自动化与仪器仪表2024,Issue(2) :195-199.DOI:10.14016/j.cnki.1001-9227.2024.02.195

面向物联网和大数据的机器学习研究

Research on machine learning for IoT and big data

谭婕娟
自动化与仪器仪表2024,Issue(2) :195-199.DOI:10.14016/j.cnki.1001-9227.2024.02.195

面向物联网和大数据的机器学习研究

Research on machine learning for IoT and big data

谭婕娟1
扫码查看

作者信息

  • 1. 西安航空职业技术学院,西安 710089
  • 折叠

摘要

随着物联网和大数据技术的发展,卷积神经网络在图像分类任务中取得了显著的成果.研究为了解决传统岗课赛证课程存在数据传播有限、特征提取困难等问题,构建了一种面向于物联网和大数据下的岗课赛证教育课程分类的卷神经网络分类识别模型.首先利用物联网与大数据对教育课程分类进行研究,然后将课程分类与卷积神经网络相结合,构建了适用于岗课赛证课程分类的模型,最后通过将训练好的模型应用到实际测试数据上进行训练和测试.结果表明研究构建的岗课赛证课程分类识别模型在课程分类准确性、PR曲线和F1值分别为90.61%、0.89和0.81,实验结果均优于对比方法.这验证了研究提出的岗课赛证课程分类识别模型具有较高的分类识别能力,同时这项研究对于岗课赛证的发展起到了推进作用.

Abstract

With the development of IoT and big data technology,convolutional neural networks have achieved remarkable results in image classification tasks.In order to solve the problems of limited data propagation and difficult feature extraction of traditional post-course race certificate courses,the study constructs a convolutional neural network in post-course race certificate education course classification recognition model based on IoT and big data technology.Firstly,the educational course classification is studied by using IoT and big data,then the course classification is combined with convolutional neural network to build a model applicable to the course classification of the on-the-job course,and finally the trained model is trained and tested by applying the trained model to the actual test data.The results show that the accuracy rate,PR curve and F1 value of the research constructed course classification recognition model are 90.61%,0.89 and 0.81 respectively,and the experimental results are better than the comparison methods.This verifies that the classification recognition model proposed by the study has high classification recognition ability,and this study plays a role in promoting the development of the post course certification.

关键词

物联网/大数据/卷神经网络/岗课赛证/课程分类

Key words

internet of things/big data/volume neural networks/certificate of post-course competition/course categories

引用本文复制引用

基金项目

陕西省教育科学规划课题(十四五)(2022)(SGH22Y1638)

陕西省教育厅科研项目(2023)(23JK0503)

出版年

2024
自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
参考文献量13
段落导航相关论文