@inproceedings{e04bc306470a41cb89f5168c6f887972,
title = "A Comparative Study on Prediction of PM2.5 Level Using Optimization Techniques",
abstract = "Atmospheric particulate matter which is generally known as PM2.5 and its variants consists of solid as well as liquid components suspending in stagnant air in our environment. If concentration of PM2.5 like particulate matter which is made up of very minute particles exceeds its limit leading to serious health problems like lungs problems in Humans as well as adverse impacts on Environment. Machine learning techniques can be used to efficiently train a model on data and improve its efficiency using Optimization Techniques. This work aims to Predict PM2.5 levels accurately in minimum time; Multiple Optimization techniques are explored here mainly Gradient Descent variants are applied using Linear Regression on meteorological data collected from a weather station in India. Our Study has showed that the AdaGrad achieve better performance with least error rate than other Optimization techniques.",
author = "Abhishek Kashyap and Soumyalatha Naveen and Ashwinkumar, \{U. M.\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021 ; Conference date: 09-07-2021 Through 11-07-2021",
year = "2021",
doi = "10.1109/CONECCT52877.2021.9622560",
language = "English",
series = "Proceedings of CONECCT 2021: 7th IEEE International Conference on Electronics, Computing and Communication Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of CONECCT 2021",
address = "United States",
}