Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105352
L. Liu, Lili Jiang
Many mothers expressed varying degrees of anxiety about their children's online lessons at home. To investigate the related phenomena, data samples were obtained through questionnaires on mothers. The dependent variable of each sample was the anxiety degree towards online classes, and the independent variable was the various socioeconomic attributes of mothers. The results indicated that there was a strong correlation between the mother's anxiety about online classes and socioeconomic attributes such as age, educational background, income, and the elderly person at home accompanying children in online classes. The results of linear regression modeling indicate that it is difficult to fit a simple linear relationship between dependent and independent variables. The integrated learning model based on Bagging indicates that the dependent and independent variables can be fitted into a more complex numerical relationship. The experimental results show that the classification accuracy is 91.6%. The portrait characteristics of mothers with high anxiety about online classes include unaccompanied children at home during online classes, family income, and the mother's educational background. When there was only one child in the family, the age difference between mother and child was significantly larger than the average difference of all subjects.
{"title":"Multiple Linear Regression and Bagging-based Analysis and Modeling of Influence of Mother's Socio-economic Attributes on Anxiety of Online Education","authors":"L. Liu, Lili Jiang","doi":"10.1109/ECEI57668.2023.10105352","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105352","url":null,"abstract":"Many mothers expressed varying degrees of anxiety about their children's online lessons at home. To investigate the related phenomena, data samples were obtained through questionnaires on mothers. The dependent variable of each sample was the anxiety degree towards online classes, and the independent variable was the various socioeconomic attributes of mothers. The results indicated that there was a strong correlation between the mother's anxiety about online classes and socioeconomic attributes such as age, educational background, income, and the elderly person at home accompanying children in online classes. The results of linear regression modeling indicate that it is difficult to fit a simple linear relationship between dependent and independent variables. The integrated learning model based on Bagging indicates that the dependent and independent variables can be fitted into a more complex numerical relationship. The experimental results show that the classification accuracy is 91.6%. The portrait characteristics of mothers with high anxiety about online classes include unaccompanied children at home during online classes, family income, and the mother's educational background. When there was only one child in the family, the age difference between mother and child was significantly larger than the average difference of all subjects.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133456039","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105345
Henan Zhang, Hongyan Li
In the era of the digital economy, the deep integration of new technologies and cross-border e-commerce is forcing the redefinition of talent. Especially, data literacy has become an important indicator for measuring the capacity of cross-border e-commerce talents. Therefore, the problem in the traditional teaching mode, “stressing process and neglecting analysis,” needs to be solved urgently. To this end, we integrate big data technology into the practical teaching process of cross-border e-commerce by applying Python tools that are suitable for teachers and students with liberal arts backgrounds. Using web crawler technology in teaching design to improve the availability of business data, data analysis technology is used to enhance the scientific nature of management decision-making, and data visualization technology is used to ensure the timeliness of business decision-making. The teaching demonstration results show that the teaching design scheme has strong operability. The application of big data technology is an effective means to help the practical teaching of cross-border e-commerce and realize the organic integration of students' business thinking and data literacy. It also positively promotes the reform of e-commerce teaching in applied universities, interdisciplinary learning, and the integration of arts and sciences, and meets the needs of industry development talents.
