This paper discusses the importance of integrating sports and education in colleges and its role in the overall development of students. The study first constructs a basic framework for sports education in colleges and universities, and then analyzes in depth the positive effects of the integration of sports and education on students’ character, temperament, as well as sports morality and will quality by setting up a regression analysis model and using statistical test methods. The study’s results showed that the integration of sport and education not only significantly enhanced students’ sports morality and will quality, but also significantly positively affected character and temperament building. This finding emphasizes the importance of strengthening the position of the physical education discipline in higher education to promote the holistic development of students. The results showed that the integration of physical education had a positive effect on character and temperament, Sig=0.000<0.01. And the integration of physical education and teaching significantly affected students’ sports morality and volitional qualities, p<0.05. The present study is conducive to enhancing the importance of school physical education in the education system and promoting students’ all-round development.
{"title":"A Research on the Role of P.E. in Cultivating College Students from the Perspective of Integration of Academic Learning and Physical Exercising","authors":"Min Zhang","doi":"10.2478/amns-2024-0338","DOIUrl":"https://doi.org/10.2478/amns-2024-0338","url":null,"abstract":"\u0000 This paper discusses the importance of integrating sports and education in colleges and its role in the overall development of students. The study first constructs a basic framework for sports education in colleges and universities, and then analyzes in depth the positive effects of the integration of sports and education on students’ character, temperament, as well as sports morality and will quality by setting up a regression analysis model and using statistical test methods. The study’s results showed that the integration of sport and education not only significantly enhanced students’ sports morality and will quality, but also significantly positively affected character and temperament building. This finding emphasizes the importance of strengthening the position of the physical education discipline in higher education to promote the holistic development of students. The results showed that the integration of physical education had a positive effect on character and temperament, Sig=0.000<0.01. And the integration of physical education and teaching significantly affected students’ sports morality and volitional qualities, p<0.05. The present study is conducive to enhancing the importance of school physical education in the education system and promoting students’ all-round development.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140521669","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 develops a sentiment analysis model for English academic discourse based on word information to effectively understand and analyze the sentiment tendencies in English literary texts. The structure of the model includes word embedding layer, character-level feature extraction, word-level feature extraction and feature fusion and classification layer. The word embedding layer realizes the mapping between word vectors and word vectors by microblogging pre-trained word vectors. The character-level feature extraction session uses a multi-window convolutional layer to capture N-Gram information. In contrast, the word-level feature extraction obtains deeper semantic information through a Bi-LSTM layer and fuses it with character-level information to enhance robustness. The feature fusion and classification layer further combines these features and determines the fusion weights through a linear layer to achieve sentiment classification. In performance tests, the model achieves 92.5% sentiment classification accuracy on the standard dataset, an improvement of about 6% compared to traditional methods. In particular, the accuracy is improved by 5% when dealing with text with sentiment polarity transition, showing good adaptability. In addition, using 657 positive and 679 negative sentiment words as seed words effectively expands the sentiment lexicon and enhances the comprehensiveness and accuracy of sentiment analysis.
{"title":"Exploring the Subjectivity of English Academic Discourse in the Context of Big Data","authors":"Ying Pan","doi":"10.2478/amns-2024-0489","DOIUrl":"https://doi.org/10.2478/amns-2024-0489","url":null,"abstract":"\u0000 This study develops a sentiment analysis model for English academic discourse based on word information to effectively understand and analyze the sentiment tendencies in English literary texts. The structure of the model includes word embedding layer, character-level feature extraction, word-level feature extraction and feature fusion and classification layer. The word embedding layer realizes the mapping between word vectors and word vectors by microblogging pre-trained word vectors. The character-level feature extraction session uses a multi-window convolutional layer to capture N-Gram information. In contrast, the word-level feature extraction obtains deeper semantic information through a Bi-LSTM layer and fuses it with character-level information to enhance robustness. The feature fusion and classification layer further combines these features and determines the fusion weights through a linear layer to achieve sentiment classification. In performance tests, the model achieves 92.5% sentiment classification accuracy on the standard dataset, an improvement of about 6% compared to traditional methods. In particular, the accuracy is improved by 5% when dealing with text with sentiment polarity transition, showing good adaptability. In addition, using 657 positive and 679 negative sentiment words as seed words effectively expands the sentiment lexicon and enhances the comprehensiveness and accuracy of sentiment analysis.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140525805","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}
Yingchun Yang, Xu Zhao, Tianxi Han, Zhe Li, Fei Pan
Aiming at the challenge of storing massive power grid data, this paper proposes an improved swing gate trend algorithm to effectively compress 5G data. The algorithm first performs least squares smoothing on the original data to reduce noise interference on the SDT algorithm, which enables the data compression process to more accurately determine the data trend. Further, the shortcomings of the original SDT algorithm are improved, including adaptive frequency conversion data processing, dynamic threshold adjustment, and anomaly recording strategy, to enhance the practicality and efficiency of the algorithm. Through simulation analysis and example data validation, the study shows that the data compression ratio can be stabilized at about 23.98 when the data compression time reaches 1.6 minutes, and the actual error is very close to the desired error. The time overhead of the improved SDT algorithm is only 0.225 seconds, indicating that the algorithm is efficient and reliable. Combined with different data compression storage strategies, the algorithm can further reduce the data compression time. This study provides an adequate data compression method for electric code violation identification, which offers a practical solution for processing and storing large-scale grid data.
