In the field of computer vision, deep learning has developed tremendously, large-scale preforming has received increasing attention from experts and researchers. Different training models often have large performance gaps in training speed and accuracy when performing large-scale pre-training. In this case, choosing the appropriate model for large-scale pre-training is particularly important. This experiment uses the same image data set and the same hardware conditions to construct the image classification model respectively in the three mainstream image recognition large-scale pre-training models, Vision Transformer (VIT), Swin-Transformer and ConvNeXt, try to analyze the advantages and disadvantages of each model by experimental results. It is observed that Vision Transformer has the fastest running speed in computer vision classification experiments, but its accuracy is not as good as the other two models, Swin-Transformer has the slowest speed and average accuracy, ConvNeXt has the highest accuracy, but its speed is mediocre. The results of this experiment have some reference significance for future model selection for large-scale pre-training tasks in computer vision, this can decrease training time and improve training accuracy to some extent.
{"title":"Comparison of large-scale pre-trained models based ViT, swin transformer and ConvNeXt","authors":"Jiapeng Yu","doi":"10.1117/12.2671201","DOIUrl":"https://doi.org/10.1117/12.2671201","url":null,"abstract":"In the field of computer vision, deep learning has developed tremendously, large-scale preforming has received increasing attention from experts and researchers. Different training models often have large performance gaps in training speed and accuracy when performing large-scale pre-training. In this case, choosing the appropriate model for large-scale pre-training is particularly important. This experiment uses the same image data set and the same hardware conditions to construct the image classification model respectively in the three mainstream image recognition large-scale pre-training models, Vision Transformer (VIT), Swin-Transformer and ConvNeXt, try to analyze the advantages and disadvantages of each model by experimental results. It is observed that Vision Transformer has the fastest running speed in computer vision classification experiments, but its accuracy is not as good as the other two models, Swin-Transformer has the slowest speed and average accuracy, ConvNeXt has the highest accuracy, but its speed is mediocre. The results of this experiment have some reference significance for future model selection for large-scale pre-training tasks in computer vision, this can decrease training time and improve training accuracy to some extent.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"12610 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130515098","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}
It is not uncommon for bridges to be damaged by earthquakes. As an important throat in the transportation network, earthquakes not only cause the loss of bridges themselves, but also cause a series of losses of the transportation network. In the design of bridge seismic isolation, the seismic isolation device is used to isolate the structure from the frequency band where the seismic energy is concentrated and reduce the seismic response of the structure by prolonging the period and increasing the damping. Compared with advanced countries in seismic research, there is a big gap in the research of mechanical model and parameters of seismic isolation devices in China's bridge seismic code. Based on the analysis of the advantages and disadvantages of the finite element sensitivity method and its application limitations, a sequential recurrence method for support optimization is proposed. The results of an example show that the sequential recurrence method has the advantages of strong adaptability and unconditional convergence. On the basis of the sequence recurrence method, the modified sequence recurrence method is further proposed. This method can take into account the influence of the pier inertia force and is applicable to a variety of support forms. After the genetic algorithm optimization calculation, the difference of the bending moment at the bottom of each pier can be controlled within, which solves the problem of large difference of the bending moment at the bottom of the pier under the longitudinal earthquake of irregular bridges.
