Pub Date : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00067
Runtian Wang, Chengtao Cai
Person Re-identification (ReID), under the same or different cameras, judges whether two or more pictures are the same person through feature extraction and match of images of pedestrian detection box. In the past period of time, a lot of progress has been made in the field of pedestrian reidentification, and the recognition rate has reached a very high level. One important reason is that people re-examined the loss function, proposed various variants, and the effect has been greatly improved. In this paper, two different loss functions are used for comparison, and the influence of different loss functions on pedestrian re-recognition results is analyzed through the comparison results.
{"title":"The Effect of Differents Loss Function in Person Re-identification","authors":"Runtian Wang, Chengtao Cai","doi":"10.1109/ISAIEE57420.2022.00067","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00067","url":null,"abstract":"Person Re-identification (ReID), under the same or different cameras, judges whether two or more pictures are the same person through feature extraction and match of images of pedestrian detection box. In the past period of time, a lot of progress has been made in the field of pedestrian reidentification, and the recognition rate has reached a very high level. One important reason is that people re-examined the loss function, proposed various variants, and the effect has been greatly improved. In this paper, two different loss functions are used for comparison, and the influence of different loss functions on pedestrian re-recognition results is analyzed through the comparison results.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115501512","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00145
Shuguo Che
The improved ID3 algorithm is based on the simulation of the human brain for information processing, through the user feedback, the system will analyze the user input data, and then according to the analysis results to determine whether the system meets the teaching needs. The system analyzes the user feedback data, and then judges whether the teaching quality meets the requirements based on the results. This paper firstly analyzes the ID3 algorithm, which is a kind of decision tree learning algorithm based on information first, the algorithm has good global search ability, it can analyze the user feedback data; secondly analyzes the research of the improved ID3 algorithm; finally, the design research and experiment of the physical education teaching quality evaluation system are carried out, through the analysis of test data, the experiment proves that the system has good teaching quality.
{"title":"Design of College Physical Education Teaching Quality Evaluation System Based on Improved ID3 Algorithm","authors":"Shuguo Che","doi":"10.1109/ISAIEE57420.2022.00145","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00145","url":null,"abstract":"The improved ID3 algorithm is based on the simulation of the human brain for information processing, through the user feedback, the system will analyze the user input data, and then according to the analysis results to determine whether the system meets the teaching needs. The system analyzes the user feedback data, and then judges whether the teaching quality meets the requirements based on the results. This paper firstly analyzes the ID3 algorithm, which is a kind of decision tree learning algorithm based on information first, the algorithm has good global search ability, it can analyze the user feedback data; secondly analyzes the research of the improved ID3 algorithm; finally, the design research and experiment of the physical education teaching quality evaluation system are carried out, through the analysis of test data, the experiment proves that the system has good teaching quality.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115718442","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00009
Jinhong Zhu
It is time-consuming and error-prone to manually determine whether there is a brain tumor in an image. However, traditional automatic classification algorithms have certain limitations, which makes the automation of brain tumor classification still a challenging problem. In this article, a new method for automatic classification of brain tumors is proposed, which combines neural network models with transfer learning methods, so as to improve or solve the problem of slow iteration and long time-consuming model generation, improve accuracy, and reduce parameter. In short, the convolutional neural network model (CNN) is combined with the method of transfer learning to achieve automatic image classification on the Brain Tumor Detection 2020 dataset provided by Model Whale. More specifically, during the experiment, Tensorflow was selected as the deep learning framework. First, the transfer learning method was used, and imagenet weights were used. Then, Comparing model performance by changing the choice of the backbone network of the CNN. Select the accuracy rate as the evaluation index, compare the performance of the model, use binary_crossentropy as the loss function, and the optimizer uses adam. In this paper, three backbone networks, VGG, MobileNet and ResNet, are compared. Experimental results indicate that the automatic classification of brain tumors with the combination of CNN model and transfer learning method has better performance and the VGG model has the best performance.
