The improved algorithm (iSEFR) increases Precision, Recall and Accuracy by an average of 9%, 14% and 11% respectively.
The improved algorithm (iSEFR) increases Precision, Recall and Accuracy by an average of 9%, 14% and 11% respectively.
They enable the key benefits of edge computing, such as reduced latency, improved data security, higher energy efficiency, and lower bandwidth consumption,�...
Salerno, S., 2020. SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices.[Online] Available at: https://eloquentarduino.github.io/2020/07/sefr-a-fast-�...
This paper presents an iterative training framework with a binary classifier to improve the learning capability of a deep learning model for detecting abnormal�...
Ensemble Machine Learning at the Edge Using the Codec Classifier ...
digitalcommons.usu.edu › etd2023
The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on�...
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A framework with novel Opt-SGD and Opt-OVO algorithms to enable binary and multi-class ML classifier training directly on Arduino MCU boards.
Jul 19, 2021 � In this article, we provide ML-MCU, a framework with our novel Optimized-Stochastic Gradient Descent (Opt-SGD) and Optimized One-Versus-One (Opt�...
Apr 15, 2024 � It consists of two AI models, one at the edge and another at the cloud. Both models are an improved and lightweight versions of the temporal�...
May 27, 2024 � ML models may discover IoT device firmware and software security problems by training on known vulnerabilities and coding patterns. This helps�...