{"title":"Parameter optimization design of isolated bridge based on improved genetic algorithm","authors":"Bin Huang","doi":"10.1117/12.2671670","DOIUrl":"https://doi.org/10.1117/12.2671670","url":null,"abstract":"It is not uncommon for bridges to be damaged by earthquakes. As an important throat in the transportation network, earthquakes not only cause the loss of bridges themselves, but also cause a series of losses of the transportation network. In the design of bridge seismic isolation, the seismic isolation device is used to isolate the structure from the frequency band where the seismic energy is concentrated and reduce the seismic response of the structure by prolonging the period and increasing the damping. Compared with advanced countries in seismic research, there is a big gap in the research of mechanical model and parameters of seismic isolation devices in China's bridge seismic code. Based on the analysis of the advantages and disadvantages of the finite element sensitivity method and its application limitations, a sequential recurrence method for support optimization is proposed. The results of an example show that the sequential recurrence method has the advantages of strong adaptability and unconditional convergence. On the basis of the sequence recurrence method, the modified sequence recurrence method is further proposed. This method can take into account the influence of the pier inertia force and is applicable to a variety of support forms. After the genetic algorithm optimization calculation, the difference of the bending moment at the bottom of each pier can be controlled within, which solves the problem of large difference of the bending moment at the bottom of the pier under the longitudinal earthquake of irregular bridges.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132842985","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}
Bingchen Zhang, Wuhan Yu, Yang Lü, Zhifang Yang, Juan Yu, X. Fang, Lihua Chen
The morphological changes of the hippocampus in brain Magnetic Resonance Imaging (MRI) images are of great significance for the early screening of Alzheimer's disease. Currently, in clinical practice, the diagnosis of the hippocampus is achieved manually by doctors with experience. Because the hippocampus has the characteristics of small size, complex shape, and indistinct boundary with surrounding structures, manual segmentation, and grading of the hippocampus in brain MRI is time-consuming and labor-intensive, which is susceptible to errors because of human subjective judgment. To address that, this paper proposes a hippocampal MRI diagnosis algorithm based on Faster R-CNN and Mask R-CNN. The main contributions are 1) automatic identification of hippocampus in brain MRI by Faster R-CNN neural network, 2) precisely segmenting the hippocampus and judging the atrophy level through Mask R-CNN. Case studies are performed on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and the medical records of the First Affiliated Hospital of Chongqing Medical University. Results indicate that the proposed method achieves a good segmentation effect on the hippocampus in the coronal MRI image of the brain and accurately grades the level of hippocampal atrophy, which can better assist doctors in diagnosing Alzheimer's disease.
{"title":"Hippocampus MRI diagnosis based on deep learning in application of preliminary screening of Alzheimer’s disease","authors":"Bingchen Zhang, Wuhan Yu, Yang Lü, Zhifang Yang, Juan Yu, X. Fang, Lihua Chen","doi":"10.1117/12.2671572","DOIUrl":"https://doi.org/10.1117/12.2671572","url":null,"abstract":"The morphological changes of the hippocampus in brain Magnetic Resonance Imaging (MRI) images are of great significance for the early screening of Alzheimer's disease. Currently, in clinical practice, the diagnosis of the hippocampus is achieved manually by doctors with experience. Because the hippocampus has the characteristics of small size, complex shape, and indistinct boundary with surrounding structures, manual segmentation, and grading of the hippocampus in brain MRI is time-consuming and labor-intensive, which is susceptible to errors because of human subjective judgment. To address that, this paper proposes a hippocampal MRI diagnosis algorithm based on Faster R-CNN and Mask R-CNN. The main contributions are 1) automatic identification of hippocampus in brain MRI by Faster R-CNN neural network, 2) precisely segmenting the hippocampus and judging the atrophy level through Mask R-CNN. Case studies are performed on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and the medical records of the First Affiliated Hospital of Chongqing Medical University. Results indicate that the proposed method achieves a good segmentation effect on the hippocampus in the coronal MRI image of the brain and accurately grades the level of hippocampal atrophy, which can better assist doctors in diagnosing Alzheimer's disease.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"1996 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113966405","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, three convolutional neural network models are used to achieve end-to-end recognition of blood cell images. The network model parameters are initialized by transfer learning from the pre-trained model on ImageNet, and then the blood cell images are input into the model, and the network model training is completed by back-propagation to continuously update the parameters. For small-scale datasets, the number of blood cell images is expanded using data increments to improve the generalization ability of the model. Experimental results on the BCCD dataset show that the best result MobileNetV2 achieves an accuracy and precision of 0.894 and 0.916, respectively.