{"title":"Automatic Brain Tumor Classification Based on Transfer Learning Models","authors":"Jinhong Zhu","doi":"10.1109/ISAIEE57420.2022.00009","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00009","url":null,"abstract":"It is time-consuming and error-prone to manually determine whether there is a brain tumor in an image. However, traditional automatic classification algorithms have certain limitations, which makes the automation of brain tumor classification still a challenging problem. In this article, a new method for automatic classification of brain tumors is proposed, which combines neural network models with transfer learning methods, so as to improve or solve the problem of slow iteration and long time-consuming model generation, improve accuracy, and reduce parameter. In short, the convolutional neural network model (CNN) is combined with the method of transfer learning to achieve automatic image classification on the Brain Tumor Detection 2020 dataset provided by Model Whale. More specifically, during the experiment, Tensorflow was selected as the deep learning framework. First, the transfer learning method was used, and imagenet weights were used. Then, Comparing model performance by changing the choice of the backbone network of the CNN. Select the accuracy rate as the evaluation index, compare the performance of the model, use binary_crossentropy as the loss function, and the optimizer uses adam. In this paper, three backbone networks, VGG, MobileNet and ResNet, are compared. Experimental results indicate that the automatic classification of brain tumors with the combination of CNN model and transfer learning method has better performance and the VGG model has the best performance.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116780967","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00108
Runyi Liu, Linzhu Liu
In order to improve the personalized recommendation ability of entertainment news, this paper puts forward a personalized recommendation method of entertainment news based on collaborative filtering algorithm and deep semantic mining. Using word vector, neural topic model and other technologies to mine semantic information in news text to obtain news feature representation vector, and integrating all semantic features to represent user's preference vector, and then generating candidate sets that users may be interested in by matching, in the ranking stage of news recommendation, collaborative filtering algorithm is adopted to filter out interference options, and deep semantic mining technology is combined to realize dynamic mining and detection of entertainment news that users are interested in. Aiming at the news candidate set generated in the recall stage, the self-attention mechanism and other technologies are used to model the reading behavior sequences of users in different periods, and the learning of users' long-term and short-term preferences is completed by combining the attention mechanism, so that the click-through rate of candidate news can be predicted, and accurate recommendation can be made to users. The simulation results show that the personalized recommendation of entertainment news by this method has better pertinence and higher recommendation satisfaction, and improves the ability of emotional classification and feature enhancement of entertainment news.
{"title":"Design of personalized recommendation method for entertainment news based on collaborative filtering algorithm","authors":"Runyi Liu, Linzhu Liu","doi":"10.1109/ISAIEE57420.2022.00108","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00108","url":null,"abstract":"In order to improve the personalized recommendation ability of entertainment news, this paper puts forward a personalized recommendation method of entertainment news based on collaborative filtering algorithm and deep semantic mining. Using word vector, neural topic model and other technologies to mine semantic information in news text to obtain news feature representation vector, and integrating all semantic features to represent user's preference vector, and then generating candidate sets that users may be interested in by matching, in the ranking stage of news recommendation, collaborative filtering algorithm is adopted to filter out interference options, and deep semantic mining technology is combined to realize dynamic mining and detection of entertainment news that users are interested in. Aiming at the news candidate set generated in the recall stage, the self-attention mechanism and other technologies are used to model the reading behavior sequences of users in different periods, and the learning of users' long-term and short-term preferences is completed by combining the attention mechanism, so that the click-through rate of candidate news can be predicted, and accurate recommendation can be made to users. The simulation results show that the personalized recommendation of entertainment news by this method has better pertinence and higher recommendation satisfaction, and improves the ability of emotional classification and feature enhancement of entertainment news.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116954678","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00100
Xu Qiangsheng, Tian Biye, Zhou Jinping, Ma Qiang
It is an inevitable trend that investment promotes supply side structural adjustment and demand side response to achieve bilateral power generation. Investment strategies and development paths in key areas of power grid in the future are the focus of power enterprises. Considering the derivative value of power grid investment, this paper proposes a smart grid investment decision evaluation model based on binomial coefficient and variation coefficient. First, the concept, criteria and methods of grid investment derivative value are summarized; Secondly, the smart grid investment derivative value index system is constructed from three aspects: grid investment benefit, grid operation and maintenance effect, and sustainable development value; Based on binomial coefficient method and variation coefficient method,a combination weighting method of subjective and objective fusion is constructed, and dynamic combination factors are adjusted according to different scene requirements to achieve dynamic balance of multiple objectives, reflecting the complementary advantages of subjective and objective methods. At last.we use Curve Comparison Chart and Specific 3D Curve to prove the effectiveness and accuracy of our method.
