LVQ Based Classification of Air Quality Using Data for Lockdown Period of COVID-19
| Autor: | Pushpa Bhakuni Negi, Sudhanshu Maurya, Pradeep Juneja, Amit Mittal, Deepa Nainwal, Sandeep Kumar Sunori |
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| Rok vydání: | 2021 |
| Předmět: |
Learning vector quantization
Artificial neural network business.industry Computer science Competitive learning Supervised learning Vector quantization Machine learning computer.software_genre Statistical classification Binary classification Unsupervised learning Artificial intelligence business computer |
| Zdroj: | 2021 6th International Conference on Communication and Electronics Systems (ICCES). |
| DOI: | 10.1109/icces51350.2021.9488956 |
| Popis: | The lockdown duration of COVID-19 gave rise to a significant betterment in AQI (Air Quality Index) worldwide. In the present research paper, binary classification problem of the air pollutants data of Uttarakhand, India, for year 2019 and 2020 (lockdown period), has been addressed. This problem is challenging to solve as it is non-linearly separable. Using this data, a neural network has been trained, to perform classification, using competitive learning technique (unsupervised learning). Then, for achieving better classification results, a supervised learning technique, learning vector quantization algorithm (LVQ), is used. Finally, the performance of both the networks is compared. All results are obtained in MATLAB. |
| Databáze: | OpenAIRE |
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