{"title":"Multi-classification recognition of blood cell images based on transfer learning","authors":"Shuokun Yang, Fucheng You, D. Sun","doi":"10.1117/12.2671147","DOIUrl":"https://doi.org/10.1117/12.2671147","url":null,"abstract":"In this paper, three convolutional neural network models are used to achieve end-to-end recognition of blood cell images. The network model parameters are initialized by transfer learning from the pre-trained model on ImageNet, and then the blood cell images are input into the model, and the network model training is completed by back-propagation to continuously update the parameters. For small-scale datasets, the number of blood cell images is expanded using data increments to improve the generalization ability of the model. Experimental results on the BCCD dataset show that the best result MobileNetV2 achieves an accuracy and precision of 0.894 and 0.916, respectively.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114828960","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}
Aiming at the problem of low model recognition accuracy caused by high similarity and mutual occlusion between crops and weeds in Unmanned Aerial Vehicle (UAV) images, a pixel-level weed recognition method based on improved HRNet-OCRNet is proposed. In this method, a multi-stage and multi-scale feature fusion method is added to HRNet to preserve more details and enhance semantic information at different levels, to solve the problem of high similarity between crops and weeds. The spatial self-attention module of Polarized Self-Attention (PSA) is integrated to HRNet, enhance the network's learning of important features, and reduce the false identification caused by mutual occlusion of crops and weeds. The expansion prediction method is used to generate an accurate distribution map of crop weeds. Compared with Deeplabv3+, GCNet and K-Net, the experimental results show that the proposed method has higher recognition accuracy for crop weeds, and mean intersection over union (mIoU) reaches 85.76%.
{"title":"Crop weed image recognition of UAV based on improved HRNet-OCRNet","authors":"Yong Yang, Jing Ma, Fuheng Qu, Tianyu Ding, Haoji Shan","doi":"10.1117/12.2671254","DOIUrl":"https://doi.org/10.1117/12.2671254","url":null,"abstract":"Aiming at the problem of low model recognition accuracy caused by high similarity and mutual occlusion between crops and weeds in Unmanned Aerial Vehicle (UAV) images, a pixel-level weed recognition method based on improved HRNet-OCRNet is proposed. In this method, a multi-stage and multi-scale feature fusion method is added to HRNet to preserve more details and enhance semantic information at different levels, to solve the problem of high similarity between crops and weeds. The spatial self-attention module of Polarized Self-Attention (PSA) is integrated to HRNet, enhance the network's learning of important features, and reduce the false identification caused by mutual occlusion of crops and weeds. The expansion prediction method is used to generate an accurate distribution map of crop weeds. Compared with Deeplabv3+, GCNet and K-Net, the experimental results show that the proposed method has higher recognition accuracy for crop weeds, and mean intersection over union (mIoU) reaches 85.76%.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"248 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133659828","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 order to build an aeroengine on-board model with full envelope, full state, high accuracy and high real-time, a modeling method based on flight data is proposed. This method builds state variable model based on component level model. Considering the influence of Reynolds number, power extraction, air bleed and other factors, the steady state model of the on-board model is modified based on regression analysis using flight data to reduce the modeling error caused by individual engine differences. At the same time, in order to compensate the residual steady-state error, a steady-state error model based on Gaussian Mixture Model Neural Network (GMM-NN) is established. Considering the need to reconstruct the speed sensor, the speed signal cannot be used as the scheduling variable to build a new scheduling variable, which has less dynamic error compared to taking fuel as the scheduling variable. Compared with the traditional model, the input of this model is only control variables and flight conditions, and it can reconstruct the signals of speed, pressure, temperature and other sensors. At the same time, it has the advantages of simple structure, no iterative calculation and high accuracy. Compared with flight data, the maximum dynamic error of compressor outlet total pressure of the new scheduling variable model is 3.564%, which is 4.13 times higher than the maximum relative error of 14.735% of the fuel scheduling model. In the verification of multi flight data, the average errors of LP rotor speed, HP rotor speed, compressor outlet total pressure and LP turbine outlet total temperature are 0.52%, 0.39%, 0.53% and 0.9% respectively, meeting the accuracy requirements of the project.
