CNN BASED FEATURE EXTRACTION FOR AIR POLLUTION DETECTION

R. Udaya Shanmuga , Dr.G.Tamilpavai

Feature extraction, CNN (convolution neural networks), SVM (support vector machine), air pollution detection

The introduction of new techniques and methods for rapidly and reliably detecting and analyzing air quality is very essential and demanding for air pollution deduction. Machine learning algorithms equipped with basic features leads to poor and slow performance due to minimal complex image characteristics representation. Now, the deep learning methodology is used as the active and effective AI tool for feature extraction. Extracting complex image features from the given images is the standard and important procedure of feature extraction which utilizes the extracted features to predict the air pollution from the given image dataset. For this purpose convolution neural network is employed in this work. Air Pollution Image Dataset (APID) was created using publicly available camera images. Fully connected layers of CNN divides the features into different categories of different classes based on similarity. The first step is to number the images into 61 groups, and the second step was to group them again into seven groups. The best results were obtained using 4096 features with an accuracy of 67 percent and 95 percent, respectively, for 61 and 7 class groups. This provides an enhanced version of feature extraction and accurate results when compared with existing methods.

Published Paper   E-Certificate

"CNN BASED FEATURE EXTRACTION FOR AIR POLLUTION DETECTION", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 9, page no.891 - 896, September-2022, Available :https://ijsdr.org/papers/IJSDR2209142.pdf

Volume 7 Issue 9, September-2022

Pages : 891 - 896

Paper Reg. ID: IJSDR_201821

Published Paper Id: IJSDR2209142

Downloads: 000347318

Research Area: Engineering

Country: tirunelveli, tamilnadu, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2209142

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2209142

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition