi.org/10.22331/q-2018-08-06-79.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Quantum2, 79 (2018) https://doi.org/10.22331/q-2018-08-06-79

Reference 7

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This paper cites Quantum Machine Intelligence7(1), 38 (2025) https://doi.org/10.1007/s42484-025-00248-6.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Quantum Machine Intelligence7(1), 38 (2025) https://doi.org/10.1007/s42484-025-00248-6

Reference 8

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This paper cites Contemporary Physics56(2), 172–185 (2015) https://doi.org/10.1080/ 00107514.2014.964942.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Contemporary Physics56(2), 172–185 (2015) https://doi.org/10.1080/ 00107514.2014.964942

Reference 9

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This paper cites Nature549(7671), 195–202 (2017) https://doi.org/ 10.1038/nature23474.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Nature549(7671), 195–202 (2017) https://doi.org/ 10.1038/nature23474

Reference 10

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This paper cites Physical Review A98(1), 012324 (2018) https://doi.org/10.1103/PhysRevA.98.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Physical Review A98(1), 012324 (2018) https://doi.org/10.1103/PhysRevA.98

Reference 11

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This paper cites Physical Review A96(6), 062327 (2017) https://doi.org/ 10.1103/PhysRevA.96.062327.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Physical Review A96(6), 062327 (2017) https://doi.org/ 10.1103/PhysRevA.96.062327

Reference 12

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This paper cites Measurement242(Part D), 116216 (2025) https://doi.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Measurement242(Part D), 116216 (2025) https://doi

Reference 13

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This paper cites BMC Bioinformatics14(1), 106 (2013) https://doi.org/10.1186/1471-2105-14-106.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems BMC Bioinformatics14(1), 106 (2013) https://doi.org/10.1186/1471-2105-14-106

Reference 14

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This paper cites Springer Series in Statistics.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Springer Series in Statistics

Reference 15

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This paper cites New Journal of Physics18(2), 023023 (2016) https://doi.org/10.1088/1367-2630/18/2/023023.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems New Journal of Physics18(2), 023023 (2016) https://doi.org/10.1088/1367-2630/18/2/023023

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This paper cites Quantum Science and Technology4(4), 043001 (2019) https://doi.org/10.1088/2058-9565/ab4eb5.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Quantum Science and Technology4(4), 043001 (2019) https://doi.org/10.1088/2058-9565/ab4eb5

Reference 17

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This paper cites Proceedings of the 2005 International Conference on Advances in Intelligent Computing (ICIC’05), Volume Part I, 878–887 (2005) https://doi.org/10.1007/11538059 91.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Proceedings of the 2005 International Conference on Advances in Intelligent Computing (ICIC’05), Volume Part I, 878–887 (2005) https://doi.org/10.1007/11538059 91

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This paper cites IEEE International Joint Conference on Neural Networks, 1322–1328 (2008) https://doi.org/10.1109/IJCNN.2008.4633969 42.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems IEEE International Joint Conference on Neural Networks, 1322–1328 (2008) https://doi.org/10.1109/IJCNN.2008.4633969 42

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems SIGKDD Explorations 6(1), 20–29 (2004) https://doi.org/10.1145/1007730.100773

Reference 20

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This paper cites Vibration6(1), 218–238 (2023) https://doi.org/10.3390/vibration6010014.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Vibration6(1), 218–238 (2023) https://doi.org/10.3390/vibration6010014

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This paper cites Applied Sciences15(21), 11303 (2025) https://doi.org/10.3390/app152111303.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Applied Sciences15(21), 11303 (2025) https://doi.org/10.3390/app152111303

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Resolution: verified exact; doi, observed 2026-08-03T10:08:27.224893Z

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This paper cites 2019 International Conference on Control, Automation and Diagnosis (ICCAD) (2019) https://doi.org/10.1109/ ICCAD46983.2019.9037949.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems 2019 International Conference on Control, Automation and Diagnosis (ICCAD) (2019) https://doi.org/10.1109/ ICCAD46983.2019.9037949

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This paper cites Technical Report, IBM Research (2001).

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Technical Report, IBM Research (2001)

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems IEEE Transactions on Neural Networks10(5), 988–999 (1999) https://doi.org/10.1109/72.788640

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Machine Learning45(1), 5–32 (2001) https://doi

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Intriguing properties of neural networks

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This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

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This paper cites Measurement257(Part A), 118564 (2026) https://doi.org/10.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Measurement257(Part A), 118564 (2026) https://doi.org/10

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Unresolved cited work

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Physical Review Letters122(4), 040504 (2019) https://doi.org/10.1103/ PhysRevLett.122.040504

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems Learning Robust Observable to Address Noise in Quantum Machine Learning

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems https://www.kaggle.com/datasets/ pythonafroz/solar-panel-images

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This paper cites https://www.kaggle.com/datasets/brjapon/ cwru-bearing-datasets.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems https://www.kaggle.com/datasets/brjapon/ cwru-bearing-datasets

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This paper cites https://www.kaggle.com/datasets/ ziya07/engine-failure-detection-dataset.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems https://www.kaggle.com/datasets/ ziya07/engine-failure-detection-dataset

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This paper cites https://www.kaggle.com/ datasets/programmer3/industrial-fault-detection-dataset.

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems https://www.kaggle.com/ datasets/programmer3/industrial-fault-detection-dataset

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Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems IEEE Internet of Things Journal 11(3), 3840–3852 (2023) https://doi.org/10.1109/JIOT.2023.3300577 44

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