{"title":"Research on modeling method of aeroengine on-board model based on flight data","authors":"Cheng Chen, Qiangang Zheng, Haibo Zhang","doi":"10.1117/12.2672792","DOIUrl":"https://doi.org/10.1117/12.2672792","url":null,"abstract":"In order to build an aeroengine on-board model with full envelope, full state, high accuracy and high real-time, a modeling method based on flight data is proposed. This method builds state variable model based on component level model. Considering the influence of Reynolds number, power extraction, air bleed and other factors, the steady state model of the on-board model is modified based on regression analysis using flight data to reduce the modeling error caused by individual engine differences. At the same time, in order to compensate the residual steady-state error, a steady-state error model based on Gaussian Mixture Model Neural Network (GMM-NN) is established. Considering the need to reconstruct the speed sensor, the speed signal cannot be used as the scheduling variable to build a new scheduling variable, which has less dynamic error compared to taking fuel as the scheduling variable. Compared with the traditional model, the input of this model is only control variables and flight conditions, and it can reconstruct the signals of speed, pressure, temperature and other sensors. At the same time, it has the advantages of simple structure, no iterative calculation and high accuracy. Compared with flight data, the maximum dynamic error of compressor outlet total pressure of the new scheduling variable model is 3.564%, which is 4.13 times higher than the maximum relative error of 14.735% of the fuel scheduling model. In the verification of multi flight data, the average errors of LP rotor speed, HP rotor speed, compressor outlet total pressure and LP turbine outlet total temperature are 0.52%, 0.39%, 0.53% and 0.9% respectively, meeting the accuracy requirements of the project.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808161","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}
Q. Zhang, Yongzhi Zhu, Chuanhao Lan, Qinghang Mao, Yikai Cui
In the era of information explosion, rumors will cause great harm and affect social stability. Most rumor detection methods concentrate on extracting features from content and consumer information. We propose a brand-new approach to early rumor identification, MSR-GAT. Firstly, the source text and comment text are fused as node features and the relation between events is considered edge information. Then, the graph attention model is constructed to classify nodes and complete rumor detection. The experimental findings demonstrate that the detection algorithm outperforms the baselines algorithm in accuracy, precision, recall and F1-Measure. It can accurately identify rumors.
{"title":"Weibo rumor detection based on GCN","authors":"Q. Zhang, Yongzhi Zhu, Chuanhao Lan, Qinghang Mao, Yikai Cui","doi":"10.1117/12.2671057","DOIUrl":"https://doi.org/10.1117/12.2671057","url":null,"abstract":"In the era of information explosion, rumors will cause great harm and affect social stability. Most rumor detection methods concentrate on extracting features from content and consumer information. We propose a brand-new approach to early rumor identification, MSR-GAT. Firstly, the source text and comment text are fused as node features and the relation between events is considered edge information. Then, the graph attention model is constructed to classify nodes and complete rumor detection. The experimental findings demonstrate that the detection algorithm outperforms the baselines algorithm in accuracy, precision, recall and F1-Measure. It can accurately identify rumors.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125697391","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 order to improve the control accuracy of aero-engine operation limits, a novel design approach of Min-Max limit protection is proposed. The inverse mapping models of different limits based on On-Line Sliding Window Deep Neural Network (OL SW DNN) are proposed and established firstly. The OL SW DNN models calculate the limit value of fuel flows to ensure that engine satisfies all operation limits. The operation restrictions in the proposed method can vary in different flight conditions. With the application of on-line learning modeling method, the engine can always operate within the given operation limits no matter whether engine degrades or not. Moreover, the OL SW DNN adopts deep learning structure and has strong fitting capacity for the nonlinear object. The comparison simulations of the popular limit protection design method based on optimization method and the proposed one are carried out. Compared with the popular method, the limit line of each operation limits in the proposed method not only has much higher accuracy especially when engine appears degradation, but also can be continuous change.
