A Comparative Analysis of Air Pollution Detection Technique using Image Processing, Machine Learning and Deep Learning Approach

Samia Sultana

A Comparative Analysis of Air Pollution Detection Technique using Image Processing, Machine Learning and Deep Learning Approach

Samia Sultana *

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A Comparative Analysis of Air Pollution Detection Technique using Image Processing, Machine Learning and Deep Learning Approach

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Abstract

Air pollution is one of the principal environmental issues for the industrial emission and infection of the atmosphere which is caused by the climatic and traffic elements, burning of fossil fuels, etc. For the past several years, various methods and models have been discovered to detect the pollution of the air. In this paper, among all of those, three mechanisms have been focused, which are image processing approach, machine learning, and deep learning technique. A comparative study has developed among these three methods to detect the pollutant of the air in the account of time, cost and efficiency so that different scenario and system can choose the best method according to their need. The objective of this paper is to assimilate the procedure of these methods in brief and utilize this study to estimate the best solution for the corresponding requirement of any particular circumstances.

References

10 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Samia Sultana. 2019. "A Comparative Analysis of Air Pollution Detection Technique using Image Processing, Machine Learning and Deep Learning Approach". Global Journal of Computer Science and Technology - A: Hardware & Computation GJCST-A Volume 19 (GJCST Volume 19 Issue A1).

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GJCST Volume 19 Issue A1

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-A Classification I.2.m

Submission ReceivedDecember 12, 2018
Peer Review Double Blind
Handling Editor
Accepted January 3, 2019
Published January 15, 2019

Version of record

v1.2

Issue date

September 17, 2019

Language

English

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Article Matrices

Total Score: 141

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology

Authors: Samia Sultana (PhD/Dr. count: 0)

View Count (all-time): 430

Total Views (Real + Logic): 1272

Total Downloads (simulated): 82

Publish Date: 2019 01, Tue

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Total Views: 1.3K

Total Downloads: 82

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