The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.
{"title":"Application of Big Data in Entrepreneurship and Innovation Education for Higher Vocational Teaching","authors":"Long Chen, Jiang He","doi":"10.4018/ijitwe.333898","DOIUrl":"https://doi.org/10.4018/ijitwe.333898","url":null,"abstract":"The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.
{"title":"Matching Prediction of Teacher Demand and Training Based on SARIMA Model Based on Neural Network","authors":"Jianliu Zhu","doi":"10.4018/ijitwe.333637","DOIUrl":"https://doi.org/10.4018/ijitwe.333637","url":null,"abstract":"This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"12 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A deep semantic-level cross-domain recommendation model based on DSV-CDRM is proposed to address the problems of existing methods such as single modeling approach. First, review information is converted into word vectors using a TinyBERT pre-trained language model, and then two global deep semantic viewpoint matrices are used in conjunction with a gating mechanism to guide queries. An additional convolutional layer is added on top of the improved text convolution to construct auxiliary documents using similar but non-overlapping user comments. Finally, correlations between deep semantic viewpoints between different domains are learned by constructing a correlation matrix and performing semantic matching. Experiments on the Amazon public dataset demonstrate that the proposed method outperforms existing models in both MAE and MSE, and it can effectively improve the performance of cross-domain recommendation system.
针对现有方法（如单一建模方法）存在的问题，提出了一种基于 DSV-CDRM 的深度语义级跨域推荐模型。首先，使用 TinyBERT 预训练语言模型将评论信息转换为单词向量，然后使用两个全局深度语义观点矩阵结合门控机制来引导查询。在改进文本卷积的基础上增加一个卷积层，利用相似但不重叠的用户评论构建辅助文档。最后，通过构建相关矩阵和执行语义匹配，学习不同领域之间深层语义观点的相关性。在亚马逊公共数据集上的实验表明，所提出的方法在 MAE 和 MSE 方面都优于现有模型，能有效提高跨领域推荐系统的性能。
{"title":"Deep Semantic-Level Cross-Domain Recommendation Model Based on DSV-CDRM","authors":"Xuewei Lai, Qingqing Jie","doi":"10.4018/ijitwe.333639","DOIUrl":"https://doi.org/10.4018/ijitwe.333639","url":null,"abstract":"A deep semantic-level cross-domain recommendation model based on DSV-CDRM is proposed to address the problems of existing methods such as single modeling approach. First, review information is converted into word vectors using a TinyBERT pre-trained language model, and then two global deep semantic viewpoint matrices are used in conjunction with a gating mechanism to guide queries. An additional convolutional layer is added on top of the improved text convolution to construct auxiliary documents using similar but non-overlapping user comments. Finally, correlations between deep semantic viewpoints between different domains are learned by constructing a correlation matrix and performing semantic matching. Experiments on the Amazon public dataset demonstrate that the proposed method outperforms existing models in both MAE and MSE, and it can effectively improve the performance of cross-domain recommendation system.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"66 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139274284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E-learning offers an experience that is not constrained by time or geography. Owing to the advancements in technology and accessible computing, users have several ways to interact with e-learning programs. Therefore, usability approaches are crucial for the success of an e-learning application or a website. This study investigates various user-interface usability evaluation methods (UEM) and distribution of e-learning web-based applications, such as Moodle, Blackboard, Learning Management System (LMS), Zoom, Google Classroom, Facebook, and other online programs that exist for online education. To evaluate the usability features of online educational apps and websites, including their effectiveness and usability for students, a survey was conducted to collect responses on online education.
{"title":"Evaluating the User Interface and Usability Approaches for E-Learning Systems","authors":"J. Alqurni","doi":"10.4018/ijitwe.333638","DOIUrl":"https://doi.org/10.4018/ijitwe.333638","url":null,"abstract":"E-learning offers an experience that is not constrained by time or geography. Owing to the advancements in technology and accessible computing, users have several ways to interact with e-learning programs. Therefore, usability approaches are crucial for the success of an e-learning application or a website. This study investigates various user-interface usability evaluation methods (UEM) and distribution of e-learning web-based applications, such as Moodle, Blackboard, Learning Management System (LMS), Zoom, Google Classroom, Facebook, and other online programs that exist for online education. To evaluate the usability features of online educational apps and websites, including their effectiveness and usability for students, a survey was conducted to collect responses on online education.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"BME-25 6","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the popularization of the internet, cybercrime continues to increase, and traditional blacklist methods have difficulty in coping with new threats. To address this challenge, the authors propose a web domain name security access recognition algorithm based on bidirectional recurrent neural networks, aiming to more effectively combat domain name generation technology. This algorithm extracts richer semantic features at each layer through bidirectional recurrent neural networks to more accurately describe domain name features, thus effectively handling SGD problems in abnormal network traffic detection. The results show that compared with the other three algorithms, the model trained by HCA-BAGD has better performance and higher accuracy, successfully solving the problem of network security detection. This study emphasizes the importance of cybersecurity and emphasizes continuous innovation and the adoption of new technological tools to ensure the safe operation of the internet ecosystem, bringing new perspectives and solutions to research and applications in the field of cybersecurity.
