VAMPIRE: vectorized automated ML pre-processing and post-processing framework for edge applications
نویسندگان
چکیده
Abstract Machine learning techniques aim to mimic the human ability automatically learn how perform tasks through training examples. They have proven capable of such as prediction, and adaptation based on experience can be used in virtually any scientific application, ranging from biomedical, robotic, business decision applications, others. However, lack domain knowledge for a particular application make feature extraction ineffective or even unattainable. Furthermore, presence pre-processed datasets, iterative process optimizing Learning parameters, which do not translate one another, maybe difficult inexperienced practitioners. To address these issues, we present this paper Vectorized Automated ML Pre-processIng post-pRocEssing framework, approximately named ( VAMPIRE ), implements algorithms converting large time-series recordings into datasets. Also, it introduces new concept, Activation Engine , is attached output Multi Layer Perceptron extracts optimal threshold apply binary classification. Moreover, tree-based algorithm achieve multi-class classification using . internet things gives rise applications remote sensing communications, so consequently applying improve operation accuracy, latency, reliability beneficial systems. Therefore, all classifications were performed edge order reach high accuracy with limited resources. forecasts applied three unrelated biomedical two other urban activity detection Features extracted when required, testing Raspberry Pi remotely, where inference speed achieved every experiment. Additionally, board remained competitive terms power consumption compared laptop was optimized Graphical Processing Unit.
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ژورنال
عنوان ژورنال: Computing
سال: 2022
ISSN: ['0010-485X', '1436-5057']
DOI: https://doi.org/10.1007/s00607-022-01096-z