Abstract
Air quality information has drawn a lot of attention in every part of the world. People nowadays are more concerned about their health, among them children are at great risk as their lungs are developing at young age and increase in air pollutants will deteriorate their health. Therefore, air quality monitoring stations are placed to examine the air quality and to predict future air quality. In this regard, our research is focused on air quality monitoring, examination and prediction. As we know that air pollution is not a static problem, rather it is spatio-temporal problem as it changes from time to time and location to location. In this regard, a new computational technique named SVM aggregation is proposed for spatio-temporal air pollution analysis. Through knowledge fusion and with the help of SVM aggregation air pollution problem will be addressed systematically from monitoring to examination and future air quality prediction.
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Ali, S. (2018). Knowledge Discovery via SVM Aggregation for Spatio-temporal Air Pollution Analysis. In: Chaki, N., Cortesi, A., Devarakonda, N. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 9. Springer, Singapore. https://doi.org/10.1007/978-981-10-6319-0_16
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DOI: https://doi.org/10.1007/978-981-10-6319-0_16
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