{"title":"The Construction of Network Domain Name Security Access Identification System Based on Artificial Intelligence","authors":"Lin Li","doi":"10.4018/ijitwe.333636","DOIUrl":"https://doi.org/10.4018/ijitwe.333636","url":null,"abstract":"With the popularization of the internet, cybercrime continues to increase, and traditional blacklist methods have difficulty in coping with new threats. To address this challenge, the authors propose a web domain name security access recognition algorithm based on bidirectional recurrent neural networks, aiming to more effectively combat domain name generation technology. This algorithm extracts richer semantic features at each layer through bidirectional recurrent neural networks to more accurately describe domain name features, thus effectively handling SGD problems in abnormal network traffic detection. The results show that compared with the other three algorithms, the model trained by HCA-BAGD has better performance and higher accuracy, successfully solving the problem of network security detection. This study emphasizes the importance of cybersecurity and emphasizes continuous innovation and the adoption of new technological tools to ensure the safe operation of the internet ecosystem, bringing new perspectives and solutions to research and applications in the field of cybersecurity.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"23 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper mainly studies the content of the recommendation algorithm of learning resource courses in online learning platforms such as MOOC and mainly introduces the automatic encoder neural network that integrates course relevance to realize the personalized course recommendation model. The authors first introduce how to embed a course relevance decoder in an autoencoder neural network. Secondly, the proposed confidence matrix method is introduced to distinguish the recommendation effect of the learned to the unlearned courses, and the training process of the model is introduced. Then, the design content of the experiment is introduced, including the model structure, comparative experiments, parameter settings, and evaluation indicators. Finally, the experimental results are analyzed in detail from the horizontal and vertical aspects. It is hoped that this research can provide a reference for personalized recommendation of learning resources based on deep learning technology and big data analysis.
{"title":"A Course Recommendation Algorithm for a Personalized Online Learning Platform for Students From the Perspective of Deep Learning","authors":"Zhengmeng Xu, Hai Lin, Meiping Wu","doi":"10.4018/ijitwe.333603","DOIUrl":"https://doi.org/10.4018/ijitwe.333603","url":null,"abstract":"This paper mainly studies the content of the recommendation algorithm of learning resource courses in online learning platforms such as MOOC and mainly introduces the automatic encoder neural network that integrates course relevance to realize the personalized course recommendation model. The authors first introduce how to embed a course relevance decoder in an autoencoder neural network. Secondly, the proposed confidence matrix method is introduced to distinguish the recommendation effect of the learned to the unlearned courses, and the training process of the model is introduced. Then, the design content of the experiment is introduced, including the model structure, comparative experiments, parameter settings, and evaluation indicators. Finally, the experimental results are analyzed in detail from the horizontal and vertical aspects. It is hoped that this research can provide a reference for personalized recommendation of learning resources based on deep learning technology and big data analysis.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"170 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The technology of relay protection in China's power system has gradually changed from the traditional operation mode to the development direction of informatization, intelligence, and automation. As a result, the role of relay protection in the power system has become more and more important. It brings higher requirements to the reliability of relay protection; effective reliability assessment of the relay protection system and the corresponding condition operation, minimize or avoid accidents, and ensure the safety of power grids. Starting from the operating characteristics of relay protection, it is suitable for practical engineering applications. Aiming at the problems of low work efficiency and low inspection quality in manual inspection of relay protection pressure plate switching state, The Faster R-CNN image processing algorithm will be come up with. This method uses grayscale, binarization and filtering techniques to preprocess the platen photos, and uses RPN.
