| Title |
DMAPS: An Effective and Efficient Way for the Air Purification of the Outdoors (Deep-mind Air Purification System for a smart city) |
| ID_Doc |
42731 |
| Authors |
Vashishtha, P; Choudhury, T |
| Title |
DMAPS: An Effective and Efficient Way for the Air Purification of the Outdoors (Deep-mind Air Purification System for a smart city) |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.procs.2020.03.233 |
| Abstract |
The air pollution these days is a serious environmental concern and it is not just a mere fact but a harsh reality which is creating problems for the mankind such as some serious health issues. In some parts of the world the air quality index have reached to an irrefutable level which demands for a solution now. Hence, the purpose of this paper is to tackle this problem at a large scale by providing with an artificially intelligent mobile air purifier for "outdoors". The main problem of DMAPS is to provide with purified air in the outdoors, such as a residential colony, in apartments, office complex, etc. since most of the times people travel outdoors and stay away from their home for a long time. This ability of providing with such a smart-sensing, self-driving machine is the advantage of the paper because other air purifiers exist but they are made for indoors, they do not have mobility and also do not possess AI capabilities. The method used in this air purifier is formulated by connecting three major functions. The first one being, 'the smart mobility' of the machine using the approach of deep reinforcement learning, the second is the air 'purification using the Arduino system' and the last is, 'the detection of humans' so that DMAPS can be around them and provide them with purified air, based on the YOLO algorithm. The whole system, works in a way that each and every function is very much connected and works simultaneously. In a nutshell, this air purifier, moves from one place (where there is low pollution content) to another place (where there is higher pollution content), purifies the environment and simultaneously have an objective to smartly detect humans to give out fresh air to them. Hence, in this way the whole methodology tries to fix the problem of air pollution by purifying the air outdoors to make it safe to inhale. (C) 2020 The Authors. Published by Elsevier B.V. |
| Author Keywords |
Deep reinforcement learning; Arduino; YOLO algorithm; artificially intelligent |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000582710700036 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://doi.org/10.1016/j.procs.2020.03.233
|