Pub Date : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137969
Guilu Wang
Fatigue driving detection based on YOLOV5 object detection algorithm. YOLOV5N with fewer parameters is selected as the basic model, and the large object detection layer in YOLOV5N is removed according to the object size clustering results, which reduces the parameters and improves the detection results. SAM is introduced to improve the ability of the backbone network to extract key features, and the convolution kernel in SAM is expanded to provide a wider receptive field for the model, in exchange for better detection results with a small increase in parameters. Referring to BiFPN, the Neck part of YOLOV5N is modified to provide more diverse fusion methods for multi-scale features. The precision, recall and mAP of the improved model are higher than those of YOLOV5N.
{"title":"Fatigue Driving Detection Based on Improved YOLOV5","authors":"Guilu Wang","doi":"10.1109/ACAIT56212.2022.10137969","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137969","url":null,"abstract":"Fatigue driving detection based on YOLOV5 object detection algorithm. YOLOV5N with fewer parameters is selected as the basic model, and the large object detection layer in YOLOV5N is removed according to the object size clustering results, which reduces the parameters and improves the detection results. SAM is introduced to improve the ability of the backbone network to extract key features, and the convolution kernel in SAM is expanded to provide a wider receptive field for the model, in exchange for better detection results with a small increase in parameters. Referring to BiFPN, the Neck part of YOLOV5N is modified to provide more diverse fusion methods for multi-scale features. The precision, recall and mAP of the improved model are higher than those of YOLOV5N.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121158837","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137868
B. He, Xiao Wang, Lili Zhu
In the internet age, various contents flood people’s internet life, causing information redundancy, so performing more useful information extraction becomes an important task. Among the recommendation algorithms, the most common one is the collaborative filtering algorithm, which has the problem of data sparsity when performing matrix construction due to the poor relationship between users and items, which affects the effectiveness of recommendations. To address the data sparsity problem, the thesis proposes a collaborative filtering recommendation algorithm (KGCF) based on K-Means and GCN, which introduces K-Means and GCN, using the ability of K-Means to aggregate data and the ability of GCN to extract features in non-Euclidean space to obtain the hidden relationships between users and items, and populate the similarity matrix of users and items to alleviate the The paper uses the MovieLens dataset to improve the recommendation performance of traditional collaborative filtering algorithms. The paper uses the MovieLens dataset for comparison experiments, and uses MAE as the evaluation metric. The results show that this paper’s algorithm is better than similar algorithms in solving the sparsity of collaborative filtering data.
{"title":"Collaborative Filtering Recommendation Algorithm Based on K-Means and GCN","authors":"B. He, Xiao Wang, Lili Zhu","doi":"10.1109/ACAIT56212.2022.10137868","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137868","url":null,"abstract":"In the internet age, various contents flood people’s internet life, causing information redundancy, so performing more useful information extraction becomes an important task. Among the recommendation algorithms, the most common one is the collaborative filtering algorithm, which has the problem of data sparsity when performing matrix construction due to the poor relationship between users and items, which affects the effectiveness of recommendations. To address the data sparsity problem, the thesis proposes a collaborative filtering recommendation algorithm (KGCF) based on K-Means and GCN, which introduces K-Means and GCN, using the ability of K-Means to aggregate data and the ability of GCN to extract features in non-Euclidean space to obtain the hidden relationships between users and items, and populate the similarity matrix of users and items to alleviate the The paper uses the MovieLens dataset to improve the recommendation performance of traditional collaborative filtering algorithms. The paper uses the MovieLens dataset for comparison experiments, and uses MAE as the evaluation metric. The results show that this paper’s algorithm is better than similar algorithms in solving the sparsity of collaborative filtering data.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123890996","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137884
Keying Huang, Rui Bai, Jin Ji, Jun Zhao, Wen-ning Yan
As the power system of an aircraft, accurate prediction of the remaining useful life (RUL) of an aero-engine is of great importance to ensure the flight safety of the aircraft. However, existing methods are all data-driven-based, and such methods are extremely demanding in terms of data volume. To address the problem of insufficient engine data, this paper proposes a similarity-based method for predicting the life of small-sample aircraft engines. Firstly, the KPCA method is used to model the engine degradation trajectory, then a simple and effective method is proposed to determine the degradation start moment of each engine, and finally the similarity between each training sample and the test sample is determined based on the trained KPCA model, and then the remaining life of the test sample is estimated. Experiments show that the method proposed in this paper is effective in predicting the remaining life of an engine under the condition of small samples.
