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- W3203182011 abstract "The rapid development in the field of System of Chip (SoC) technology,Internet of Things (IoT), cloud computing, and artificial intelligence hasbrought more possibilities of improving and solving the current problems. Withdata analytics and the use of machine learning/deep learning, it is madepossible to learn the underlying patterns and make decisions based on what waslearned from massive data generated from IoT sensors. When combined with cloudcomputing, the whole pipeline can be automated, and free of manual controls andoperations. In this paper, an implementation of an automated data engineeringpipeline for anomaly detection of IoT sensor data is studied and proposed. Theprocess involves the use of IoT sensors, Raspberry Pis, Amazon Web Services(AWS) and multiple machine learning techniques with the intent to identifyanomalous cases for the smart home security system." @default.
- W3203182011 created "2021-10-11" @default.
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- W3203182011 date "2021-09-28" @default.
- W3203182011 modified "2023-09-27" @default.
- W3203182011 title "An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data" @default.
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