{"title":"Power grid investment decision optimization based on binomial coefficient and variation coefficient","authors":"Xu Qiangsheng, Tian Biye, Zhou Jinping, Ma Qiang","doi":"10.1109/ISAIEE57420.2022.00100","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00100","url":null,"abstract":"It is an inevitable trend that investment promotes supply side structural adjustment and demand side response to achieve bilateral power generation. Investment strategies and development paths in key areas of power grid in the future are the focus of power enterprises. Considering the derivative value of power grid investment, this paper proposes a smart grid investment decision evaluation model based on binomial coefficient and variation coefficient. First, the concept, criteria and methods of grid investment derivative value are summarized; Secondly, the smart grid investment derivative value index system is constructed from three aspects: grid investment benefit, grid operation and maintenance effect, and sustainable development value; Based on binomial coefficient method and variation coefficient method,a combination weighting method of subjective and objective fusion is constructed, and dynamic combination factors are adjusted according to different scene requirements to achieve dynamic balance of multiple objectives, reflecting the complementary advantages of subjective and objective methods. At last.we use Curve Comparison Chart and Specific 3D Curve to prove the effectiveness and accuracy of our method.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121109108","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00122
Jiajian Zhao, Yingying Sun, Chao Xu, Feijie Wang
To solve the surge in passenger flow caused by the major urban activities or other events, this article is to provide a smart passenger evacuation method in urban rail transit. The passenger evacuation calculation is based on the crowding degree and passenger flow at the current station and passenger flow at the next station, calculates the station dwell time and the inter-section running time (or the train performance level), by increasing the station dwell time and shortening the inter-section running time, achieves rapid evacuation of passengers, shortens the waiting time for passengers, and reduces manual intervention, without causing other secondary effects.
{"title":"Research on a Smart Passenger Evacuation Method Based on Passenger Flow and Train Load Factor","authors":"Jiajian Zhao, Yingying Sun, Chao Xu, Feijie Wang","doi":"10.1109/ISAIEE57420.2022.00122","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00122","url":null,"abstract":"To solve the surge in passenger flow caused by the major urban activities or other events, this article is to provide a smart passenger evacuation method in urban rail transit. The passenger evacuation calculation is based on the crowding degree and passenger flow at the current station and passenger flow at the next station, calculates the station dwell time and the inter-section running time (or the train performance level), by increasing the station dwell time and shortening the inter-section running time, achieves rapid evacuation of passengers, shortens the waiting time for passengers, and reduces manual intervention, without causing other secondary effects.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358798","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00024
Guanhao Gao
In order to improve the capabilities of parallel processing and planning of large volumes of electricity data, an intelligent platform for processing large volumes of electricity data developed on the basis of the Hadoop framework is proposed. The research includes a data processing module, a data loading module, a bus control module, and a human-machine interaction module to build an integrated database model for the parallel processing of big data in the field of electricity. The results of the simulation tests show that the intelligent big electricity data processing platform developed in this study can efficiently combine data mining and resource planning capabilities, further improve the integrated big electric data processing capability, and provide a new idea for an efficient solution for the efficient management and analysis of big electricity data, which is crucial for the development of electric systems.
{"title":"Design of Intelligent Processing Platform for Electric Big Data in Hadoop","authors":"Guanhao Gao","doi":"10.1109/ISAIEE57420.2022.00024","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00024","url":null,"abstract":"In order to improve the capabilities of parallel processing and planning of large volumes of electricity data, an intelligent platform for processing large volumes of electricity data developed on the basis of the Hadoop framework is proposed. The research includes a data processing module, a data loading module, a bus control module, and a human-machine interaction module to build an integrated database model for the parallel processing of big data in the field of electricity. The results of the simulation tests show that the intelligent big electricity data processing platform developed in this study can efficiently combine data mining and resource planning capabilities, further improve the integrated big electric data processing capability, and provide a new idea for an efficient solution for the efficient management and analysis of big electricity data, which is crucial for the development of electric systems.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"66 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129204000","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00098
Tengfei Liu, Xiao-qing Zhu
This paper mainly designs and analyzes the mechanical hardware structure of an hydrospace detection autonomous underwater vehicle. Using Catia, Adams and other computer-aided design and analysis software, the shape structure of the underwater robot is designed and its mechanical characteristics in the underwater hydrospace detection work is analyzed. Comprehensively considered the impact of the use of the relevant parts of the underwater robot, such as the driving motor, camera and other parts on the shape, weight, size and other aspects of the underwater robot, the hardware structure of the autonomous underwater vehicle is optimized, and the feasibility of its underwater work is verified by simulation experiments.
