International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4018 Air quality monitoring using CNN classification R.KALAISELVI 1 , S.PRIYANGA 2 , R.GAJALAKSHMI 3 , S.INDUMATHI 4 , R.RAMYA 5 1 Assistant Professor, Computer Science and Engineering Department, Arasu Engineering College, Kumbakonam, Tamilnadu, India 2 UG Scholar, Computer Science and Engineering Department, Arasu Engineering College, Kumbakonam, Tamilnadu, India 3 UG Scholar, Computer Science and Engineering Department, Arasu Engineering College, Kumbakonam, Tamilnadu, India 4 UG Scholar Computer Science and Engineering Department, Arasu Engineering College, Kumbakonam, Tamilnadu, India 5 UG Scholar Computer Science and Engineering Department, Arasu Engineering College, Kumbakonam, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract: Deep air learning analyzes the quality of air, by capturing an image in an open atmosphere. Later, the captured image will be updated in the dataset .The pixels are pointed in the gray scale conversion and the noise will be removed by using the homomorphism filter. The picture which is already updated will be compared to the new picture by using CNN classification algorithm, which will analyze the quality of air. Example oxygen, carbon-dioxide etc., Air pollution may cause many severe diseases. An efficient air quality monitoring system provides great benefit for human health and air pollution control. The proposed method uses a deep convolution Neural Network (CNN) to classify the natural image in to different categories based on their concentration. The experimental results demonstrate that this method validate the image-based concentration estimation to analyze the quality of air. KEYWORDS: Deep air Learning, CNN classification, Big data. I. INTRODUCTION: In today’s world large amount of data can be generated and at the same time, it should be needed the new forms of data processing that enable enhanced insight, decision making, cost effective and process automation. Those data’s are typically called as the Big Data. A massive volume of structured, semi structured and unstructured data that is so large that it's difficult to process with traditional database and software techniques. Data can be classified as structured, semi structured and unstructured based on how it is stored and managed. In several challenges for urban air computing as the related data have some special Characteristics. There is not a universally accepted judgment to reveal the main causes of the occurrence and dissipation of air pollution. The labeled data of the air-quality-monitor- stations are incomplete, and there exist lots of missing labels. In Logistic Regression avoids data latency to its core. Chance of loss or damage of data[1].Fuzzy logic and Logistic Regression Clustering offers more efficient storage space. Integrating of grouped data may end in failure[2]. Svm algorithm competes with the interpolation data information stored are with insecure compensation[3]. Random Forest the computational cost of training a random forest it