A crowd clustering prediction and captioning technique for public health emergencies
The COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-05-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1283.pdf |
| _version_ | 1797832594705874944 |
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| author | Xiaoling Zhou Guiping Zhu |
| author_facet | Xiaoling Zhou Guiping Zhu |
| author_sort | Xiaoling Zhou |
| collection | DOAJ |
| description | The COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal crowd gatherings. We suggest a crowd clustering prediction and captioning technique based on a global neural network to detect and caption these scenes rapidly and effectively. We superimpose two long convolution lines for the residual structure, which may produce a broad sensing region and apply our model’s fewer parameters to ensure a wide sensing region, less computation, and increased efficiency of our method. After that, we can travel to the areas where people are congregating. So, to produce news material about the present occurrence, we suggest a double-LSTM model. We train and test our upgraded crowds-gathering model using the ShanghaiTech dataset and assess our captioning model on the MSCOCO dataset. The results of the experiment demonstrate that using our strategy can significantly increase the accuracy of the crowd clustering model, as well as minimize MAE and MSE. Our model can produce competitive results for scene captioning compared to previous approaches. |
| first_indexed | 2024-04-09T14:10:16Z |
| format | Article |
| id | doaj.art-d728b436c4b1461e8d2ff9e67da3265a |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-09T14:10:16Z |
| publishDate | 2023-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-d728b436c4b1461e8d2ff9e67da3265a2023-05-06T15:05:04ZengPeerJ Inc.PeerJ Computer Science2376-59922023-05-019e128310.7717/peerj-cs.1283A crowd clustering prediction and captioning technique for public health emergenciesXiaoling Zhou0Guiping Zhu1School of Journalism and Communication, Nanjing Normal University, Nanjing, Jiangsu, ChinaNanjing Television Station, Nanjing, Jiangsu, ChinaThe COVID-19 pandemic has come to the end. People have started to consider how quickly different industries can respond to disasters due to this public health emergency. The most noticeable aspect of the epidemic regarding news text generation and social issues is detecting and identifying abnormal crowd gatherings. We suggest a crowd clustering prediction and captioning technique based on a global neural network to detect and caption these scenes rapidly and effectively. We superimpose two long convolution lines for the residual structure, which may produce a broad sensing region and apply our model’s fewer parameters to ensure a wide sensing region, less computation, and increased efficiency of our method. After that, we can travel to the areas where people are congregating. So, to produce news material about the present occurrence, we suggest a double-LSTM model. We train and test our upgraded crowds-gathering model using the ShanghaiTech dataset and assess our captioning model on the MSCOCO dataset. The results of the experiment demonstrate that using our strategy can significantly increase the accuracy of the crowd clustering model, as well as minimize MAE and MSE. Our model can produce competitive results for scene captioning compared to previous approaches.https://peerj.com/articles/cs-1283.pdfPublic health emergenciesCrowd clustering predictionNews text collectionSocial problem managementScene captioning |
| spellingShingle | Xiaoling Zhou Guiping Zhu A crowd clustering prediction and captioning technique for public health emergencies PeerJ Computer Science Public health emergencies Crowd clustering prediction News text collection Social problem management Scene captioning |
| title | A crowd clustering prediction and captioning technique for public health emergencies |
| title_full | A crowd clustering prediction and captioning technique for public health emergencies |
| title_fullStr | A crowd clustering prediction and captioning technique for public health emergencies |
| title_full_unstemmed | A crowd clustering prediction and captioning technique for public health emergencies |
| title_short | A crowd clustering prediction and captioning technique for public health emergencies |
| title_sort | crowd clustering prediction and captioning technique for public health emergencies |
| topic | Public health emergencies Crowd clustering prediction News text collection Social problem management Scene captioning |
| url | https://peerj.com/articles/cs-1283.pdf |
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