{"title":"Design and Kinematic Simulation of an Hydrospace Detection Autonomous Underwater Vehicle","authors":"Tengfei Liu, Xiao-qing Zhu","doi":"10.1109/ISAIEE57420.2022.00098","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00098","url":null,"abstract":"This paper mainly designs and analyzes the mechanical hardware structure of an hydrospace detection autonomous underwater vehicle. Using Catia, Adams and other computer-aided design and analysis software, the shape structure of the underwater robot is designed and its mechanical characteristics in the underwater hydrospace detection work is analyzed. Comprehensively considered the impact of the use of the relevant parts of the underwater robot, such as the driving motor, camera and other parts on the shape, weight, size and other aspects of the underwater robot, the hardware structure of the autonomous underwater vehicle is optimized, and the feasibility of its underwater work is verified by simulation experiments.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396857","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00127
Hongze Yu
Digital rural intelligent tourism is an important symbol of social modernization. In this paper, the intelligent optimization algorithm is used to conduct data modeling of rural intelligent tourism. By processing multiple targets of tourism, the innovation of digital rural intelligent tourism model is finally completed, so as to promote the rapid development of rural tourism. Smart tourism mode is an important means for the development of the industry. The adoption of advanced algorithms to enhance the innovation of the mode is conducive to the development and income generation of tourism. This paper studies the construction of digital rural intelligent tourism model on account of intelligent optimization algorithm, and explains the development and working principle of digital rural intelligent tourism model. The data analysis proves that the research on the construction of digital rural intelligent tourism model on account of the intelligent optimization algorithm has an efficient performance in the construction of digital rural intelligent tourism model.
{"title":"Digital Rural Intelligent Tourism Model on Account of Intelligent Optimization Algorithm","authors":"Hongze Yu","doi":"10.1109/ISAIEE57420.2022.00127","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00127","url":null,"abstract":"Digital rural intelligent tourism is an important symbol of social modernization. In this paper, the intelligent optimization algorithm is used to conduct data modeling of rural intelligent tourism. By processing multiple targets of tourism, the innovation of digital rural intelligent tourism model is finally completed, so as to promote the rapid development of rural tourism. Smart tourism mode is an important means for the development of the industry. The adoption of advanced algorithms to enhance the innovation of the mode is conducive to the development and income generation of tourism. This paper studies the construction of digital rural intelligent tourism model on account of intelligent optimization algorithm, and explains the development and working principle of digital rural intelligent tourism model. The data analysis proves that the research on the construction of digital rural intelligent tourism model on account of the intelligent optimization algorithm has an efficient performance in the construction of digital rural intelligent tourism model.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115882485","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 : 2022-12-01DOI: 10.1109/ISAIEE57420.2022.00107
Sun Wei, Lei Zhang, Jing Li
The existing generalization-based location privacy protection scheme of Crowd-Sensing cannot balance user privacy needs and data quality, and uses a uniform policy to protect all locations of all users. To address this problem, this paper proposes a personalized privacy protection scheme, which first calculates the sensitivity of each location based on location entropy, access frequency and other features, and then encodes the location using Geo-hash code. The prefixes of different lengths are chosen according to the different sensitivities to achieve different strengths of privacy protection. Finally, a reasonable gridding algorithm is designed so that the data quality does not degrade as the protection strength increases, thus achieving the goal of improving data quality while protecting the user's location privacy. Finally, the effectiveness of the proposed algorithm is further verified through experiments.
{"title":"Personalised Location Privacy Protection based on Grid Division for Crowd-Sensing","authors":"Sun Wei, Lei Zhang, Jing Li","doi":"10.1109/ISAIEE57420.2022.00107","DOIUrl":"https://doi.org/10.1109/ISAIEE57420.2022.00107","url":null,"abstract":"The existing generalization-based location privacy protection scheme of Crowd-Sensing cannot balance user privacy needs and data quality, and uses a uniform policy to protect all locations of all users. To address this problem, this paper proposes a personalized privacy protection scheme, which first calculates the sensitivity of each location based on location entropy, access frequency and other features, and then encodes the location using Geo-hash code. The prefixes of different lengths are chosen according to the different sensitivities to achieve different strengths of privacy protection. Finally, a reasonable gridding algorithm is designed so that the data quality does not degrade as the protection strength increases, thus achieving the goal of improving data quality while protecting the user's location privacy. Finally, the effectiveness of the proposed algorithm is further verified through experiments.","PeriodicalId":345703,"journal":{"name":"2022 International Symposium on Advances in Informatics, Electronics and Education (ISAIEE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131026766","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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