Summary of Indoor Air Quality Dataset with Activities Of Daily Living in Low to Middle-income Communities, by Prasenjit Karmakar et al.
Indoor Air Quality Dataset with Activities of Daily Living in Low to Middle-income Communities
by Prasenjit Karmakar, Swadhin Pradhan, Sandip Chakraborty
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this study, researchers focus on understanding the impact of indoor air pollution in developing countries like India. They collect data from 30 indoor sites across four regions over six months, covering different types of environments such as apartments, classrooms, and laboratories. The dataset provides a unique opportunity for machine learning (ML) models to learn about complex patterns of pollutants under various indoor activities. To handle missing data caused by power outages or network issues, advanced data cleaning and imputation techniques are required. Additionally, real-time indoor activity labels are provided through a simple speech-to-text application, allowing researchers to identify recurring sources of pollution, forecast exposure, and develop systems that can improve indoor designs and recommend pollution-aware solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Indoor air pollution is a big problem in India and other developing countries. It’s bad for people’s health and causes many deaths every year. This study looked at how indoor air pollution affects different parts of India, like cities and countryside. They collected data from 30 places over six months to see what kinds of pollutants are present and why. They also found a way to label the data with information about what people were doing inside those places. This dataset can be used by researchers who work on machine learning and environmental issues to develop new solutions that can help reduce indoor air pollution. |
Keywords
* Artificial intelligence * Machine learning
