Summary of Optimizing Cnn-bigru Performance: Mish Activation and Comparative Analysis with Relu, by Asmaa Benchama and Khalid Zebbara
Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu
by Asmaa Benchama, Khalid Zebbara
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the role of activation functions (AF) in deep learning for intrusion detection. By introducing non-linearities, AF enables neural networks to model complex patterns and relationships in real-world data, enhancing their predictive accuracy across various tasks. The study compares the performance of Mish, a recent AF, with Rectified Linear Unit (ReLU), a widely used AF, using three datasets: ASNM-TUN, ASNM-CDX, and HOGZILLA. The results show that Mish outperforms ReLU, demonstrating its effectiveness in improving the performance of intrusion detection systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how special functions help computers learn to spot bad things on a computer network. It shows that these functions can make the computer better at finding problems and fixing them. The study uses three big sets of data to compare two different kinds of functions. The results show that one function is better than the other, which means it’s more helpful for keeping computers safe. |
Keywords
* Artificial intelligence * Deep learning * Relu