{"title":"Power System Relay Protection Based on Faster R-CNN Algorithm","authors":"Yong Liu, Zhengbiao Jing","doi":"10.4018/ijitwe.333475","DOIUrl":"https://doi.org/10.4018/ijitwe.333475","url":null,"abstract":"The technology of relay protection in China's power system has gradually changed from the traditional operation mode to the development direction of informatization, intelligence, and automation. As a result, the role of relay protection in the power system has become more and more important. It brings higher requirements to the reliability of relay protection; effective reliability assessment of the relay protection system and the corresponding condition operation, minimize or avoid accidents, and ensure the safety of power grids. Starting from the operating characteristics of relay protection, it is suitable for practical engineering applications. Aiming at the problems of low work efficiency and low inspection quality in manual inspection of relay protection pressure plate switching state, The Faster R-CNN image processing algorithm will be come up with. This method uses grayscale, binarization and filtering techniques to preprocess the platen photos, and uses RPN.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article focuses on the application of the internet of things and decision tree algorithms in the collection of sports practice teaching data and evaluates the performance and effectiveness of this method through experimental data analysis. The results show that the sports practice teaching data collection method based on the internet of things decision tree algorithm has shown good performance and effectiveness in the experiment. Therefore, this method can effectively extract useful information, provide accurate feedback and guidance for teachers, and is conducive to improving teaching quality and optimizing teaching methods.
{"title":"Data Collection and Analysis in Physical Education Practical Teaching Based on Internet of Things","authors":"Yang Xu, Min Liu","doi":"10.4018/ijitwe.332857","DOIUrl":"https://doi.org/10.4018/ijitwe.332857","url":null,"abstract":"This article focuses on the application of the internet of things and decision tree algorithms in the collection of sports practice teaching data and evaluates the performance and effectiveness of this method through experimental data analysis. The results show that the sports practice teaching data collection method based on the internet of things decision tree algorithm has shown good performance and effectiveness in the experiment. Therefore, this method can effectively extract useful information, provide accurate feedback and guidance for teachers, and is conducive to improving teaching quality and optimizing teaching methods.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"53 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.
{"title":"Exploration on Portfolio Selection and Risk Prediction in Financial Markets Based on SVM Algorithm","authors":"Xinyu Han, Dianqi Yao","doi":"10.4018/ijitwe.332777","DOIUrl":"https://doi.org/10.4018/ijitwe.332777","url":null,"abstract":"In order to cope with the complex risk environment of the current financial market, achieve portfolio optimization and accurate risk prediction, this paper conducts effective research using SVM algorithm. This article uses stock data as a sample to empirically analyze the risk return and risk prediction performance of investment portfolio strategies based on SVM algorithm. Compared with traditional index fund investment strategies, the risk resistance of investment portfolio strategies is significantly improved, and the risk return is also stable at a high level. In addition, with the support of SVM algorithm, the risk prediction error level in the financial market remains within a relatively low range. From the perspective of practical applications, the financial market investment portfolio selection and risk prediction based on SVM algorithm has strong feasibility.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"17 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135167378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to turn our attention to the real progress of the education world, this paper makes a concrete study on the efficiency optimization of MySQL database under big data. On the basis of discussing the significance of cultivating students' team spirit, this paper analyzes and discusses the strategies of cultivating students' team spirit in extracurricular sports activities. MySQL database, as one of the widely used open source databases, naturally has relatively excellent running efficiency, but it also poses a strong challenge for further improving the running efficiency of MySQL database. In many external services, databases are needed, whether in websites or other large-scale software. All the mobile users who need to use the database to store a large amount of user data have caused many websites to collect users and user behavior analysis, which can make them better optimize the website and products, so the dependence and application of data are increasing, and the optimization of databases can greatly improve the ability of developers to retrieve and analyze data.
{"title":"Sports Work Strategy of College Counselors Based on MySQL Database Big Data Analysis","authors":"Xiao Zhang, Ali Yu, Xin Wang, Xue Zhang","doi":"10.4018/ijitwe.332788","DOIUrl":"https://doi.org/10.4018/ijitwe.332788","url":null,"abstract":"In order to turn our attention to the real progress of the education world, this paper makes a concrete study on the efficiency optimization of MySQL database under big data. On the basis of discussing the significance of cultivating students' team spirit, this paper analyzes and discusses the strategies of cultivating students' team spirit in extracurricular sports activities. MySQL database, as one of the widely used open source databases, naturally has relatively excellent running efficiency, but it also poses a strong challenge for further improving the running efficiency of MySQL database. In many external services, databases are needed, whether in websites or other large-scale software. All the mobile users who need to use the database to store a large amount of user data have caused many websites to collect users and user behavior analysis, which can make them better optimize the website and products, so the dependence and application of data are increasing, and the optimization of databases can greatly improve the ability of developers to retrieve and analyze data.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":"57 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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