{"title":"Compression of electrical code violation recognition data using the improved swinging door trending algorithm","authors":"Yingchun Yang, Xu Zhao, Tianxi Han, Zhe Li, Fei Pan","doi":"10.2478/amns-2024-0478","DOIUrl":"https://doi.org/10.2478/amns-2024-0478","url":null,"abstract":"\u0000 Aiming at the challenge of storing massive power grid data, this paper proposes an improved swing gate trend algorithm to effectively compress 5G data. The algorithm first performs least squares smoothing on the original data to reduce noise interference on the SDT algorithm, which enables the data compression process to more accurately determine the data trend. Further, the shortcomings of the original SDT algorithm are improved, including adaptive frequency conversion data processing, dynamic threshold adjustment, and anomaly recording strategy, to enhance the practicality and efficiency of the algorithm. Through simulation analysis and example data validation, the study shows that the data compression ratio can be stabilized at about 23.98 when the data compression time reaches 1.6 minutes, and the actual error is very close to the desired error. The time overhead of the improved SDT algorithm is only 0.225 seconds, indicating that the algorithm is efficient and reliable. Combined with different data compression storage strategies, the algorithm can further reduce the data compression time. This study provides an adequate data compression method for electric code violation identification, which offers a practical solution for processing and storing large-scale grid data.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523721","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 explores the application of information fusion technology in teaching art and design majors in colleges and universities, aiming to improve the teaching effect and students’ practical ability. The study adopts the flipped classroom teaching model and combines the multiple linear regression method to analyze the effectiveness of the teaching model. The model includes three stages: teaching preparation, teaching process and teaching reflection. The practice teaching system includes online and offline integration of on-campus practice, humanistic literacy practice, on-site participation practice and other multivariate systems. The study results showed that the students who adopted this teaching model scored 4.237, 4.388, and 4.186 (out of 5) in learning self-efficacy, learning adaptability, and learning engagement, respectively, indicating that the students were in the middle to upper level in these areas. The results of regression analysis showed that learning self-efficacy and learning adaptability had a significant positive effect on learning engagement. The conclusion indicates that the application of information fusion technology can significantly improve the learning self-efficacy, learning adaptability and learning engagement of art and design majors, thus improving the quality of teaching and students’ practical ability. This provides new perspectives and methods for teaching art design majors in colleges and universities.
{"title":"Information fusion technology helps promote the teaching practice of art design specialty in colleges and universities","authors":"Xuemin Wang","doi":"10.2478/amns-2024-0557","DOIUrl":"https://doi.org/10.2478/amns-2024-0557","url":null,"abstract":"\u0000 This study explores the application of information fusion technology in teaching art and design majors in colleges and universities, aiming to improve the teaching effect and students’ practical ability. The study adopts the flipped classroom teaching model and combines the multiple linear regression method to analyze the effectiveness of the teaching model. The model includes three stages: teaching preparation, teaching process and teaching reflection. The practice teaching system includes online and offline integration of on-campus practice, humanistic literacy practice, on-site participation practice and other multivariate systems. The study results showed that the students who adopted this teaching model scored 4.237, 4.388, and 4.186 (out of 5) in learning self-efficacy, learning adaptability, and learning engagement, respectively, indicating that the students were in the middle to upper level in these areas. The results of regression analysis showed that learning self-efficacy and learning adaptability had a significant positive effect on learning engagement. The conclusion indicates that the application of information fusion technology can significantly improve the learning self-efficacy, learning adaptability and learning engagement of art and design majors, thus improving the quality of teaching and students’ practical ability. This provides new perspectives and methods for teaching art design majors in colleges and universities.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140523948","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 explores a blended learning model for piano music education, merging traditional classroom instruction with an online personalized learning system to boost resource efficiency, student engagement, and learning outcomes. Utilizing the item collaborative filtering (CF) and learning style filtering recommendation algorithms, we tailored teaching materials to individual student needs, significantly improving match accuracy between resources and learners. Results from implementing this optimized hybrid teaching approach showed a 15% increase in course ratings, a 20% rise in student participation, and a marked enhancement in learners’ interest in piano studies. Additionally, learner satisfaction soared by 25% due to the learning style-based algorithm’s ability to personalize resource allocation. This research underscores the effectiveness of combining blended teaching models with personalized learning systems in elevating piano education quality and efficacy.
