Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00076
Chunting Li, Honglin Chen
Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a “Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision”. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.
{"title":"Research on Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision","authors":"Chunting Li, Honglin Chen","doi":"10.1109/ICCSMT54525.2021.00076","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00076","url":null,"abstract":"Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a “Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision”. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961971","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00078
Wenya Zhou
Most mainstream measures of economic development employ a weighted scoring system under the assumption that each indicator can perfectly substitute each other, which is a strong assumption that may vary from the real world. In this paper, the author uses the K-Means machine learning algorithm to cluster the 195 countries in the world, as an attempt to provide a more holistic view of each country's level of economic development without employing the assumption. With the assistance of silhouette scores, the algorithm created 6 clusters, each with its distinctive properties that future researchers or policy makers can rely upon to generate country-specific views about economic development. Nevertheless, manual inspection of the result discovers the potential problem with the incomplete datasets and the need for a PCA test to reduce dimensions. Considerations of realistic implications also suggest that the standard K-Means clustering might be over-simplifying the complicated nature of some country's economic problems.
{"title":"K-Means Clustering Algorithm Analysis on Specific Economic Development Problems in Target Countries","authors":"Wenya Zhou","doi":"10.1109/ICCSMT54525.2021.00078","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00078","url":null,"abstract":"Most mainstream measures of economic development employ a weighted scoring system under the assumption that each indicator can perfectly substitute each other, which is a strong assumption that may vary from the real world. In this paper, the author uses the K-Means machine learning algorithm to cluster the 195 countries in the world, as an attempt to provide a more holistic view of each country's level of economic development without employing the assumption. With the assistance of silhouette scores, the algorithm created 6 clusters, each with its distinctive properties that future researchers or policy makers can rely upon to generate country-specific views about economic development. Nevertheless, manual inspection of the result discovers the potential problem with the incomplete datasets and the need for a PCA test to reduce dimensions. Considerations of realistic implications also suggest that the standard K-Means clustering might be over-simplifying the complicated nature of some country's economic problems.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125141646","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00039
Wangchun Zhang
Enterprises are aware of the important value of social resources with the era of digital economy and begin to pay attention to the social commerce combining social networks with traditional e-commerce. However, social commerce faces the problem of social media and traditional e-commerce. Based on the Stimulus-Organism-Response (SOR) framework, this study explores how users' perceived overload (information, system feature and social overload) affects their continuance intention mediated by two perceived states (social support and perceived risk). The results show that only information overload and system feature overload significantly affect informational support and emotional support, while social overload and system feature overload significantly affect perceived risk. In addition, only emotional support and perceived risk affects users' continuance intention. Both the theoretical and practical implications are discussed.
{"title":"The Impact of User Perceived Overload on Continuance Intention to Use Social Commerce: —Based on Stimulus-Organism-Response Model","authors":"Wangchun Zhang","doi":"10.1109/ICCSMT54525.2021.00039","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00039","url":null,"abstract":"Enterprises are aware of the important value of social resources with the era of digital economy and begin to pay attention to the social commerce combining social networks with traditional e-commerce. However, social commerce faces the problem of social media and traditional e-commerce. Based on the Stimulus-Organism-Response (SOR) framework, this study explores how users' perceived overload (information, system feature and social overload) affects their continuance intention mediated by two perceived states (social support and perceived risk). The results show that only information overload and system feature overload significantly affect informational support and emotional support, while social overload and system feature overload significantly affect perceived risk. In addition, only emotional support and perceived risk affects users' continuance intention. Both the theoretical and practical implications are discussed.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125265993","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00046
Ping Mu
In the information age, ERP financial software has been widely used in various industries, and its application effect is very significant. In order to give full play to the role of ERP financial software itself and further improve the level of corporate financial management, we need to strengthen the study of its specific application in the enterprise. Although ERP financial software is helpful to the financial management level of enterprises, because each enterprise has different situations, we need to apply ERP financial software flexibly in accordance with the actual situation. In view of the actual situation of corporate financial management, we should pay close attention to some issues to ensure that the software can healthly integrate corporate financial management.
{"title":"Research on the Application of ERP Financial Software in Enterprises","authors":"Ping Mu","doi":"10.1109/ICCSMT54525.2021.00046","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00046","url":null,"abstract":"In the information age, ERP financial software has been widely used in various industries, and its application effect is very significant. In order to give full play to the role of ERP financial software itself and further improve the level of corporate financial management, we need to strengthen the study of its specific application in the enterprise. Although ERP financial software is helpful to the financial management level of enterprises, because each enterprise has different situations, we need to apply ERP financial software flexibly in accordance with the actual situation. In view of the actual situation of corporate financial management, we should pay close attention to some issues to ensure that the software can healthly integrate corporate financial management.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378802","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00116
Nan Cheng, Hailiang Wu, Zhong Liu, Yamin Wang, Wuchen Zhang, Jizhi Su
In view of the current problems of substation fire protection, the risk structure decomposition method is used to identify risks, and the risk function is introduced to analyze the risk areas in the entire station area, build a risk evaluation matrix, and take risk measures by area to form a typical area intelligent fire protection Terminal layout plan, build a substation fire protection perception system. The typical regional intelligent fire terminal layout plan formed can provide a scientific basis for the construction and transformation of the substation fire protection system in the future.
