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Using long short term memory and convolutional neural networks for driver drowsiness detection

Research article (Accident Analysis & Prevention, 2021) · cited 88× · AI/ML
academia openalex:W3155738052
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Using long short term memory and convolutional neural networks for driver drowsiness detection

Summary

Using long short term memory and convolutional neural networks for driver drowsiness detection is a scholarly article[1].

Key Facts

  • Using long short term memory and convolutional neural networks for driver drowsiness detection's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Using long short term memory and convolutional neural networks for driver drowsiness detection. Retrieved May 24, 2026, from https://4ort.xyz/entity/using-long-short-term-memory-and-convolutional-neural-networks-for-driver-drowsiness-detection
MLA “Using long short term memory and convolutional neural networks for driver drowsiness detection.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/using-long-short-term-memory-and-convolutional-neural-networks-for-driver-drowsiness-detection.
BibTeX @misc{4ortxyz_using-long-short-term-memory-and-convolutional-neural-networks-for-driver-drowsiness-detection_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Using long short term memory and convolutional neural networks for driver drowsiness detection}}, year = {2026}, url = {https://4ort.xyz/entity/using-long-short-term-memory-and-convolutional-neural-networks-for-driver-drowsiness-detection}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Using long short term memory and convolutional neural networks for driver drowsiness detection — https://4ort.xyz/entity/using-long-short-term-memory-and-convolutional-neural-networks-for-driver-drowsiness-detection (retrieved 2026-05-24)

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