{"title":"Research on Practical Teaching of Cross-border E-commerce in Applied Universities Based on Big Data Technology","authors":"Henan Zhang, Hongyan Li","doi":"10.1109/ECEI57668.2023.10105345","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105345","url":null,"abstract":"In the era of the digital economy, the deep integration of new technologies and cross-border e-commerce is forcing the redefinition of talent. Especially, data literacy has become an important indicator for measuring the capacity of cross-border e-commerce talents. Therefore, the problem in the traditional teaching mode, “stressing process and neglecting analysis,” needs to be solved urgently. To this end, we integrate big data technology into the practical teaching process of cross-border e-commerce by applying Python tools that are suitable for teachers and students with liberal arts backgrounds. Using web crawler technology in teaching design to improve the availability of business data, data analysis technology is used to enhance the scientific nature of management decision-making, and data visualization technology is used to ensure the timeliness of business decision-making. The teaching demonstration results show that the teaching design scheme has strong operability. The application of big data technology is an effective means to help the practical teaching of cross-border e-commerce and realize the organic integration of students' business thinking and data literacy. It also positively promotes the reform of e-commerce teaching in applied universities, interdisciplinary learning, and the integration of arts and sciences, and meets the needs of industry development talents.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133302758","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105419
Shuyu Jin, Xiaoyun Zhang, Xin Li, Min Cheng, Xiaodong Cui, Jianming Liu
Medical education has traditionally focused on clinical knowledge and skills. However, in recent years, medical humanities education has gained recognition as an essential component of medical training to improve medical students' understanding of medicine's social, cultural, and ethical aspects. With the advent of big data and artificial intelligence (AI), new opportunities have emerged to enhance the effectiveness of medical education. Thus, we propose a novel teaching model for medical humanities education that leverages AI and digital human technology to provide an interactive and engaging learning experience for medical students. In the context of educational practice, the primary purpose of this study is to build a digital simulator and virtual simulation experiment system based on big data and to explore the possibility of its application in the actual teaching process and the practical application results. In practice, a series of clinical teaching cases based on organ systems have been designed and applied to actual medical teaching through this technology. These teaching formats extend the depth and scope of medical teaching, enhance learning interest, and effectively achieve teaching objectives.
{"title":"Development and Application of Teaching Model for Medical Humanities Education using Artificial Intelligence and Digital Humans Technologies","authors":"Shuyu Jin, Xiaoyun Zhang, Xin Li, Min Cheng, Xiaodong Cui, Jianming Liu","doi":"10.1109/ECEI57668.2023.10105419","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105419","url":null,"abstract":"Medical education has traditionally focused on clinical knowledge and skills. However, in recent years, medical humanities education has gained recognition as an essential component of medical training to improve medical students' understanding of medicine's social, cultural, and ethical aspects. With the advent of big data and artificial intelligence (AI), new opportunities have emerged to enhance the effectiveness of medical education. Thus, we propose a novel teaching model for medical humanities education that leverages AI and digital human technology to provide an interactive and engaging learning experience for medical students. In the context of educational practice, the primary purpose of this study is to build a digital simulator and virtual simulation experiment system based on big data and to explore the possibility of its application in the actual teaching process and the practical application results. In practice, a series of clinical teaching cases based on organ systems have been designed and applied to actual medical teaching through this technology. These teaching formats extend the depth and scope of medical teaching, enhance learning interest, and effectively achieve teaching objectives.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121019452","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105399
I. Lin
Many countries are using big data to understand users' behavior to improve policies or promote new policies to provide better services to public users. Using a medium-sized city in Taiwan as a case study, the application of big data is reviewed by using Python to interface with Google Maps API for traffic information. Through the GIS interface, the government can understand the traffic service (or policies) for the public.
{"title":"Combining Big Data and GIS Interface to Achieve Effectiveness of E-government","authors":"I. Lin","doi":"10.1109/ECEI57668.2023.10105399","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105399","url":null,"abstract":"Many countries are using big data to understand users' behavior to improve policies or promote new policies to provide better services to public users. Using a medium-sized city in Taiwan as a case study, the application of big data is reviewed by using Python to interface with Google Maps API for traffic information. Through the GIS interface, the government can understand the traffic service (or policies) for the public.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114655418","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105252
Xinxin Wang
In order to solve the problem of conventional combination and complex and inefficient processing of large-scale digital art images, the self-organization technology based on an image spatial similarity algorithm is provided, and the visual feature representation method of color, image, spatial layout features, SIFT and other similarity algorithms is provided for art images. The spatial clustering method of features is further verified by calculating and modeling the spatial layout features of art images. Based on the multi-layer Nearest Neighbor Propagation clustering method, the experimental picture database is hierarchically clustered to form a hierarchical view mode of pictures. Experiment results show that this method has an excellent performance in the processing and application of art pictures.
