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An Ensemble Approach of Multi-objective Differential Evolution Based Benzene Detection

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Futuristic Trends in Network and Communication Technologies (FTNCT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1395))

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Abstract

Benzene is among the most common and menacing contaminant in the air that accelerate the rate of severe health issues among people. Presently, environmental sensor-based networks are utilized to monitor the quality of the air. The cost including numerous sensors with dynamic network sizes limit the operational and monitoring efficiency. In the proposed study, the advanced non-linear problem-solving principles of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Differential Evolution (DE) algorithm is utilized to monitor the quality of the air and to predict the scale of \(C_{6}H_{6}\) in the surrounding environment of the individual without installing or creating any sensor-based network. The concentration of \(C_{6}H_{6}\) in the air is predicted by utilizing ANFIS through which evaluation of the relationship between several atmospheric gases is accomplished and DE is responsible to optimize the parameters of the ANFIS model for effective prediction accuracy. The prediction performance of the system is evaluated by calculating Accuracy, Coefficient of Determination (\(r^{2}\)), and Root Mean Squared Error (RMSE) on five publicly available datasets. To validate the experimental results of the proposed system, the calculated results are compared with several base-line and hybrid methods of machine learning. The calculated outcomes justify the suitability of building self-reliable cost-effective and time-sensitive air monitoring system for predicting the concentration of benzene in the air.

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Notes

  1. 1.

    Source: https://www.who.int/en/news-room/actualitysheets/detail/encompassing(outside)-air-quality-and-wellbeing.

  2. 2.

    Source: https://www.lung.org/about-us/media/officialstatements/2018-condition-of-the-air.html.

  3. 3.

    https://pubchem.ncbi.nlm.nih.gov/compound/benzene.

  4. 4.

    Source: http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-qualitydatabase-8.

  5. 5.

    Source: https://github.com/PetraVidnerova/SensorsScikitTest/blob/master/data/C6H6-nrm-part5.test.csv.

  6. 6.

    Source: https://daac.ornl.gov/NACP/guides/NACPGHGDataCompilation.html.

  7. 7.

    https://www.nist.gov/file/36031.

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Authors and Affiliations

  1. Lovely Professional University, Punjab, India

    Veerawali Behal & Ramandeep Singh

Authors
  1. Veerawali Behal
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  2. Ramandeep Singh
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Editor information

Editors and Affiliations

  1. ABES Engineering College, Ghaziabad, India

    Pradeep Kumar Singh

  2. Southern Federal University, Rostov-on-Don, Russia

    Gennady Veselov

  3. Luleå University of Technology, Luleå, Sweden

    Valeriy Vyatkin

  4. Southern Federal University, Rostov-on-Don, Russia

    Anton Pljonkin

  5. University of Cádiz, Cádiz, Spain

    Juan Manuel Dodero

  6. Jaypee Institute of Information Technology, Waknaghat, India

    Yugal Kumar

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Behal, V., Singh, R. (2021). An Ensemble Approach of Multi-objective Differential Evolution Based Benzene Detection. In: Singh, P.K., Veselov, G., Vyatkin, V., Pljonkin, A., Dodero, J.M., Kumar, Y. (eds) Futuristic Trends in Network and Communication Technologies. FTNCT 2020. Communications in Computer and Information Science, vol 1395. Springer, Singapore. https://doi.org/10.1007/978-981-16-1480-4_23

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  • DOI: https://doi.org/10.1007/978-981-16-1480-4_23

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  • Online ISBN: 978-981-16-1480-4

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