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- W4367354821 abstract "Tiny Machine Learning (TinyML) and the Internet of Things offer a new opportunity for resource-constrained embedded systems. Conventional machine learning requires a lot of processing power to be implemented; the TinyML approach shifts the need for processing power to the cloud and produces lightweight models to run on an embedded device. This paper introduces a TinyML workflow and explains the steps involved in creating a model and deploying it in a resource-constrained environment for an eHealth use case. A dataset of epileptic patients was used to build a model and then implement it on a microcontroller using TinyML A good performance was obtained using a Microcontroller for epileptic seizure prediction." @default.
- W4367354821 created "2023-04-30" @default.
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- W4367354821 date "2023-01-01" @default.
- W4367354821 modified "2023-10-10" @default.
- W4367354821 title "Tiny Machine Learning for IoT and eHealth Applications: Epileptic Seizure Prediction Use Case" @default.
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- W4367354821 doi "https://doi.org/10.1007/978-3-031-29860-8_25" @default.
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