Aiming at the problem of moving target extraction, a target adaptive clustering extraction algorithm is proposed. Based on the perspective of digital labor alienation theory, on the basis of fully absorbing the existing research results, combined with the clustering extraction algorithm, this paper puts forward the analysis and Countermeasures of digital labor alienation based on clustering extraction algorithm. From the perspective of alienation, the alienation of digital labor profoundly affects everyone's life, resulting in ideological invasion, digital capital hegemony and digital fetishism. However, under the existing social production relations, we must weigh the advantages and disadvantages brought by digital labor, rationally look at the relationship between human liberation and labor alienation, and better develop digital labor and digital economy from the perspective of improving productivity.
{"title":"Analysis and Countermeasures of digital labor alienation based on clustering extraction algorithm","authors":"Shasha Zhang","doi":"10.1145/3510858.3511354","DOIUrl":"https://doi.org/10.1145/3510858.3511354","url":null,"abstract":"Aiming at the problem of moving target extraction, a target adaptive clustering extraction algorithm is proposed. Based on the perspective of digital labor alienation theory, on the basis of fully absorbing the existing research results, combined with the clustering extraction algorithm, this paper puts forward the analysis and Countermeasures of digital labor alienation based on clustering extraction algorithm. From the perspective of alienation, the alienation of digital labor profoundly affects everyone's life, resulting in ideological invasion, digital capital hegemony and digital fetishism. However, under the existing social production relations, we must weigh the advantages and disadvantages brought by digital labor, rationally look at the relationship between human liberation and labor alienation, and better develop digital labor and digital economy from the perspective of improving productivity.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74448666","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 recent years, with the rapid development of information technology, computer technology and the Internet, various sectors of society have collected a large amount of data. At present, traditional statistical analysis models have limitations. Machine learning system is currently one of the main means to effectively solve problems such as data development and mining. Machine learning is a process of self-improvement using the computer system itself. Therefore, computer applications written by computers can be automated by accumulating practical experience. This article aims to study the application of machine learning algorithms in audit data analysis. Based on the analysis of audit information construction, audit data analysis system design principles, and audit data analysis system non-functional requirements analysis, the audit data analysis system is designed. The association rule algorithm in machine learning is used in audit data mining. Finally, the performance of the system is tested. The test results show that the performance of the system designed in this paper is unified with the pre-demand, which shows that the effectiveness of the system can be satisfied.
{"title":"Application of Machine Learning Algorithms in Audit Data Analysis","authors":"Jianyu Zhou","doi":"10.1145/3510858.3510881","DOIUrl":"https://doi.org/10.1145/3510858.3510881","url":null,"abstract":"In recent years, with the rapid development of information technology, computer technology and the Internet, various sectors of society have collected a large amount of data. At present, traditional statistical analysis models have limitations. Machine learning system is currently one of the main means to effectively solve problems such as data development and mining. Machine learning is a process of self-improvement using the computer system itself. Therefore, computer applications written by computers can be automated by accumulating practical experience. This article aims to study the application of machine learning algorithms in audit data analysis. Based on the analysis of audit information construction, audit data analysis system design principles, and audit data analysis system non-functional requirements analysis, the audit data analysis system is designed. The association rule algorithm in machine learning is used in audit data mining. Finally, the performance of the system is tested. The test results show that the performance of the system designed in this paper is unified with the pre-demand, which shows that the effectiveness of the system can be satisfied.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72950499","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}
Yang Yang, Jianglong Fu, Jianguang. Zhao, Juan Hao, H. Sun, Xiaohui Qin
Event detection system is more and more used in road monitoring. This paper proposes a traffic multi state recognition system based on intelligent vision recognition technology. The gray change interval of frame difference is used to update the image background, and the pixel change control rule is introduced as the core software algorithm. Combined with traffic state monitoring data source, multi state traffic events such as congestion, pedestrian and parking are detected. The actual system test shows that the system can timely and accurately detect related traffic events, and has high detection accuracy. As an important part of intelligent transportation system, traffic incident automatic detection (AID) system plays an important role in avoiding traffic accidents, handling and controlling.
