Machine Learning Based Embedded Code Multi-Label Classification

With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role. The functions of the MCUs are becoming more and more po...

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Autores principales:	Yu Zhou, Suxia Cui, Yonghui Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Embedded code classifier multi-label tag-correlated text classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/409aa0363f524f39af77883d77ea40eb
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id	oai:doaj.org-article:409aa0363f524f39af77883d77ea40eb
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spelling	oai:doaj.org-article:409aa0363f524f39af77883d77ea40eb2021-11-18T00:01:24ZMachine Learning Based Embedded Code Multi-Label Classification2169-353610.1109/ACCESS.2021.3123498https://doaj.org/article/409aa0363f524f39af77883d77ea40eb2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9590539/https://doaj.org/toc/2169-3536With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role. The functions of the MCUs are becoming more and more powerful and complicated, which brings huge challenges to embedded programmers. Embedded code, which is highly related to the hardware resources, differs from other popular programming code. The hardware configuration may be a big challenge to the programmers, who may only be good at software development and algorithm design. Online code searching can be time consuming and cannot guarantee an optimal approach. To solve this problem, in this paper, an embedded code classifier, which is designed to help embedded programmers to search for the most efficient code with precise tags, is demonstrated. A high quality embedded code dataset is built. A tag correlated multi-label machine learning model is developed for the embedded code dataset. The experimental results show that the proposed code dataset structure is proved to be more efficient on embedded code classification. The proposed embedded classifier algorithm shows a promising result on embedded code dataset. And it outperforms the traditional machine learning text classification models.Yu ZhouSuxia CuiYonghui WangIEEEarticleEmbedded code classifiermulti-labeltag-correlatedtext classificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150187-150200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Embedded code classifier multi-label tag-correlated text classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Embedded code classifier multi-label tag-correlated text classification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yu Zhou Suxia Cui Yonghui Wang Machine Learning Based Embedded Code Multi-Label Classification
description	With the development of Internet of Things (IoT) technology, embedded based electronic devices have penetrated every corner of our daily lives. As the brain of IoT devices, embedded based micro controller unit (MCU) plays an irreplaceable role. The functions of the MCUs are becoming more and more powerful and complicated, which brings huge challenges to embedded programmers. Embedded code, which is highly related to the hardware resources, differs from other popular programming code. The hardware configuration may be a big challenge to the programmers, who may only be good at software development and algorithm design. Online code searching can be time consuming and cannot guarantee an optimal approach. To solve this problem, in this paper, an embedded code classifier, which is designed to help embedded programmers to search for the most efficient code with precise tags, is demonstrated. A high quality embedded code dataset is built. A tag correlated multi-label machine learning model is developed for the embedded code dataset. The experimental results show that the proposed code dataset structure is proved to be more efficient on embedded code classification. The proposed embedded classifier algorithm shows a promising result on embedded code dataset. And it outperforms the traditional machine learning text classification models.
format	article
author	Yu Zhou Suxia Cui Yonghui Wang
author_facet	Yu Zhou Suxia Cui Yonghui Wang
author_sort	Yu Zhou
title	Machine Learning Based Embedded Code Multi-Label Classification
title_short	Machine Learning Based Embedded Code Multi-Label Classification
title_full	Machine Learning Based Embedded Code Multi-Label Classification
title_fullStr	Machine Learning Based Embedded Code Multi-Label Classification
title_full_unstemmed	Machine Learning Based Embedded Code Multi-Label Classification
title_sort	machine learning based embedded code multi-label classification
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/409aa0363f524f39af77883d77ea40eb
work_keys_str_mv	AT yuzhou machinelearningbasedembeddedcodemultilabelclassification AT suxiacui machinelearningbasedembeddedcodemultilabelclassification AT yonghuiwang machinelearningbasedembeddedcodemultilabelclassification
_version_	1718425220067360768

Machine Learning Based Embedded Code Multi-Label Classification

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