{"title":"Research and application of Substation Fire Protection System based on big data","authors":"Nan Cheng, Hailiang Wu, Zhong Liu, Yamin Wang, Wuchen Zhang, Jizhi Su","doi":"10.1109/ICCSMT54525.2021.00116","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00116","url":null,"abstract":"In view of the current problems of substation fire protection, the risk structure decomposition method is used to identify risks, and the risk function is introduced to analyze the risk areas in the entire station area, build a risk evaluation matrix, and take risk measures by area to form a typical area intelligent fire protection Terminal layout plan, build a substation fire protection perception system. The typical regional intelligent fire terminal layout plan formed can provide a scientific basis for the construction and transformation of the substation fire protection system in the future.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441015","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00041
Shuguang Wang, Mengshan Li
All aspects of the construction of the smart city need to rely on the information management platforms to achieve sustainable expansion and intelligent integration. It also needs to rely on the information model to obtain reliable big data onto real-time sharing, so as to improve the efficiency of urban governance of multiple dimensions. The construction and application of emergency big data and intelligent security emergency management platform will help to improve emergency management efficiency and reduce losses caused by emergencies. This paper expounds the problems existing on the emergency management of public safety problems with the smart city, uses research methods such as data analysis, is committed to the collection, processing and analysis of big data of the emergency management system, scientifically forecasts the public emergency management needs of the smart city, and puts forward suggestions to improve the public safety emergency management in combination with the concept of the smart city.
{"title":"Research on public safety emergency management of “Smart city”","authors":"Shuguang Wang, Mengshan Li","doi":"10.1109/ICCSMT54525.2021.00041","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00041","url":null,"abstract":"All aspects of the construction of the smart city need to rely on the information management platforms to achieve sustainable expansion and intelligent integration. It also needs to rely on the information model to obtain reliable big data onto real-time sharing, so as to improve the efficiency of urban governance of multiple dimensions. The construction and application of emergency big data and intelligent security emergency management platform will help to improve emergency management efficiency and reduce losses caused by emergencies. This paper expounds the problems existing on the emergency management of public safety problems with the smart city, uses research methods such as data analysis, is committed to the collection, processing and analysis of big data of the emergency management system, scientifically forecasts the public emergency management needs of the smart city, and puts forward suggestions to improve the public safety emergency management in combination with the concept of the smart city.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111598","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00080
Honglin Chen, Chunting Li
Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.
{"title":"Pruning Deep Feature Networks Using Channel Importance Propagation","authors":"Honglin Chen, Chunting Li","doi":"10.1109/ICCSMT54525.2021.00080","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00080","url":null,"abstract":"Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129734081","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00084
Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu
Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.
{"title":"A Fast and Efficient Lines Matching Method via Multi-depth-layer Strategy","authors":"Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu","doi":"10.1109/ICCSMT54525.2021.00084","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00084","url":null,"abstract":"Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130024622","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00010
Hai-Feng Zhang, Yeqiu Wang
Based on the panel data of computer software and information technology service industry agglomeration in 28 provinces and cities in China from 2015 to 2019, this paper calculates the agglomeration and other indicators, and uses the threshold effect model to study the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness. The results show that when agglomeration is taken as the threshold variable, there is a significant single threshold for manufacturing competitiveness. The degree of competition and the investment of per capita GDP can significantly promote the improvement of manufacturing competitiveness. The impact of regional economy and openness on manufacturing competitiveness is contrary, and the interaction needs to be improved.
{"title":"Threshold regression analysis is used to analyze the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness","authors":"Hai-Feng Zhang, Yeqiu Wang","doi":"10.1109/ICCSMT54525.2021.00010","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00010","url":null,"abstract":"Based on the panel data of computer software and information technology service industry agglomeration in 28 provinces and cities in China from 2015 to 2019, this paper calculates the agglomeration and other indicators, and uses the threshold effect model to study the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness. The results show that when agglomeration is taken as the threshold variable, there is a significant single threshold for manufacturing competitiveness. The degree of competition and the investment of per capita GDP can significantly promote the improvement of manufacturing competitiveness. The impact of regional economy and openness on manufacturing competitiveness is contrary, and the interaction needs to be improved.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492302","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 : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00082
Hong Yan, Xinyue Ma, Shengwen He
The purpose of this paper is to solve the problem of intelligent analysis of learners' behavior and intelligent recommendation in the domain of medical online education. The teaching behavior has transformed from experience teaching into massive data teaching. Moreover, the learning behavior is also changed from centralized learning to fragmented learning. In this paper, we study the method of personal education recommendation to meet these challenges. In this paper, a novel multi-view extreme learning machine model is proposed. We can get the optimized classification results. Based on these results, we proposed a collaborative filtering based personal recommendation method and applied via Spark framework. The experimental results show that, based on the effective analysis of learning behavior, the proposed method can be used to recommend the medical online learning content for the learners in practical teaching. In this paper, data mining and recommendation methods are realized in the field of medical online education. The methodological research and case studies can meet the needs of medical online education.
{"title":"Research on Real-time Medical Online Learning Content Recommendation based on Multi-view Data Mining","authors":"Hong Yan, Xinyue Ma, Shengwen He","doi":"10.1109/ICCSMT54525.2021.00082","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00082","url":null,"abstract":"The purpose of this paper is to solve the problem of intelligent analysis of learners' behavior and intelligent recommendation in the domain of medical online education. The teaching behavior has transformed from experience teaching into massive data teaching. Moreover, the learning behavior is also changed from centralized learning to fragmented learning. In this paper, we study the method of personal education recommendation to meet these challenges. In this paper, a novel multi-view extreme learning machine model is proposed. We can get the optimized classification results. Based on these results, we proposed a collaborative filtering based personal recommendation method and applied via Spark framework. The experimental results show that, based on the effective analysis of learning behavior, the proposed method can be used to recommend the medical online learning content for the learners in practical teaching. In this paper, data mining and recommendation methods are realized in the field of medical online education. The methodological research and case studies can meet the needs of medical online education.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116969162","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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