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DC Field | Value | Language |
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dc.contributor.author | Rao P.P. | |
dc.contributor.author | Rao S.P. | |
dc.contributor.author | Ranjan R. | |
dc.date.accessioned | 2021-05-05T10:15:40Z | - |
dc.date.available | 2021-05-05T10:15:40Z | - |
dc.date.issued | 2020 | |
dc.identifier.citation | MPCIT 2020 - Proceedings: IEEE 3rd International Conference on "Multimedia Processing, Communication and Information Technology" , Vol. , , p. 77 - 81 | en_US |
dc.identifier.uri | https://doi.org/10.1109/MPCIT51588.2020.9350390 | |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/14703 | - |
dc.description.abstract | India has witnessed an unprecedented increase in garbage levels in the past 20 years. Massive quantities of waste, particularly solid waste, are generated daily and seldom picked up. Consequently, garbage is being dumped in landfills and water bodies, hence not managed effectively. This mismanagement has detrimental consequences on our environment. Thus, there is a need to develop an efficient system to manage waste. In this paper, an IoT-based, automated smart bin monitoring system is proposed. Moreover, a deep learning model was used to forecast future garbage levels from the data collected. The proposed neural network model was able to predict garbage levels with an accuracy of 80.33%. Results verify the accurate prognosis of garbage levels. Additionally, data were analysed using bar charts. The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbagebins. © 2020 IEEE. | en_US |
dc.title | Deep Learning Based Smart Garbage Monitoring System | en_US |
dc.type | Conference Paper | en_US |
Appears in Collections: | 2. Conference Papers |
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