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Title	An Ensemble Based Machine Learning Approach For Traffic Prediction In Smart City
ID_Doc	8063
Authors	Jenifer J.; Priyadarsini R.J.
Year	2021
Published	2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021
DOI	http://dx.doi.org/10.1109/ICAECA52838.2021.9675631
Abstract	In recent decades, the Internet-of-things (IoT) are becoming more popular among the various industrial applications such as smart home, healthcare, industry, academia, etc. IoT is an epideictic keyword for development of internet and electronic devices. The electronic devices which are embedded with sensing mechanisms for exploiting its extended abilities. This produces huge amount of data and its termed as Big Data. Extracting the hidden information from big data is most challengeable for industrialists and researchers. Machine Learning (ML) is an important resource for gaining insights from a wide range of data. In IoT, ML models are being used to predict the device behavior, pattern for the real-time and streaming data for taking better decision. This work concentrates on building an ensemble method by hybridizing bagging and boosting methodologies. The proposed ensemble classifier is evaluated using smart-city dataset available in Kaggle. The results are having good improvement when compared with the standalone machine learning models. © 2021 IEEE.
Author Keywords	Bagging; Boosting; Internet of Things; Machine Learning; Random Forest; XGBoost

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