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Title	Anomaly Detection In Indoor Localization Using Machine Learning
ID_Doc	9616
Authors	Chandana P.; Aishwarya Ch.; Muskan S.S.
Year	2021
Published	Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021
DOI	http://dx.doi.org/10.1109/ICIRCA51532.2021.9544956
Abstract	One of the most concerning topics in smart cities is the internet of things, namely wireless sensor networks in indoor localization. Wi-Fi with received signal strengths (RSSs) is one of the most used indoor localization techniques. It generates signal intensity irregularities in Wi-Fi RSSs owing to reflection, refraction, incursion, and channel noise. RSS values cannot be defined (the location of each unknown node must be accurately stated). Here, Asymmetrical, abnormal circumstances are present in the Wi-Fi indoor localization area. Matplotlib, numpy, pandas, ploty, scipy, seaborn, scikitlearn, wordcloud, statsmodels, and streamlit were utilized in this study. © 2021 IEEE.
Author Keywords	Machine learning; random forest

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