Monitoring air quality using the neural network based control chart

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dc.contributor.authorAzmat, Sumaira
dc.contributor.authorSabir, Qurat Ul An
dc.contributor.authorTariq, Saadia
dc.contributor.authorShafqat, Ambreen
dc.contributor.authorRao, G. Srinivasa
dc.contributor.authorAslam, Muhammad
dc.date.accessioned2024-08-17T09:31:31Z
dc.date.available2024-08-17T09:31:31Z
dc.date.issued2023
dc.descriptionAbstract. Full-text available at https://link.springer.com/article/10.1007/s12647-023-00663-9
dc.description.abstractThis paper intends to develop ANN (artificial neural network) based control charts. The (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules, and activated function). Among many models that deal with combining factors and data-based supervised learning classifiers, ANN has the most significant impact on air quality as air quality has nonlinear and noisy data. The best activation of a new hybrid EWMA (HEWMA) control chart is proposed by mixing two EWMA control charts to efficiently monitor the process mean. The ANN-based HEWMA scheme was a promising procedure for the detection of air quality measurements. We compare the performance of the ANN-based HEWMA control chart and the EWMA control chart based on average run lengths when the data are contaminated with the measurement error. The results revealed that the higher the temperature, the better fitting shape we obtain from air quality parameters. The ANN-based HEWMA control chart deals with measurement errors more efficiently than the EWMA control chart.
dc.identifier.citationAzmat, S., Sabir, Q. U. A., Tariq, S., Shafqat, A., Rao, G. S., & Aslam, M. (2023). Monitoring air quality using the neural network based control chart. MAPAN, 38(4), 885-893.
dc.identifier.doi10.1007/s12647-023-00663-9
dc.identifier.otherDOI: 10.1007/s12647-023-00663-9
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4677
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofMAPAN
dc.subjectArtificial Neural Networks (ANN)
dc.subjectControl charts
dc.subjectHybrid EWMA (HEWMA)
dc.subjectAir quality monitoring
dc.subjectMachine learning (ML)
dc.subjectMeasurement error
dc.titleMonitoring air quality using the neural network based control chart
dc.typeArticle
oaire.citation.issue4
oaire.citation.volume38
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