{"title":"Design of AID and Monitoring Function Based on Intelligent Vision","authors":"Yang Yang, Jianglong Fu, Jianguang. Zhao, Juan Hao, H. Sun, Xiaohui Qin","doi":"10.1145/3510858.3511360","DOIUrl":"https://doi.org/10.1145/3510858.3511360","url":null,"abstract":"Event detection system is more and more used in road monitoring. This paper proposes a traffic multi state recognition system based on intelligent vision recognition technology. The gray change interval of frame difference is used to update the image background, and the pixel change control rule is introduced as the core software algorithm. Combined with traffic state monitoring data source, multi state traffic events such as congestion, pedestrian and parking are detected. The actual system test shows that the system can timely and accurately detect related traffic events, and has high detection accuracy. As an important part of intelligent transportation system, traffic incident automatic detection (AID) system plays an important role in avoiding traffic accidents, handling and controlling.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77242735","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 development of information technology, the traditional financial industry has also entered a period of rapid development. The business scope of financial institutions has expanded dramatically with the technological updates, and the service level and user experience have become higher and higher. However, new credit risk issues inevitably emerge within various areas of the financial market, such as the lending business. The lending business, one of the core businesses of the financial industry, generates huge profits for financial institutions, but is very dependent on the level of risk control. In order to minimize the risk, financial institutions want to use the emerging internet technology to analyze massive data, mine effective information and refine risk indices. Therefore, how to use emerging technologies such as big data and data mining to assess loan defaults is gradually becoming a hot issue for financial institutions and an important research direction. In this paper, 150,000 data records of loan customers are obtained from Kaggle credit score dataset, and data pre-processing is performed by statistical methods to clean the unreasonable data in the dataset, such as duplicate, missing and abnormal values. Using logistic regression algorithm, an interpretable credit evaluation model was built on the user's credit records to predict the default likelihood of the user in the coming years. The final quantitative scoring of loan users' default likelihood helps financial institutions control their risks.
{"title":"Financial Evaluation Model and Algorithm Based on Data Mining","authors":"G. Cheng","doi":"10.1145/3510858.3510914","DOIUrl":"https://doi.org/10.1145/3510858.3510914","url":null,"abstract":"With the development of information technology, the traditional financial industry has also entered a period of rapid development. The business scope of financial institutions has expanded dramatically with the technological updates, and the service level and user experience have become higher and higher. However, new credit risk issues inevitably emerge within various areas of the financial market, such as the lending business. The lending business, one of the core businesses of the financial industry, generates huge profits for financial institutions, but is very dependent on the level of risk control. In order to minimize the risk, financial institutions want to use the emerging internet technology to analyze massive data, mine effective information and refine risk indices. Therefore, how to use emerging technologies such as big data and data mining to assess loan defaults is gradually becoming a hot issue for financial institutions and an important research direction. In this paper, 150,000 data records of loan customers are obtained from Kaggle credit score dataset, and data pre-processing is performed by statistical methods to clean the unreasonable data in the dataset, such as duplicate, missing and abnormal values. Using logistic regression algorithm, an interpretable credit evaluation model was built on the user's credit records to predict the default likelihood of the user in the coming years. The final quantitative scoring of loan users' default likelihood helps financial institutions control their risks.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87525989","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}
Particle swarm optimization (PSO) is an intelligent evolutionary method, which is widely used to search the global optimal solution. However, in the early stage of the algorithm, the rapid flight of particle swarm to the current optimal solution may lead to premature convergence, while in the later stage of the algorithm, the convergence of most particles will lead to the decrease of particle swarm velocity. In this paper, the advantages and principles of IPSOA are discussed, and the insulation problem of mixed gas is discussed. By comparing the standard PSOA with the improved PSOA, the results show that the calculation result of the improved PSOA is close to the optimal value of the function itself, which proves that the improved PSOA has better optimization ability.
