A Comparative Study on Air Quality Analysis Through DNN by SVM, K - Means and Naive Bayes Algorithms

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-11
Year of Publication : 2019
Authors : R.Amulraju, D. Ashok Kumar, T.Vithyaa
DOI :  10.14445/22312803/IJCTT-V67I11P107


MLA Style:R.Amulraju, D. Ashok Kumar, T.Vithyaa "A Comparative Study on Air Quality Analysis Through DNN by SVM, K - Means and Naive Bayes Algorithms," International Journal of Computer Trends and Technology 67.11 (2019):42-48.

APA Style R.Amulraju, D. Ashok Kumar, T.Vithyaa. A Comparative Study on Air Quality Analysis Through DNN by SVM, K - Means and Naive Bayes Algorithms  International Journal of Computer Trends and Technology, 67(11),42-48.

Air Quality may be a major concern round the world. It’s full of a large vary of natural and human influences. The foremost necessary of the natural influences area unit geologic, hydrological and environmental condition, since these have an effect on the standard of Air. To invoke a deep neural network (DNN)-based approach (entitled Deep Air), that consists of a spatial transformation part and a deep distributed fusion network. Considering air pollutants’ spatial correlations, the previous part converts the spatial thin air quality knowledge into a homogenous input to simulate the waste product sources. The latter network adopts a neural distributed design to fuse heterogeneous urban knowledge for at the same time capturing the factors touching air quality, e.g. environmental condition. To Deployed Deep Air in our pollution Prediction system, providing fine-grained air quality forecast. Additionally we tend to confirm the precise results and analysis of the contaminated contents victimization K-Means cluster, SVM Classifier and Naive Bayes. Comparison the contaminated content results with these three processes the K-Means provides the proper result and determines the precise output with facilitate of the datasets. The collected datasets area unit pre-processed and classified to induce the proper results. Finally the results area unit manipulated in associate graph format, that exposes the ends up in associate correct manner.

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Air Quality, K-means, SVM, Naïve Bayes, Precision, Recall, F-Measure