{"title":"A new min-max limit protection design method for aero-engine based on inverse mapping","authors":"Zhengchen Zhu, Qiangang Zheng, Shubo Zhang","doi":"10.1117/12.2671052","DOIUrl":"https://doi.org/10.1117/12.2671052","url":null,"abstract":"In order to improve the control accuracy of aero-engine operation limits, a novel design approach of Min-Max limit protection is proposed. The inverse mapping models of different limits based on On-Line Sliding Window Deep Neural Network (OL SW DNN) are proposed and established firstly. The OL SW DNN models calculate the limit value of fuel flows to ensure that engine satisfies all operation limits. The operation restrictions in the proposed method can vary in different flight conditions. With the application of on-line learning modeling method, the engine can always operate within the given operation limits no matter whether engine degrades or not. Moreover, the OL SW DNN adopts deep learning structure and has strong fitting capacity for the nonlinear object. The comparison simulations of the popular limit protection design method based on optimization method and the proposed one are carried out. Compared with the popular method, the limit line of each operation limits in the proposed method not only has much higher accuracy especially when engine appears degradation, but also can be continuous change.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072322","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 problems of wide range of strip steel surface defect size variation, slow detection efficiency, low detection accuracy, and difficulty of mobile-side model deployment, an improved YOLOv4 algorithm model is proposed in this paper. Firstly, in order to improve the robustness of the model, data augmentation is applied to the dataset. Secondly, in order to improve the matching between the a priori frame and the feature map, the K-means++ algorithm with faster convergence and better results is used instead of the K-means algorithm in the original YOLO algorithm for the design of the a priori frame. Finally, CSPDarknet is specifically replaced for the Ghostnet to enhance the backbone network's ability to extract defective features. The experimental results show that the improved YOLOv4 algorithm achieves 87.9% mAP on the publicly available NEU-DET dataset, which is 2.4% lower than the original YOLOv4 algorithm. However, the number of parameters of the model decreases by 80% compared with the original YOLOv4, and the detection speed is around 44 FPS, which can not only meet the needs of industrial production, but also meet the requirements of deploying the model to mobile.
{"title":"Research on defect detection algorithm of strip steel based on improved YOLOv4","authors":"Sun Qiang, Sheng Bo","doi":"10.1117/12.2671202","DOIUrl":"https://doi.org/10.1117/12.2671202","url":null,"abstract":"To address the current problems of wide range of strip steel surface defect size variation, slow detection efficiency, low detection accuracy, and difficulty of mobile-side model deployment, an improved YOLOv4 algorithm model is proposed in this paper. Firstly, in order to improve the robustness of the model, data augmentation is applied to the dataset. Secondly, in order to improve the matching between the a priori frame and the feature map, the K-means++ algorithm with faster convergence and better results is used instead of the K-means algorithm in the original YOLO algorithm for the design of the a priori frame. Finally, CSPDarknet is specifically replaced for the Ghostnet to enhance the backbone network's ability to extract defective features. The experimental results show that the improved YOLOv4 algorithm achieves 87.9% mAP on the publicly available NEU-DET dataset, which is 2.4% lower than the original YOLOv4 algorithm. However, the number of parameters of the model decreases by 80% compared with the original YOLOv4, and the detection speed is around 44 FPS, which can not only meet the needs of industrial production, but also meet the requirements of deploying the model to mobile.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123862631","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}
As economic globalization advances, financial market is increasingly favored by investors. With the development and strong demand of financial market, the forecast of stock price trend has aroused widespread attentions from both the academic and industry. As is well known, stock investment has both high returns and high risks. However, it is difficult to quantify the internal and external factors that affect stock market fluctuations, and it is also difficult to process massive and complex stock data. Therefore, traditional non-artificial intelligence approaches are not always satisfactory in forecasting stock price. Therefore, it has great significance to use big data technologies to excavate massive useful information hidden in stocks as well as to use neural network technology such as LSTM to further solve the problem of stock price trend forecast. In the paper, we report a development and implementation of deep learning-based stock opening price forecasting system based.
{"title":"Research and implementation of deep-learning-based stock opening price forecasting system","authors":"S. Mali","doi":"10.1117/12.2671287","DOIUrl":"https://doi.org/10.1117/12.2671287","url":null,"abstract":"As economic globalization advances, financial market is increasingly favored by investors. With the development and strong demand of financial market, the forecast of stock price trend has aroused widespread attentions from both the academic and industry. As is well known, stock investment has both high returns and high risks. However, it is difficult to quantify the internal and external factors that affect stock market fluctuations, and it is also difficult to process massive and complex stock data. Therefore, traditional non-artificial intelligence approaches are not always satisfactory in forecasting stock price. Therefore, it has great significance to use big data technologies to excavate massive useful information hidden in stocks as well as to use neural network technology such as LSTM to further solve the problem of stock price trend forecast. In the paper, we report a development and implementation of deep learning-based stock opening price forecasting system based.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124913813","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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