{"title":"Insulation Optimization System of Mixed Gas based on Intelligent Particle Swarm Optimization","authors":"Shoutao Chen, Shuo Han, Ningbo Kang, Q. Yuan, Jiajun Guo, Fangning Pu","doi":"10.1145/3510858.3510887","DOIUrl":"https://doi.org/10.1145/3510858.3510887","url":null,"abstract":"Particle swarm optimization (PSO) is an intelligent evolutionary method, which is widely used to search the global optimal solution. However, in the early stage of the algorithm, the rapid flight of particle swarm to the current optimal solution may lead to premature convergence, while in the later stage of the algorithm, the convergence of most particles will lead to the decrease of particle swarm velocity. In this paper, the advantages and principles of IPSOA are discussed, and the insulation problem of mixed gas is discussed. By comparing the standard PSOA with the improved PSOA, the results show that the calculation result of the improved PSOA is close to the optimal value of the function itself, which proves that the improved PSOA has better optimization ability.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78750877","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}
Bid evaluation plays an important role in EPC (Engineering Procurement Construction) project bidding. The success of this work will directly affect the quality, cost and concrete implementation of EPC project, and also affect the direct interests of bidding enterprises. For EPC projects with fixed lump sum contract, because of its huge engineering scale and complex building function requirements, the investment estimation should not only meet the needs of the construction unit to realize cost control, but also serve as the basis for bidding and guide the general contractor to bid. In this paper, the fuzzy clustering analysis method in fuzzy mathematics is taken as the theoretical basis, and the evaluation of the specific project objectives to be achieved by the tenderee is added to establish a scientific and reasonable application model of bid evaluation, and the feasibility of the model is verified by an engineering example. The results show that this method has solved the problems of single evaluation index and unreasonable evaluation process in the existing bid evaluation methods by establishing a fuzzy comprehensive evaluation mathematical model.
{"title":"Establishment of bid evaluation model in EPC project bidding process based on fuzzy clustering algorithm","authors":"Guozong Zhang, Qianmai Luo, Xuqiao Fan","doi":"10.1145/3510858.3511402","DOIUrl":"https://doi.org/10.1145/3510858.3511402","url":null,"abstract":"Bid evaluation plays an important role in EPC (Engineering Procurement Construction) project bidding. The success of this work will directly affect the quality, cost and concrete implementation of EPC project, and also affect the direct interests of bidding enterprises. For EPC projects with fixed lump sum contract, because of its huge engineering scale and complex building function requirements, the investment estimation should not only meet the needs of the construction unit to realize cost control, but also serve as the basis for bidding and guide the general contractor to bid. In this paper, the fuzzy clustering analysis method in fuzzy mathematics is taken as the theoretical basis, and the evaluation of the specific project objectives to be achieved by the tenderee is added to establish a scientific and reasonable application model of bid evaluation, and the feasibility of the model is verified by an engineering example. The results show that this method has solved the problems of single evaluation index and unreasonable evaluation process in the existing bid evaluation methods by establishing a fuzzy comprehensive evaluation mathematical model.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77218822","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 development and wide application of machine learning technology, the use of machine learning technology for economic algorithm technology research has become a new type of financial technology field. Today's financial big data has penetrated into all walks of life and has become an important factor of production. The extraction and application of massive amounts of data by humans heralds the arrival of a new wave of productivity growth and consumer surplus. Big data originally refers to a large number of data sets generated through batch processing or web search index analysis. This paper uses machine learning technology to explore and research big data financial algorithms, analyze risk control measures, report on the improvement and perfection of traditional finance, and analyze and study the future development of big data finance. The main research content of this paper is the analysis of big data financial algorithm technology by machine learning algorithms. Machine learning technology is one of the main methods to solve big data mining problems. Machine learning technology is a process of self-improvement using the system itself, so that computer programs can automatically improve performance through accumulated experience. This paper analyzes the relevant theories and characteristics of machine learning algorithms, and integrates them into the research of big data economic algorithm technology. The final result of the research shows that when the data volume is 1G, the training time of SVM is 8 minutes, while the training time of Bayesian is 12 minutes, and the data volume is relatively small. The SVM algorithm still has obvious advantages in training time.
