Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

By Te Han, Dongxiang Jiang, Qi Zhao, Lei Wang, Kai Yin. Transactions of the Institute of Measurement and Control, 2018

341 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/b50e10b7efba5f272aec70ad8cad76a2aa086f1a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liang Gao
2. Xinyu Li
3. Long Wen

In Control and Systems Engineering

1. Liang Gao
2. Xinyu Li
3. Long Wen
4. Dhiraj Neupane
5. Jong-Hoon Seok

Citations in the corpus

• A transfer convolutional neural network for fault diagnosis based on ResNet-50 (2019, methodology): “Han et al. [44] applied fivefold cross-validation with ANN and SVM for intelligent diagnosis of rotating machinery.”
• Nanomedicine-Driven Modulation of the Gut–Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer’s and Parkinson’s Disease (2025, unknown): “Similarly, in neurological disease diagnoses, RF has consistently outperformed single classifiers due to its robustness in handling outliers and its capacity for intrinsic feature selection [149,152].”