Transformer Fault Diagnosis Using Deep Boltzmann Machine and Multiclass Relevance Vector Machines-Reference-Cited by-同舟云学术

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Transformer Fault Diagnosis Using Deep Boltzmann Machine and Multiclass Relevance Vector Machines

Published:2024-04-26 Issue: Volume: Page:1-9
ISSN:
Container-title:2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)
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Author:

Sudhakar A.V.V.¹,Satish E.G.²,Shravani B.³,Sharma Rakesh⁴,Al-Jawahry Hassan M.⁵

Affiliation:

1. SR University,Department of Electrical and Electronics Engineering,Warangal,India

2. Nitte Meenakshi Institute of Technology,Dept.of CSE,Bengaluru,India

3. Srinivasa Ramanujan Institute of Technology (Autonomous),Department of EEE,Ananthapuramu,India

4. Srinivas Institute of Technology,Department of CSE,Mangalore

5. College of Technical Engineering The Islamic University,Department of Computers Techniques Engineering,Najaf,Iraq

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10548017/10548035/10548726.pdf?arnumber=10548726

Reference13 articles.

1. Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning

2. Adult content image recognition by Boltzmann machine limited and deep learning

3. Application of XGBoost model for early prediction of earthquake magnitude from waveform data

4. Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines

5. Adult content image recognition by Boltzmann machine limited and deep learning

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