{"title":"Big Data Financial Algorithm Technology Based on Machine Learning Technology","authors":"Yiming Zhao","doi":"10.1145/3510858.3510934","DOIUrl":"https://doi.org/10.1145/3510858.3510934","url":null,"abstract":"With the development and wide application of machine learning technology, the use of machine learning technology for economic algorithm technology research has become a new type of financial technology field. Today's financial big data has penetrated into all walks of life and has become an important factor of production. The extraction and application of massive amounts of data by humans heralds the arrival of a new wave of productivity growth and consumer surplus. Big data originally refers to a large number of data sets generated through batch processing or web search index analysis. This paper uses machine learning technology to explore and research big data financial algorithms, analyze risk control measures, report on the improvement and perfection of traditional finance, and analyze and study the future development of big data finance. The main research content of this paper is the analysis of big data financial algorithm technology by machine learning algorithms. Machine learning technology is one of the main methods to solve big data mining problems. Machine learning technology is a process of self-improvement using the system itself, so that computer programs can automatically improve performance through accumulated experience. This paper analyzes the relevant theories and characteristics of machine learning algorithms, and integrates them into the research of big data economic algorithm technology. The final result of the research shows that when the data volume is 1G, the training time of SVM is 8 minutes, while the training time of Bayesian is 12 minutes, and the data volume is relatively small. The SVM algorithm still has obvious advantages in training time.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77349482","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 combination of information technology and economic fields, the amount of data has been greatly increased, and big data has begun to be valued by modern enterprises. As a new IT technology, it has had a huge impact on enterprise management, financial and management models, and business processes. Big data will surely become the basis of enterprise competition and management, and the use of information will have a decisive impact on the operating efficiency of enterprises. Big data sets put forward new requirements for corporate financial management. This article is the research goal of voice assistance and big data financial management based on high-resolution imaging algorithms. This paper establishes the specific process of the speech recognition model and high-resolution imaging algorithm based on the genetic algorithm of big data, and compares the experimental data of this paper with the data obtained from the reference literature and the Internet. Big data puts forward new requirements for financial management. It integrates high-resolution imaging algorithms and voice assistance into financial management based on big data, and studies the academic value and practical application value of financial management based on big data. Combined with actual data practice, it proves the feasibility and practicability of the research direction of this article. According to the experimental research in this article, the voice assistance and big data financial management based on the high-resolution imaging algorithm proposed in this article, adding voice assistance to the financial management can make the financial management run better, and the customers can obtain better data. The changes to the management staff can get management errors in a more timely manner, so that they can be modified in a more timely manner. In the use of genetic algorithms based on big data to optimize speech acquisition and recognition, experimental data shows that the highest recognition rate of optimized speech assistance is 98% close to 100%.