{"title":"The Establishment and Practice of a Blended Teaching Model for Piano Music Education","authors":"Tianle Zhang","doi":"10.2478/amns-2024-0819","DOIUrl":"https://doi.org/10.2478/amns-2024-0819","url":null,"abstract":"\u0000 This study explores a blended learning model for piano music education, merging traditional classroom instruction with an online personalized learning system to boost resource efficiency, student engagement, and learning outcomes. Utilizing the item collaborative filtering (CF) and learning style filtering recommendation algorithms, we tailored teaching materials to individual student needs, significantly improving match accuracy between resources and learners. Results from implementing this optimized hybrid teaching approach showed a 15% increase in course ratings, a 20% rise in student participation, and a marked enhancement in learners’ interest in piano studies. Additionally, learner satisfaction soared by 25% due to the learning style-based algorithm’s ability to personalize resource allocation. This research underscores the effectiveness of combining blended teaching models with personalized learning systems in elevating piano education quality and efficacy.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527055","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}
Rong Huang, Qi Chen, Liang Lu, Xiaofeng Chi, Dan Zheng, Yi Ding
This article explores how digital technologies such as big data and cloud computing promote college students’ innovation and entrepreneurship, especially the impact of innovation and entrepreneurship training programs on college students’ entrepreneurial intentions. The article adopts big data analysis techniques to screen variables, set research hypotheses, and use partial least squares regression to quantitatively analyze the correlation between university innovativeness and training programs. It was found that the number of university intellectual property rights was significantly associated with the objectives of the training program, with a regression coefficient of 0.069. Further, the article pointed out that most students believed that the regulation of the research segment was the weakest. Therefore, the article suggests improving the training program supervision system, significantly strengthening the supervision of the research session, and also explores the correlation between academic professional factors and faculty guidance.
{"title":"Research on practical teaching of innovation and entrepreneurship training program for college students based on big data analysis","authors":"Rong Huang, Qi Chen, Liang Lu, Xiaofeng Chi, Dan Zheng, Yi Ding","doi":"10.2478/amns-2024-0413","DOIUrl":"https://doi.org/10.2478/amns-2024-0413","url":null,"abstract":"\u0000 This article explores how digital technologies such as big data and cloud computing promote college students’ innovation and entrepreneurship, especially the impact of innovation and entrepreneurship training programs on college students’ entrepreneurial intentions. The article adopts big data analysis techniques to screen variables, set research hypotheses, and use partial least squares regression to quantitatively analyze the correlation between university innovativeness and training programs. It was found that the number of university intellectual property rights was significantly associated with the objectives of the training program, with a regression coefficient of 0.069. Further, the article pointed out that most students believed that the regulation of the research segment was the weakest. Therefore, the article suggests improving the training program supervision system, significantly strengthening the supervision of the research session, and also explores the correlation between academic professional factors and faculty guidance.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140518535","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}
Integrating Artificial Intelligence (AI) into education, particularly civic education, represents a transformative shift. This study explores the innovative fusion of AI with teaching methodologies, aiming to enhance educational outcomes and foster comprehensive student development. We construct a multidimensional civic education framework by employing theoretical and empirical approaches, examining the dynamics between educators, students, content, and pedagogical strategies. We assess student academic performance and behavior by utilizing the Multi-Task Classroom Behavior Recognition Network (MCBRN) and multivariate analysis of variance (ANOVA). Our findings reveal that the AI-enhanced teaching model significantly boosts student engagement and learning achievements in the experimental group, with behavior recognition accuracy reaching 96.9%. Moreover, these students demonstrated superior examination scores and overall competency levels compared to the control group (P<0.05), highlighting the effectiveness of this novel approach in elevating the quality of civic education through personalized and efficient learning experiences.