{"title":"Research on Self-Organizing Intelligent Classification Management Model of Artistic Aesthetic Images Based on MLAP Algorithm","authors":"Xinxin Wang","doi":"10.1109/ECEI57668.2023.10105252","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105252","url":null,"abstract":"In order to solve the problem of conventional combination and complex and inefficient processing of large-scale digital art images, the self-organization technology based on an image spatial similarity algorithm is provided, and the visual feature representation method of color, image, spatial layout features, SIFT and other similarity algorithms is provided for art images. The spatial clustering method of features is further verified by calculating and modeling the spatial layout features of art images. Based on the multi-layer Nearest Neighbor Propagation clustering method, the experimental picture database is hierarchically clustered to form a hierarchical view mode of pictures. Experiment results show that this method has an excellent performance in the processing and application of art pictures.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123890989","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105365
Caiming Liu, Y. Zhang, Chunming Xie
The artificial intelligence industry has higher requirements for employees to master computer use and related knowledge. The comprehensive impact of compound knowledge on the demand of the artificial intelligence industry can be analyzed through data mining. In order to mine the compound knowledge of computers and artificial intelligence and meet the needs of the AI industry for human resources, a correlation analysis between the compound knowledge of computers and artificial intelligence and the human resources needs of the AI industry is conducted in this study. The data set of the artificial intelligence industry's demand and the compound knowledge data set are constructed for demand analysis. The data for the artificial intelligence industry's demand is constructed using the association rules. Through mining the data, the relationship between compound knowledge and the is analyzed. By mining the data with association rules, the compound knowledge that meets the requirements for the artificial intelligence industry is found. The experimental results verify the feasibility of the proposed method.
{"title":"Computer and AI Compound Knowledge Points Mining in Line with Human Resources Demand of AI Industry","authors":"Caiming Liu, Y. Zhang, Chunming Xie","doi":"10.1109/ECEI57668.2023.10105365","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105365","url":null,"abstract":"The artificial intelligence industry has higher requirements for employees to master computer use and related knowledge. The comprehensive impact of compound knowledge on the demand of the artificial intelligence industry can be analyzed through data mining. In order to mine the compound knowledge of computers and artificial intelligence and meet the needs of the AI industry for human resources, a correlation analysis between the compound knowledge of computers and artificial intelligence and the human resources needs of the AI industry is conducted in this study. The data set of the artificial intelligence industry's demand and the compound knowledge data set are constructed for demand analysis. The data for the artificial intelligence industry's demand is constructed using the association rules. Through mining the data, the relationship between compound knowledge and the is analyzed. By mining the data with association rules, the compound knowledge that meets the requirements for the artificial intelligence industry is found. The experimental results verify the feasibility of the proposed method.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130560329","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105320
Xueyan Wu, Lili Jiang
English education is of great significance to children's lifelong development. Children's learning habits have a great impact on improving children's English level. In this study, many words were randomly selected from the primary school English teaching content to form the English vocabulary test questionnaire A, and several students were invited to complete the survey. The scores were quantified as three values variable as the measurement of children's English vocabulary level and as the dependent variable of the study. Then, based on the principles of educational psychology, 20 combinations of study habits were selected in questionnaire B, and students and parents were invited to fill in as the independent variables of the study. Finally, an improved Adaboost algorithm was proposed. Based on the training data set, a classification model of children's English vocabulary level and children's study habits was constructed. The F1 score of the model after the to-fold cross-test was 85.5%. The model pointed out that the characteristics of children with higher English levels include speaking English loudly, often contacting native speakers of English, being willing to communicate with others, often raising questions related to English learning, and often learning English anytime and anywhere.