{"title":"Voice Assistance and Big Data Financial Management Based on High-Resolution Imaging Algorithm","authors":"C. Yao","doi":"10.1145/3510858.3510965","DOIUrl":"https://doi.org/10.1145/3510858.3510965","url":null,"abstract":"With the combination of information technology and economic fields, the amount of data has been greatly increased, and big data has begun to be valued by modern enterprises. As a new IT technology, it has had a huge impact on enterprise management, financial and management models, and business processes. Big data will surely become the basis of enterprise competition and management, and the use of information will have a decisive impact on the operating efficiency of enterprises. Big data sets put forward new requirements for corporate financial management. This article is the research goal of voice assistance and big data financial management based on high-resolution imaging algorithms. This paper establishes the specific process of the speech recognition model and high-resolution imaging algorithm based on the genetic algorithm of big data, and compares the experimental data of this paper with the data obtained from the reference literature and the Internet. Big data puts forward new requirements for financial management. It integrates high-resolution imaging algorithms and voice assistance into financial management based on big data, and studies the academic value and practical application value of financial management based on big data. Combined with actual data practice, it proves the feasibility and practicability of the research direction of this article. According to the experimental research in this article, the voice assistance and big data financial management based on the high-resolution imaging algorithm proposed in this article, adding voice assistance to the financial management can make the financial management run better, and the customers can obtain better data. The changes to the management staff can get management errors in a more timely manner, so that they can be modified in a more timely manner. In the use of genetic algorithms based on big data to optimize speech acquisition and recognition, experimental data shows that the highest recognition rate of optimized speech assistance is 98% close to 100%.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"408 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84874353","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 development of information technology has promoted the reform of human resource management. The application of intelligent attendance technology changes the traditional attendance mode, improves the efficiency of human resource management, and provides better services for organizational strategy. This paper summarizes the attendance management, discusses the mobile phone GPS positioning technology attendance system, analyzes the main problems of employee attendance management, and studies the composition of product cost. The results show that compared with the cloud intelligent attendance machine of a financial company, D company has obvious price advantage, which is 15% lower than its price.
{"title":"Design and Implementation of Using Intelligent Attendance System to Assess Human Resource Management","authors":"Yu Hu","doi":"10.1145/3510858.3510907","DOIUrl":"https://doi.org/10.1145/3510858.3510907","url":null,"abstract":"The development of information technology has promoted the reform of human resource management. The application of intelligent attendance technology changes the traditional attendance mode, improves the efficiency of human resource management, and provides better services for organizational strategy. This paper summarizes the attendance management, discusses the mobile phone GPS positioning technology attendance system, analyzes the main problems of employee attendance management, and studies the composition of product cost. The results show that compared with the cloud intelligent attendance machine of a financial company, D company has obvious price advantage, which is 15% lower than its price.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87457469","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}
Over the years, AIT has begun to present to people's lives, making people's lives more comfortable. With the progress of intelligent scientific method, online education is developing in the field of education. In the face of the pandemic, millions of students around the world have turned to online education platforms to learn. This paper takes English teachers and students as examples to study the innovation and development of online education. This paper compares the influence of new teachers and students through experiential models and traditional teaching models, and focuses on analyzing the benefits of reformed advanced technologies for online English teachers and students. The results showed that 67% of the students in the experimental group were satisfied or very satisfied with the new teacher-student model, while only 46% of the students in the control group were satisfied or very satisfied with the traditional teaching model. Artificial intelligence scientific method (AIT) can promote the reform platform of Online English education.
{"title":"English Education Online Platform Based on Artificial Intelligence","authors":"Xiaoxiao Duan, Ping Duan","doi":"10.1145/3510858.3510895","DOIUrl":"https://doi.org/10.1145/3510858.3510895","url":null,"abstract":"Over the years, AIT has begun to present to people's lives, making people's lives more comfortable. With the progress of intelligent scientific method, online education is developing in the field of education. In the face of the pandemic, millions of students around the world have turned to online education platforms to learn. This paper takes English teachers and students as examples to study the innovation and development of online education. This paper compares the influence of new teachers and students through experiential models and traditional teaching models, and focuses on analyzing the benefits of reformed advanced technologies for online English teachers and students. The results showed that 67% of the students in the experimental group were satisfied or very satisfied with the new teacher-student model, while only 46% of the students in the control group were satisfied or very satisfied with the traditional teaching model. Artificial intelligence scientific method (AIT) can promote the reform platform of Online English education.","PeriodicalId":6757,"journal":{"name":"2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT)","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87741702","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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