Mengzi Zhang, Xiao Chen Yue, Jin Xiaocheng Zhou, Shaowei Zhang
{"title":"基于深度学习的软件工程专业人才培养质量","authors":"Mengzi Zhang, Xiao Chen Yue, Jin Xiaocheng Zhou, Shaowei Zhang","doi":"10.52783/jes.3738","DOIUrl":null,"url":null,"abstract":"Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning\",\"authors\":\"Mengzi Zhang, Xiao Chen Yue, Jin Xiaocheng Zhou, Shaowei Zhang\",\"doi\":\"10.52783/jes.3738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.\",\"PeriodicalId\":44451,\"journal\":{\"name\":\"Journal of Electrical Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.52783/jes.3738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52783/jes.3738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning
Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.