{"title":"Improved Adaboost Algorithm Method-Based Research on Influence of Pupils' Learning Habits on English Vocabulary Level","authors":"Xueyan Wu, Lili Jiang","doi":"10.1109/ECEI57668.2023.10105320","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105320","url":null,"abstract":"English education is of great significance to children's lifelong development. Children's learning habits have a great impact on improving children's English level. In this study, many words were randomly selected from the primary school English teaching content to form the English vocabulary test questionnaire A, and several students were invited to complete the survey. The scores were quantified as three values variable as the measurement of children's English vocabulary level and as the dependent variable of the study. Then, based on the principles of educational psychology, 20 combinations of study habits were selected in questionnaire B, and students and parents were invited to fill in as the independent variables of the study. Finally, an improved Adaboost algorithm was proposed. Based on the training data set, a classification model of children's English vocabulary level and children's study habits was constructed. The F1 score of the model after the to-fold cross-test was 85.5%. The model pointed out that the characteristics of children with higher English levels include speaking English loudly, often contacting native speakers of English, being willing to communicate with others, often raising questions related to English learning, and often learning English anytime and anywhere.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128992744","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105255
Meize Chen, Guangze Cao, Jianing Yang
With the advancement of urbanization, various underground pipelines such as natural gas pipelines, communication pipelines, water lines, and other lines gradually become the lifeline to maintain the normal operation of the city. Thus, the daily inspection and maintenance of pipelines are needed to prevent problems such as water pipeline leakage. The urban underground integrated pipeline corridor is buried deep underground so manual inspection faces the risk of gas leakage and corridor fire. The corridor length of dozens or even hundreds of kilometers, resulting in high costs of manual inspection. Thus, pipeline repair scenarios are required as pipelines are buried deep underground, and the excavation causes environmental pollution and traffic congestion. Underground pipelines have a high cost of maintenance and inspection. Integrated pipeline corridors in the urban underground can be used to solve these problems. The integrated pipeline corridor is managed in a centralized underground space. In the middle of the corridor for manual and robotic inspection channels, to solve these problems, we designed a set of an unmanned urban underground integrated pipeline corridor inspection system with an overall system design.
{"title":"Remote System Design of Urban Underground Comprehensive Pipe Gallery Inspection","authors":"Meize Chen, Guangze Cao, Jianing Yang","doi":"10.1109/ECEI57668.2023.10105255","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105255","url":null,"abstract":"With the advancement of urbanization, various underground pipelines such as natural gas pipelines, communication pipelines, water lines, and other lines gradually become the lifeline to maintain the normal operation of the city. Thus, the daily inspection and maintenance of pipelines are needed to prevent problems such as water pipeline leakage. The urban underground integrated pipeline corridor is buried deep underground so manual inspection faces the risk of gas leakage and corridor fire. The corridor length of dozens or even hundreds of kilometers, resulting in high costs of manual inspection. Thus, pipeline repair scenarios are required as pipelines are buried deep underground, and the excavation causes environmental pollution and traffic congestion. Underground pipelines have a high cost of maintenance and inspection. Integrated pipeline corridors in the urban underground can be used to solve these problems. The integrated pipeline corridor is managed in a centralized underground space. In the middle of the corridor for manual and robotic inspection channels, to solve these problems, we designed a set of an unmanned urban underground integrated pipeline corridor inspection system with an overall system design.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126338786","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105339
Ping Yang
English translation content estimation is a key work in natural language processing. Unlike the conventional automatic evaluation method of English translation content, the translation quality estimation method does not use manual reference translation to evaluate the ability of English translation. However, according to the content quality estimation of the current sentences in English translation, the feature information extraction method lacks the generalization analysis of linguistic research, which also affects the use of subsequent vector regression methods. Therefore, the feature information of the vocabulary vector is studied to obtain the context vocabulary prediction model and matrix analysis model of deep learning. They are combined with the recursive neural network language modeling to enhance the reliability of the independent estimation and manual evaluation of translation quality. The experimental results using the data set of the sub-task of translation content quality estimation in WMT 15 and WMT 16 show that the method of obtaining the feature of sentence vector through context lexical analysis is consistently more effective than the original QuEst method and the feature acquisition method of sentence vector graph in continuous space language mode. It is also clarified that the newly established feature extraction method does not require linguistic means but significantly enhances the effectiveness of translation quality evaluation.
