Summary of Semai: Semantic Artificial Intelligence-enhanced Dna Storage For Internet-of-things, by Wenfeng Wu et al.
SemAI: Semantic Artificial Intelligence-enhanced DNA storage for Internet-of-Things
by Wenfeng Wu, Luping Xiang, Qiang Liu, Kun Yang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed Semantic Artificial Intelligence-enhanced DNA storage (SemAI-DNA) paradigm leverages AI to optimize DNA storage for cloud applications, addressing the challenges of encoding nuanced semantic information and ensuring system fault tolerance. By embedding a semantic extraction module at the encoding terminus and developing a multi-reads filtering model at the decoding terminus, SemAI-DNA outperforms conventional deep learning-based approaches, achieving 2.61 dB Peak Signal-to-Noise Ratio (PSNR) gain and 0.13 improvement in Structural Similarity Index (SSIM). This paper presents a novel approach to DNA storage that can benefit from IoT-driven data growth. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DNA storage is becoming a promising solution for cloud applications due to rapid technological advancements. A new AI-enhanced method called SemAI-DNA has been developed, which improves DNA storage by accurately encoding semantic information and making systems more reliable. This method uses special modules at the start and end of the DNA storage process. Results show that this approach is better than usual AI-based methods, achieving higher quality in terms of image quality. |
Keywords
» Artificial intelligence » Deep learning » Embedding
