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Assessment & Forecast of Air Quality data based on Neural Network
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| Author:
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R. AISHWARYA, K. SOWJENYA, SRAVANAPU DEEPIKA
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| Abstract:
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This paper presents data mining methods that utilize measurements characterized on the arrangement of allotments of limited sets. Segments are normally connected with object characteristics and significant data mining issue, for example, classification, clustering and data preparation which profit by a logarithmic and geometric investigation of the measurement space of parcels. Allotments are normally connected with object traits and significant data mining issue, for example, classification, clustering and data preparation which profit by an arithmetical and geometric investigation of the measurement space of parcels. The proposed determination model that links the data mining systems and the neural system calculation depends on the air contamination verification data acquired at the air quality monitoring stations in Shijiazhuang. From the start, this model uses data mining innovation to discover the components that influence air quality. In addition, it uses this elementary data to prepare the neural system. Finally, the evaluation test of the determining model is evaluated. The proposed air quality anticipation model developed at present responds well to the problem of down-to- earth application, because it has higher decisive precision.
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| Keyword:
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Data Mining, Sequential Mining, Frequent Mining, Neural Network.
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| EOI:
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| DOI:
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https://doi.org/10.31838/ijpr/2020.12.01.231
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| Download:
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