{"title":"Intelligent Evaluation Model of English Translation Content Quality Based on Improved Neural Network Algorithm","authors":"Ping Yang","doi":"10.1109/ECEI57668.2023.10105339","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105339","url":null,"abstract":"English translation content estimation is a key work in natural language processing. Unlike the conventional automatic evaluation method of English translation content, the translation quality estimation method does not use manual reference translation to evaluate the ability of English translation. However, according to the content quality estimation of the current sentences in English translation, the feature information extraction method lacks the generalization analysis of linguistic research, which also affects the use of subsequent vector regression methods. Therefore, the feature information of the vocabulary vector is studied to obtain the context vocabulary prediction model and matrix analysis model of deep learning. They are combined with the recursive neural network language modeling to enhance the reliability of the independent estimation and manual evaluation of translation quality. The experimental results using the data set of the sub-task of translation content quality estimation in WMT 15 and WMT 16 show that the method of obtaining the feature of sentence vector through context lexical analysis is consistently more effective than the original QuEst method and the feature acquisition method of sentence vector graph in continuous space language mode. It is also clarified that the newly established feature extraction method does not require linguistic means but significantly enhances the effectiveness of translation quality evaluation.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123016175","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}
Pub Date : 2023-02-03DOI: 10.1109/ECEI57668.2023.10105315
Chen-Yu Chiu, Min Wu, JianMin Huang, Jian-Xin Chen, Hao-Jyun Wang
Users are more at risk from ransomware as time goes on. Invading users' computers with ransomware aims to encrypt their data and demand payment. Although anti-virus software may identify ransomware assaults on computers, it cannot prevent them until they are identified. Since many users may have already been hit by ransomware during this viral window period, safeguarding users during this time becomes a priority. We present a way to identify suspected ransomware in real-time. It would integrate into the Windows mini-filter driver to fight against ransomware assaults. This approach makes it challenging for ransomware to evade our detection. Our technology allows consumers to terminate the currently running application or put it on the whitelist once it has been flagged as potentially malicious software. Our solution enables users to edit the software and recovers the altered files when they choose to end the application, lessening their loss.
{"title":"Machine Learning Detection of Ransomware by Lightweight Mini-filters","authors":"Chen-Yu Chiu, Min Wu, JianMin Huang, Jian-Xin Chen, Hao-Jyun Wang","doi":"10.1109/ECEI57668.2023.10105315","DOIUrl":"https://doi.org/10.1109/ECEI57668.2023.10105315","url":null,"abstract":"Users are more at risk from ransomware as time goes on. Invading users' computers with ransomware aims to encrypt their data and demand payment. Although anti-virus software may identify ransomware assaults on computers, it cannot prevent them until they are identified. Since many users may have already been hit by ransomware during this viral window period, safeguarding users during this time becomes a priority. We present a way to identify suspected ransomware in real-time. It would integrate into the Windows mini-filter driver to fight against ransomware assaults. This approach makes it challenging for ransomware to evade our detection. Our technology allows consumers to terminate the currently running application or put it on the whitelist once it has been flagged as potentially malicious software. Our solution enables users to edit the software and recovers the altered files when they choose to end the application, lessening their loss.","PeriodicalId":176611,"journal":{"name":"2023 IEEE 6th Eurasian Conference on Educational Innovation (ECEI)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200891","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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