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Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution

Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution

Nagarathna Ravi, Vimala Rani P, Rajesh Alias Harinarayan R, Mercy Shalinie S, Karthick Seshadri, Pariventhan P
Copyright: © 2019 |Volume: 15 |Issue: 4 |Pages: 32
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522564355|DOI: 10.4018/IJIIT.2019100105
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MLA

Ravi, Nagarathna, et al. "Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution." IJIIT vol.15, no.4 2019: pp.76-107. http://doi.org/10.4018/IJIIT.2019100105

APA

Ravi, N., Vimala Rani P, Rajesh Alias Harinarayan R, Mercy Shalinie S, Seshadri, K., & Pariventhan P. (2019). Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution. International Journal of Intelligent Information Technologies (IJIIT), 15(4), 76-107. http://doi.org/10.4018/IJIIT.2019100105

Chicago

Ravi, Nagarathna, et al. "Deep Learning-based Framework for Smart Sustainable Cities: A Case-study in Protection from Air Pollution," International Journal of Intelligent Information Technologies (IJIIT) 15, no.4: 76-107. http://doi.org/10.4018/IJIIT.2019100105

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Abstract

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

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