Simulation and Optimization of Artificial Neural Network Based Air Quality Estimator
Shirish pandey1, S.Hasan Saeed2, shailendra kumar3
1Shirish pandey, phd scholar, Deptt of EC,Integral University Lucknow, India.
2S.Hasan Saeed , HOD , department of EC,Integral University ,Lucknow, India.
3Shailendra Kumar, Professor, department of EC,Integral University, Lucknow, India.
Manuscript received on 18 August 2019. | Revised Manuscript received on 23 August 2019. | Manuscript published on 30 September 2019. | PP: 5477-5482 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4985098319/2019©BEIESP | DOI: 10.35940/ijrte.C4985.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: TE-sensor which are generally based on concept of E-nose are specially made to distinguish odours .In the present research work. E-sensor is developed using artificial intelligence technique to identify the concentration of carbon monoxide in a polluted environment. Data record access using Metal oxide sensor. The available data is broken into the number of segments .The length of data segment and the neurons in hidden layer is varied in number to find the optimized model of artificial neural network model using Mat Lab Coding. The artificial neural network model is optimized by verification in terms of mean squared error and regression. The regression is verified for training ,testing, validation and all. The mean squared error and regression are the artificial neural network model performance parameter.
Keywords: E-Nose, Air Quality
Scope of the Article: Simulation Optimization and Risk Management