| Autor: |
Kumar, G. Joselin Retna, Gunasekar, S., Dubey, Suyash, Chaudhari, Atharv, Agbulu, G. Pius |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 1/6/2023, Vol. 2427 Issue 1, p1-7, 7p |
| Abstrakt: |
The severe hazard of atmospheric air pollution to humans has made air quality tracking and prediction of significant focus lately. Consequently, the Indian government has established specific air quality indexes (AQI's) to communicate and forecast atmospheric air pollution levels across major cities. However, the air quality index measures require accurate on-target sensor learning and sophisticated statistics. Typically, this makes precise outputs difficult for portable air quality monitoring systems. Therefore, this study proposes an alternative parallel image processing technique with supervised machine learning algorithms that capture images from nature and use image processing to track particulate matter PM2.5 concentration. Ultimately, the findings of the study indicate that the proposed solution is accurate by using a parallel image processing technique. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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