INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed.
{"title":"Design and Use of Deep Confidence Network Based on Crayfish Optimization Algorithm in Automatic Assessment Method of Hearing Effectiveness","authors":"Ying Cheng","doi":"10.4108/eetsis.4847","DOIUrl":"https://doi.org/10.4108/eetsis.4847","url":null,"abstract":"INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"21 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809078","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}
INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.
{"title":"A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network","authors":"Lili Ma, Bin Xie, Fengjun Liu, Liying Ma","doi":"10.4108/eetsis.4844","DOIUrl":"https://doi.org/10.4108/eetsis.4844","url":null,"abstract":"INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"181 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870462","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}
INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.
{"title":"Improved Nuclear Reaction Heuristic Intelligence Algorithm for Online Learning in Self-Monitoring Strategy Convergence","authors":"Fengjun Liu, Yang Lu, Bin Xie, Lili Ma","doi":"10.4108/eetsis.4848","DOIUrl":"https://doi.org/10.4108/eetsis.4848","url":null,"abstract":"INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategy-integrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning.\u0000OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning self-monitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments.\u0000RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05.\u0000CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"28 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870132","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}
INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers. OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems. METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system; (2) Through the multi-strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges; (3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method. RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time. CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.
{"title":"Research on Employee Performance Management Method Based on Big Data Improvement GWO-DELM Algorithms","authors":"Zhuyu Wang, Yue Liu","doi":"10.4108/eetsis.4916","DOIUrl":"https://doi.org/10.4108/eetsis.4916","url":null,"abstract":"INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers.\u0000OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems.\u0000METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system; (2) Through the multi-strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges; (3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method.\u0000RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time.\u0000CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"74 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870682","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}
INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing. OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology. METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test. RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing. CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training.
{"title":"Edge Computing-Based Athletic Ability Testing for Sports","authors":"Chen Yang, Hui Ma","doi":"10.4108/eetsis.4730","DOIUrl":"https://doi.org/10.4108/eetsis.4730","url":null,"abstract":"INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing.\u0000OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology.\u0000METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test.\u0000RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing.\u0000CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139869059","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}
INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.
{"title":"Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm","authors":"Zhenghan Gao, Anzhu Zheng","doi":"10.4108/eetsis.4873","DOIUrl":"https://doi.org/10.4108/eetsis.4873","url":null,"abstract":"INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion.\u0000OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive.\u0000METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments.\u0000RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques.\u0000CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"53 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808827","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}
INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed.
{"title":"Design and Use of Deep Confidence Network Based on Crayfish Optimization Algorithm in Automatic Assessment Method of Hearing Effectiveness","authors":"Ying Cheng","doi":"10.4108/eetsis.4847","DOIUrl":"https://doi.org/10.4108/eetsis.4847","url":null,"abstract":"INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment.OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance.METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments.RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm.CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed. ","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"20 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868825","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}
INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing. OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology. METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test. RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing. CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training.
{"title":"Edge Computing-Based Athletic Ability Testing for Sports","authors":"Chen Yang, Hui Ma","doi":"10.4108/eetsis.4730","DOIUrl":"https://doi.org/10.4108/eetsis.4730","url":null,"abstract":"INTRODUCTION: After the 2008 Olympic Games, China has gradually become a prominent sports country, but there is still a certain distance from a sports power. China should improve the level of sports ability testing while continuously strengthening the construction of sports power. At present, the method of sports professional athletic ability tests in China can not be better combined with algorithms, so it is crucial to study the athletic ability test of edge computing.\u0000OBJECTIVES: To improve the ability of sports testing of sports majors in China, to improve the technical level of the construction of China's sports power, to solve the problem that China's sports ability testing cannot be better combined with algorithms, and to solve the problem that China's physical education disciplines cannot be well applied to computer technology.\u0000METHODS: Use the motor function theory and edge computing to establish the model needed, test the athletic ability of swimming sports according to the model, and analyze the advanced level and shortcomings of China's swimming sports with measurement according to the results of the athletic ability test.\u0000RESULTS: Firstly, edge computing and other algorithms are more accurate for professional athletic ability testing of swimming sports, and improving the iteration level of algorithms can improve the problem of the inconspicuous effect of sports testing; secondly, edge algorithms combined with traditional testing tools can calculate athletic ability more accurately in athletic ability testing.\u0000CONCLUSION: China should vigorously improve the level of edge computing and other algorithms to improve the problem of China's sports disciplines not being able to apply computer technology well and technically improve the level of sports training.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139809205","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}
INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.
{"title":"Design of New Media Event Warning Method Based on K-means and Seasonal Optimization Algorithm","authors":"Zhenghan Gao, Anzhu Zheng","doi":"10.4108/eetsis.4873","DOIUrl":"https://doi.org/10.4108/eetsis.4873","url":null,"abstract":"INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion.\u0000OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive.\u0000METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments.\u0000RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques.\u0000CONCLUSION: Addresses the lack of comprehensiveness of current approaches to early warning of new media events.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139868791","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}
S. Balaji, S. Jeevanandham, Mani Deepak Choudhry, M. Sundarrajan, Rajesh Kumar Dhanaraj
INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands.
{"title":"Data Aggregation through Hybrid Optimal Probability in Wireless Sensor Networks","authors":"S. Balaji, S. Jeevanandham, Mani Deepak Choudhry, M. Sundarrajan, Rajesh Kumar Dhanaraj","doi":"10.4108/eetsis.4996","DOIUrl":"https://doi.org/10.4108/eetsis.4996","url":null,"abstract":" \u0000INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. \u0000OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. \u0000METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. \u0000RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. \u0000CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands.","PeriodicalId":155438,"journal":{"name":"ICST Transactions on Scalable Information Systems","volume":"26 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139890127","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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