{"title":"Practical Innovation of Students’ Civic Education Model Based on Artificial Intelligence Technology","authors":"Yao Lu","doi":"10.2478/amns-2024-0827","DOIUrl":"https://doi.org/10.2478/amns-2024-0827","url":null,"abstract":"\u0000 Integrating Artificial Intelligence (AI) into education, particularly civic education, represents a transformative shift. This study explores the innovative fusion of AI with teaching methodologies, aiming to enhance educational outcomes and foster comprehensive student development. We construct a multidimensional civic education framework by employing theoretical and empirical approaches, examining the dynamics between educators, students, content, and pedagogical strategies. We assess student academic performance and behavior by utilizing the Multi-Task Classroom Behavior Recognition Network (MCBRN) and multivariate analysis of variance (ANOVA). Our findings reveal that the AI-enhanced teaching model significantly boosts student engagement and learning achievements in the experimental group, with behavior recognition accuracy reaching 96.9%. Moreover, these students demonstrated superior examination scores and overall competency levels compared to the control group (P<0.05), highlighting the effectiveness of this novel approach in elevating the quality of civic education through personalized and efficient learning experiences.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140524317","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}
Hang Zhang, Jun Pan, Jinyong Lei, Keying Feng, Tianbao Ma
Hydrogen fuel cells are characterized by non-pollution, high efficiency and long power supply time, and they are increasingly used as backup power systems in substations, communication base stations and other fields. In this paper, based on the thermodynamic model of the hydride hydrogen storage system, the relationship between pressure, composition, and temperature in metal hydride hydrogen storage is quantitatively analyzed using a PCT curve. The hydrogen fuel power supply is used as the overall backup power supply of the DC system, and the hydrogen-fuel integrated backup power supply is established to realize the uninterrupted switching between the utility power and the backup power supply. Finally, the working process of the backup power supply and the reaction process of hydrogen are analyzed to test the feasibility of a hydrogen fuel cell backup power supply. The results show that the operating current climbs to the end of 80 A under the 5 kW workload demand of the communication equipment. In addition, the hydrogen absorption reaction rate was 0.29 Mpa, and the hydrogen release reaction rate was 0.21 Mpa at a temperature of 291 K. This study has developed a fuel cell backup power system that can provide uninterruptible backup power and has a wide market capacity and application prospects.
{"title":"Research on hydrogen fuel cell backup power for metal hydride hydrogen storage system","authors":"Hang Zhang, Jun Pan, Jinyong Lei, Keying Feng, Tianbao Ma","doi":"10.2478/amns-2024-0027","DOIUrl":"https://doi.org/10.2478/amns-2024-0027","url":null,"abstract":"\u0000 Hydrogen fuel cells are characterized by non-pollution, high efficiency and long power supply time, and they are increasingly used as backup power systems in substations, communication base stations and other fields. In this paper, based on the thermodynamic model of the hydride hydrogen storage system, the relationship between pressure, composition, and temperature in metal hydride hydrogen storage is quantitatively analyzed using a PCT curve. The hydrogen fuel power supply is used as the overall backup power supply of the DC system, and the hydrogen-fuel integrated backup power supply is established to realize the uninterrupted switching between the utility power and the backup power supply. Finally, the working process of the backup power supply and the reaction process of hydrogen are analyzed to test the feasibility of a hydrogen fuel cell backup power supply. The results show that the operating current climbs to the end of 80 A under the 5 kW workload demand of the communication equipment. In addition, the hydrogen absorption reaction rate was 0.29 Mpa, and the hydrogen release reaction rate was 0.21 Mpa at a temperature of 291 K. This study has developed a fuel cell backup power system that can provide uninterruptible backup power and has a wide market capacity and application prospects.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140515862","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 discusses the multi-scenario application of ChatGPT-based language modeling in English language teaching, and empirical experiments are conducted to support the research findings. The study includes constructing and analyzing English composition scoring and similarity detection models. The BERT-BiLSTM algorithm was utilized and compared to the Word2Vec-BiLSTM model. The BERT-BiLSTM-based English composition scoring model has a high correlation and consistency with the original scores, with an average correlation of 0.72 and a consistency of 82%. Conversely, the Word2Vec-BiLSTM model has a lesser correlation and consistency. We created a model and used different K values for the experiment to detect English composition similarity. The correctness, recall, and F1 measures were higher at a K value 200, with F1 values fluctuating between 89.35% and 95.14%. These support the high accuracy and efficiency of ChatGPT-based language modeling in English language teaching.
