It is an important way to improve expressway monitoring management level to realize continuous real-time tracking and monitoring of abnormal driving vehicles such as overspeed, long time and low speed occupation of overtaking lane, continuous lane change, and dangerous goods transportation vehicles under different cameras. This article through studies the expressway ray regard convergence across monitoring area joint tracking technology, build the camera comprehensive control, camera control balance compensation and the monitoring area based on target motion continuity principle joint track model, such as through the accurate control of radar to detect vehicle more monitor cameras, solves the continuous tracking target vehicle monitoring technical problems. It has been successfully applied in Yanchong expressway, providing more convenient monitoring services for the expressway management department, and providing strong support for the decision-making of the expressway management department, which has high application value.
{"title":"Research on vehicle tracking technology in expressway cross-monitoring area based on radar and video fusion","authors":"Jianzhen Liu, B. Feng","doi":"10.1117/12.2667372","DOIUrl":"https://doi.org/10.1117/12.2667372","url":null,"abstract":"It is an important way to improve expressway monitoring management level to realize continuous real-time tracking and monitoring of abnormal driving vehicles such as overspeed, long time and low speed occupation of overtaking lane, continuous lane change, and dangerous goods transportation vehicles under different cameras. This article through studies the expressway ray regard convergence across monitoring area joint tracking technology, build the camera comprehensive control, camera control balance compensation and the monitoring area based on target motion continuity principle joint track model, such as through the accurate control of radar to detect vehicle more monitor cameras, solves the continuous tracking target vehicle monitoring technical problems. It has been successfully applied in Yanchong expressway, providing more convenient monitoring services for the expressway management department, and providing strong support for the decision-making of the expressway management department, which has high application value.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127197639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The security of government and enterprise data sharing is very important and critical. To increase the number of data sharing, create a more stable transmission and processing environment, and reduce network threats, this paper studies the security sharing method of government and enterprise data based on differential privacy protection. Firstly, the government and enterprise data encryption are described, and the Tendermint differential overlapping interactive sharing nodes are deployed in the preset area. Then, based on this, the interactive differential privacy protection data sharing model is designed, and the threat identification method is used to achieve data security sharing. The experimental results show that compared with the traditional proxy encryption data security sharing test group and the traditional CP-ABE data security sharing test group, the differential privacy protection sharing test group designed in this paper achieves relatively more times of one-way data security sharing, which indicates that the proposed method has a small error and fast speed in the actual data transmission process. The data processing in the region has less restrictive conditions and has practical application value.
{"title":"Government and enterprise data security sharing method based on differential privacy protection","authors":"Xiaomin Xu, Zhenglei Zhu, Jiange Liu, Xin Liu, Qingxuan Guo","doi":"10.1117/12.2668452","DOIUrl":"https://doi.org/10.1117/12.2668452","url":null,"abstract":"The security of government and enterprise data sharing is very important and critical. To increase the number of data sharing, create a more stable transmission and processing environment, and reduce network threats, this paper studies the security sharing method of government and enterprise data based on differential privacy protection. Firstly, the government and enterprise data encryption are described, and the Tendermint differential overlapping interactive sharing nodes are deployed in the preset area. Then, based on this, the interactive differential privacy protection data sharing model is designed, and the threat identification method is used to achieve data security sharing. The experimental results show that compared with the traditional proxy encryption data security sharing test group and the traditional CP-ABE data security sharing test group, the differential privacy protection sharing test group designed in this paper achieves relatively more times of one-way data security sharing, which indicates that the proposed method has a small error and fast speed in the actual data transmission process. The data processing in the region has less restrictive conditions and has practical application value.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124852588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an approach for a company to determine the choice of electric stations for its respective electric vehicles so that it would minimize its cost on this process. This approach can not only be applied in this problem but also can be utilized for other scenarios. The core of this method is using integer linear programming to represent “to choose” or “not to choose”. The result will give the corresponding value so that we could identify the orientation for each car. In the thesis, we abstract the problem into dealing with 5 cars going to 5 stations among 7 stations. One car will go to one of the 7 stations and no more than one car can go to the same station. The input data is achieved by calculating the distance from each station to each car. Programming is embodied in investigation to solve the integer linear programming optimization. The chosen region is formulated into a coordinate. The cost is in proportional to distance between cars and stations, so a cost function is demonstrated. Finally, the formula of cost is the product of a matrix and an unknown matrix. In order to minimize the cost, this unknown matrix which represent the choice for each car can be solved. After getting the result, the situation that one station will have different capacity, which will allow people to have more option available will be analyzed. Further evaluation of this type of problem will be discussed to analyze why the outcome of the program will all be zero and one.
{"title":"Determining efficient placement of electric vehicles charging stations using integer linear programming","authors":"Yuan Ma, Guheng Pan, Jiong Xu","doi":"10.1117/12.2669163","DOIUrl":"https://doi.org/10.1117/12.2669163","url":null,"abstract":"This paper proposes an approach for a company to determine the choice of electric stations for its respective electric vehicles so that it would minimize its cost on this process. This approach can not only be applied in this problem but also can be utilized for other scenarios. The core of this method is using integer linear programming to represent “to choose” or “not to choose”. The result will give the corresponding value so that we could identify the orientation for each car. In the thesis, we abstract the problem into dealing with 5 cars going to 5 stations among 7 stations. One car will go to one of the 7 stations and no more than one car can go to the same station. The input data is achieved by calculating the distance from each station to each car. Programming is embodied in investigation to solve the integer linear programming optimization. The chosen region is formulated into a coordinate. The cost is in proportional to distance between cars and stations, so a cost function is demonstrated. Finally, the formula of cost is the product of a matrix and an unknown matrix. In order to minimize the cost, this unknown matrix which represent the choice for each car can be solved. After getting the result, the situation that one station will have different capacity, which will allow people to have more option available will be analyzed. Further evaluation of this type of problem will be discussed to analyze why the outcome of the program will all be zero and one.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"19 811 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122661140","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}
Cracks in the rails will lead to great safety hazards in railway transportation. Aiming at the problems of low detection accuracy and inconspicuous part of cracks in crack detection, an improved model based on YOLOX-Nano is proposed. The SA-NET lightweight combined attention mechanism is added to the model to generate a feature map with channel attention and spatial attention, which strengthens the model's attention to target features and location information. Secondly, use Alpha-CIoU Loss to replace IoU Loss to increase the accuracy of the model's prediction box. The comparison experiment was carried out on the self-built data set, and the mAP of the improved YOLOX-Nano model reached 77.58%, the detection speed reached 42.2FPS, and the calculation amount and parameter amount of the model were only 0.508G and 3.5MB respectively, and the overall performance was better than other models.
{"title":"Real-time detection of railway cracks based on improved YOLOX-Nano","authors":"Chong Du, X. Zao, Xiaoliang Wu","doi":"10.1117/12.2667626","DOIUrl":"https://doi.org/10.1117/12.2667626","url":null,"abstract":"Cracks in the rails will lead to great safety hazards in railway transportation. Aiming at the problems of low detection accuracy and inconspicuous part of cracks in crack detection, an improved model based on YOLOX-Nano is proposed. The SA-NET lightweight combined attention mechanism is added to the model to generate a feature map with channel attention and spatial attention, which strengthens the model's attention to target features and location information. Secondly, use Alpha-CIoU Loss to replace IoU Loss to increase the accuracy of the model's prediction box. The comparison experiment was carried out on the self-built data set, and the mAP of the improved YOLOX-Nano model reached 77.58%, the detection speed reached 42.2FPS, and the calculation amount and parameter amount of the model were only 0.508G and 3.5MB respectively, and the overall performance was better than other models.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537925","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}
In this paper, monthly frequency multi-factor data on valuation, momentum, turnover rate and technology of A-share listed companies in Shanghai and Shenzhen markets from January 2012 to July 2022 are selected and input to LSTM and LSTM model with fused attention mechanism respectively for training after data pre-processing. The sector-neutral layered-portfolios and the sector-neutral stock selection portfolios were constructed based on the model output, respectively. In the model evaluation section, it is confirmed that the Attention-LSTM model outperforms the LSTM model in predicting stock ups and downs. The single-factor layered back test under monthly position adjustment and stock selection strategy back test confirmed that the Attention-LSTM model significantly outperformed the LSTM model in terms of annualized return, sharpe ratio, and maximum retracement, and also significantly outperformed the CSI 300 and CSI 500.
{"title":"Research on LSTM multi-factor quantitative stock selection strategy based on attention mechanism","authors":"Zezhong Li","doi":"10.1117/12.2668783","DOIUrl":"https://doi.org/10.1117/12.2668783","url":null,"abstract":"In this paper, monthly frequency multi-factor data on valuation, momentum, turnover rate and technology of A-share listed companies in Shanghai and Shenzhen markets from January 2012 to July 2022 are selected and input to LSTM and LSTM model with fused attention mechanism respectively for training after data pre-processing. The sector-neutral layered-portfolios and the sector-neutral stock selection portfolios were constructed based on the model output, respectively. In the model evaluation section, it is confirmed that the Attention-LSTM model outperforms the LSTM model in predicting stock ups and downs. The single-factor layered back test under monthly position adjustment and stock selection strategy back test confirmed that the Attention-LSTM model significantly outperformed the LSTM model in terms of annualized return, sharpe ratio, and maximum retracement, and also significantly outperformed the CSI 300 and CSI 500.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112397","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}
Existing deep learning-based Super Resolution (SR) reconstruction algorithms achieve remarkable performance on images with known degradation. Most of the degradation models exists problems in self-adaptations when facing with the deviation of the degradation model of the image of the real scene, and the effect is not good. Therefore, this paper proposes a blind image super-resolution reconstruction algorithm based on dual regression, which aims to solve the problem of poor performance of super-resolution networks in real scenes. Firstly, the closed-loop network is used to constrain the mapping space, and the optimal reconstruction function is found to improve the network reconstruction performance. Secondly, the attention mechanism is adopted into the residual block of feature extraction to expand the receptive field of the feature map, improve the reuse of features, and strengthen the reconstruction of high-frequency information. Finally, the frequency-domain blur kernel map estimates the down sampling kernel and reconstructs the low-resolution image, adaptively extracts the feature expression, enhances the ability to restore texture details, and reconstructs the real-world image better.
{"title":"Blind image super-resolution reconstruction based on dual regression network","authors":"Hongpeng Tian, ShengZhou Jiang","doi":"10.1117/12.2667901","DOIUrl":"https://doi.org/10.1117/12.2667901","url":null,"abstract":"Existing deep learning-based Super Resolution (SR) reconstruction algorithms achieve remarkable performance on images with known degradation. Most of the degradation models exists problems in self-adaptations when facing with the deviation of the degradation model of the image of the real scene, and the effect is not good. Therefore, this paper proposes a blind image super-resolution reconstruction algorithm based on dual regression, which aims to solve the problem of poor performance of super-resolution networks in real scenes. Firstly, the closed-loop network is used to constrain the mapping space, and the optimal reconstruction function is found to improve the network reconstruction performance. Secondly, the attention mechanism is adopted into the residual block of feature extraction to expand the receptive field of the feature map, improve the reuse of features, and strengthen the reconstruction of high-frequency information. Finally, the frequency-domain blur kernel map estimates the down sampling kernel and reconstructs the low-resolution image, adaptively extracts the feature expression, enhances the ability to restore texture details, and reconstructs the real-world image better.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130601094","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}
Wen Chen, Qianzhou Cai, Jin Hou, Jindong Zhang, Bochuan Zheng
Wild animal recognition is important for wild animal protection. Because the number of different wild animals is different in the wild. The wild animal image dataset collected in field by using camera trap is a typical long tail dataset. This paper proposes an Effective-Class-Balanced Softmax Loss (ECBSL) to solve the long tail problem of self-built wild animal dataset. Firstly, a new cross entropy loss function is obtained by using pointwise mutual information instead of conditional probability for modeling. Then the improved effective number of samples calculation method is used to approximately calculate the prior probability distribution of different animal species. Finally, the effectiveness of ECBSL is proved by experiments. Experiments on the self-built wild animal dataset show that the proposed method improves the recognition accuracy of the tail classes and the whole dataset. The comparison experiments with other methods show that the proposed method is superior to other methods.
野生动物识别对野生动物保护具有重要意义。因为不同野生动物的数量在野外是不同的。利用相机陷阱在野外采集的野生动物图像数据集是典型的长尾数据集。针对自建野生动物数据集的长尾问题，提出了一种有效类平衡的Softmax Loss (ECBSL)算法。首先，利用点互信息代替条件概率进行建模，得到新的交叉熵损失函数;然后采用改进的有效样本数计算方法，近似计算不同动物物种的先验概率分布。最后，通过实验验证了ECBSL的有效性。在自建野生动物数据集上的实验表明，该方法提高了尾类和整个数据集的识别精度。与其他方法的对比实验表明，该方法优于其他方法。
{"title":"Wild animal recognition based on effective-class-balanced softmax loss","authors":"Wen Chen, Qianzhou Cai, Jin Hou, Jindong Zhang, Bochuan Zheng","doi":"10.1117/12.2667361","DOIUrl":"https://doi.org/10.1117/12.2667361","url":null,"abstract":"Wild animal recognition is important for wild animal protection. Because the number of different wild animals is different in the wild. The wild animal image dataset collected in field by using camera trap is a typical long tail dataset. This paper proposes an Effective-Class-Balanced Softmax Loss (ECBSL) to solve the long tail problem of self-built wild animal dataset. Firstly, a new cross entropy loss function is obtained by using pointwise mutual information instead of conditional probability for modeling. Then the improved effective number of samples calculation method is used to approximately calculate the prior probability distribution of different animal species. Finally, the effectiveness of ECBSL is proved by experiments. Experiments on the self-built wild animal dataset show that the proposed method improves the recognition accuracy of the tail classes and the whole dataset. The comparison experiments with other methods show that the proposed method is superior to other methods.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"14 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120864916","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}
Taxi destination prediction can grasp the flow direction of the taxi, facilitate the taxi dispatches. There has always been a long-term dependency problem in taxi trajectory prediction. Although LSTM can solve the long-term dependency problem to a certain extent, it does not have a good ability to deal with the deep correlation between long trajectory sequences. To address the above problem, we propose a taxi destination prediction method based on LSTM with Tree Memory Module (TMM-LSTM). TMM-LSTM stores the state of the input trajectory through an external memory structure. It uses a tree structure to process more historical information and better deal with the long-term relationship between trajectory points. TMM-LSTM can better solve the long-term dependency problem in the taxi trajectory sequence. Experiments demonstrate that the average error distance is 6% lower than traditional LSTM model.
{"title":"Taxi destination prediction based on LSTM with tree memory module","authors":"Dan Song, Yadong Li, Meng-Yun Zhang, Ting Zhang","doi":"10.1117/12.2667488","DOIUrl":"https://doi.org/10.1117/12.2667488","url":null,"abstract":"Taxi destination prediction can grasp the flow direction of the taxi, facilitate the taxi dispatches. There has always been a long-term dependency problem in taxi trajectory prediction. Although LSTM can solve the long-term dependency problem to a certain extent, it does not have a good ability to deal with the deep correlation between long trajectory sequences. To address the above problem, we propose a taxi destination prediction method based on LSTM with Tree Memory Module (TMM-LSTM). TMM-LSTM stores the state of the input trajectory through an external memory structure. It uses a tree structure to process more historical information and better deal with the long-term relationship between trajectory points. TMM-LSTM can better solve the long-term dependency problem in the taxi trajectory sequence. Experiments demonstrate that the average error distance is 6% lower than traditional LSTM model.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123093476","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}
Cyber Mimic Defense (CMD) is a new generation of active defense technology after firewall, intrusion and other traditional defense technology. It aims to deal with uncertain threats in the network environment with visual uncertainty. This paper briefly introduces the architecture and principle of CMD and defines four main elements of the current simulation platform: system architecture, heterogeneous policy, scheduling policy and voting policy. Combined with examples, the four elements are respectively summarized. The system architecture is divided into C mode and D mode, and the heterogeneous strategy includes implementation mode, implementation method and synchronization mode. Scheduling policies are classified into offline policies and online policies. Voting policies include voting algorithms, voting levels, and delay control.
{"title":"Research on the main elements of mimic platforms","authors":"Bo Zhang, Zesheng Xi, Yu-Na Wang, Chuan He","doi":"10.1117/12.2667434","DOIUrl":"https://doi.org/10.1117/12.2667434","url":null,"abstract":"Cyber Mimic Defense (CMD) is a new generation of active defense technology after firewall, intrusion and other traditional defense technology. It aims to deal with uncertain threats in the network environment with visual uncertainty. This paper briefly introduces the architecture and principle of CMD and defines four main elements of the current simulation platform: system architecture, heterogeneous policy, scheduling policy and voting policy. Combined with examples, the four elements are respectively summarized. The system architecture is divided into C mode and D mode, and the heterogeneous strategy includes implementation mode, implementation method and synchronization mode. Scheduling policies are classified into offline policies and online policies. Voting policies include voting algorithms, voting levels, and delay control.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115077599","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}
To address the current problem of single-site prediction and inadequate extraction of spatial features for PM2.5 hourly concentration prediction, a graphical convolutional neural network (GCN) is proposed to obtain the spatial correlation between PM2.5 monitoring stations in Beijing by considering the features of time series in time and space, and assign weights according to the distance between stations to abstract into an undirected topological map. The missing data sequences are complemented by using a long and short-term memory network to extract temporal features on the time-series dataset, which are normalized and then fused with the components extracted by the GCN to make predictions. The experimental results show that GCN-BiLSTM has higher prediction accuracy and better results than single RNN, LSTM, and BiLSTM algorithms.
{"title":"Multi-site air quality prediction based on graph convolutional neural network-bi-directional LSTM model","authors":"Lalao Gao, MingChao Liao, Di Zhang","doi":"10.1117/12.2667705","DOIUrl":"https://doi.org/10.1117/12.2667705","url":null,"abstract":"To address the current problem of single-site prediction and inadequate extraction of spatial features for PM2.5 hourly concentration prediction, a graphical convolutional neural network (GCN) is proposed to obtain the spatial correlation between PM2.5 monitoring stations in Beijing by considering the features of time series in time and space, and assign weights according to the distance between stations to abstract into an undirected topological map. The missing data sequences are complemented by using a long and short-term memory network to extract temporal features on the time-series dataset, which are normalized and then fused with the components extracted by the GCN to make predictions. The experimental results show that GCN-BiLSTM has higher prediction accuracy and better results than single RNN, LSTM, and BiLSTM algorithms.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125740697","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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