本文讨论了基于 ChatGPT 的语言建模在英语教学中的多场景应用，并进行了实证实验以支持研究成果。研究内容包括构建和分析英语作文评分和相似性检测模型。研究采用了 BERT-BiLSTM 算法，并与 Word2Vec-BiLSTM 模型进行了比较。基于 BERT-BiLSTM 的英语作文评分模型与原始评分具有较高的相关性和一致性，平均相关性为 0.72，一致性为 82%。相反，Word2Vec-BiLSTM 模型的相关性和一致性较低。我们创建了一个模型，并在实验中使用不同的 K 值来检测英语作文的相似性。在 K 值为 200 时，正确率、召回率和 F1 指标都较高，F1 值在 89.35% 和 95.14% 之间波动。这些都证明了基于 ChatGPT 的语言建模在英语教学中的高准确性和高效性。
{"title":"Multi-scenario application of Chatgpt-based language modeling for empowering English language teaching and learning","authors":"Hui Sun","doi":"10.2478/amns-2024-0790","DOIUrl":"https://doi.org/10.2478/amns-2024-0790","url":null,"abstract":"\u0000 This paper discusses the multi-scenario application of ChatGPT-based language modeling in English language teaching, and empirical experiments are conducted to support the research findings. The study includes constructing and analyzing English composition scoring and similarity detection models. The BERT-BiLSTM algorithm was utilized and compared to the Word2Vec-BiLSTM model. The BERT-BiLSTM-based English composition scoring model has a high correlation and consistency with the original scores, with an average correlation of 0.72 and a consistency of 82%. Conversely, the Word2Vec-BiLSTM model has a lesser correlation and consistency. We created a model and used different K values for the experiment to detect English composition similarity. The correctness, recall, and F1 measures were higher at a K value 200, with F1 values fluctuating between 89.35% and 95.14%. These support the high accuracy and efficiency of ChatGPT-based language modeling in English language teaching.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140515870","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 burgeoning realm of Internet technology has ushered e-commerce into a pivotal economic role. However, navigating the myriad risks inherent in e-commerce operations is vital for the sustained growth of businesses in this sector. This study melds economic management principles with a deep dive into e-commerce risk management, focusing on predictive strategies and mitigation measures. We commence by dissecting the principal risk categories within e-commerce operations. Subsequently, we employ Structural Equation Modeling (SEM) and Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for quantitatively dissection of these risk factors. Our findings pinpoint internal, technological, and operational management risks as the critical triad influencing e-commerce strategic operations. Remarkably, the PSO-GRNN model’s risk prediction accuracy stands at 93.62%, outstripping conventional models significantly. Through this research, we offer a robust framework for e-commerce entities to enhance their strategic foresight and resilience, aiding in optimizing their strategic maneuvers.
{"title":"Risk prediction and control of strategic operation of e-commerce enterprises based on economic management science","authors":"Qingyu Hong, Lei Luo, Yanting Zhang","doi":"10.2478/amns-2024-0763","DOIUrl":"https://doi.org/10.2478/amns-2024-0763","url":null,"abstract":"\u0000 The burgeoning realm of Internet technology has ushered e-commerce into a pivotal economic role. However, navigating the myriad risks inherent in e-commerce operations is vital for the sustained growth of businesses in this sector. This study melds economic management principles with a deep dive into e-commerce risk management, focusing on predictive strategies and mitigation measures. We commence by dissecting the principal risk categories within e-commerce operations. Subsequently, we employ Structural Equation Modeling (SEM) and Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for quantitatively dissection of these risk factors. Our findings pinpoint internal, technological, and operational management risks as the critical triad influencing e-commerce strategic operations. Remarkably, the PSO-GRNN model’s risk prediction accuracy stands at 93.62%, outstripping conventional models significantly. Through this research, we offer a robust framework for e-commerce entities to enhance their strategic foresight and resilience, aiding in optimizing their strategic maneuvers.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140526859","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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