{"title":"A Similarity-Based Remaining Useful Life Prediction Method for Aero Engines with Small Smples","authors":"Keying Huang, Rui Bai, Jin Ji, Jun Zhao, Wen-ning Yan","doi":"10.1109/ACAIT56212.2022.10137884","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137884","url":null,"abstract":"As the power system of an aircraft, accurate prediction of the remaining useful life (RUL) of an aero-engine is of great importance to ensure the flight safety of the aircraft. However, existing methods are all data-driven-based, and such methods are extremely demanding in terms of data volume. To address the problem of insufficient engine data, this paper proposes a similarity-based method for predicting the life of small-sample aircraft engines. Firstly, the KPCA method is used to model the engine degradation trajectory, then a simple and effective method is proposed to determine the degradation start moment of each engine, and finally the similarity between each training sample and the test sample is determined based on the trained KPCA model, and then the remaining life of the test sample is estimated. Experiments show that the method proposed in this paper is effective in predicting the remaining life of an engine under the condition of small samples.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123922342","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137970
Li Zhou, Jin Shen, Ting Zhang
In order to further strengthen the control of financial market trends, a financial trend prediction model based on deep belief network (DBN) is proposed to further improve the prediction level of financial trend. Among them, the prediction and classification of financial market trend is realized by introducing Elliott wave theory. The prediction model adopts deep belief network model. Experimental results show that by introducing the Elliott wave theory, the designed financial trend prediction model based on deep belief network can achieve the accurate prediction of financial trend, the prediction precision is 67.5%, and the corresponding mean square error is 0.413. Compared with BP network and MLP network, deep belief network shows better performance on four evaluation indicators, namely ER, MAE, RMSE and MSE, and is more suitable for the design of financial trend prediction model. The above experimental results verify the feasibility and superiority of the financial trend prediction model based on deep belief network proposed in this study, which has certain application value.
{"title":"Financial Trend Prediction Based on Deep Belief Network","authors":"Li Zhou, Jin Shen, Ting Zhang","doi":"10.1109/ACAIT56212.2022.10137970","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137970","url":null,"abstract":"In order to further strengthen the control of financial market trends, a financial trend prediction model based on deep belief network (DBN) is proposed to further improve the prediction level of financial trend. Among them, the prediction and classification of financial market trend is realized by introducing Elliott wave theory. The prediction model adopts deep belief network model. Experimental results show that by introducing the Elliott wave theory, the designed financial trend prediction model based on deep belief network can achieve the accurate prediction of financial trend, the prediction precision is 67.5%, and the corresponding mean square error is 0.413. Compared with BP network and MLP network, deep belief network shows better performance on four evaluation indicators, namely ER, MAE, RMSE and MSE, and is more suitable for the design of financial trend prediction model. The above experimental results verify the feasibility and superiority of the financial trend prediction model based on deep belief network proposed in this study, which has certain application value.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123296813","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137944
Lei Yang, H. Huang, Suli Bai, Yanhong Liu
Medical image segmentation is a basal and essential task for computer-aided diagnosis and quantification of diseases. However, robust and precise medical image segmentation is still a challenging task on account of much factors, such as complex backgrounds, overlapping structures, high variation of appearances and low contrast. Recently, with the strong support of deep convolutional neural networks (DCNNs), the encoder-decoder based segmentation networks have been the popular detection schemes for medical image analysis, yet image segmentation based on DCNNs still faces some limitations, such as restricted receptive field, limited information flow, etc. To address such challenges, a novel dual-branch deep residual U-Net network is proposed in this paper for medical image detection which provides more avenues for information flow to gather both high-level and low-level feature maps and a greater depth of contextual data.A residual U-Net network is constructed for efficient feature expression using residual learning, attention block, and feature expression. Meanwhile, fused with atrous spatial pyramid pooling (ASPP) block and squeeze-and-excitation (SE) block, The residual U-Net network is suggested to embed an attention fusion block to gather multi-scale contextual data. On the basis, To fully utilize local contextual data and increase segmentation precision, a dual-branch deep residual U-Net network is built by stacking two residual U-Net networks. Combined with multiple public benchmark data sets on medical images, including the CVC-ClinicDB, the GIAS set and LUNA16 set, experimental results indicate the superior ability of proposed segmentation network on medical image segmentation compared with other advanced segmentation models.
{"title":"An Automatic Medical Image Segmentation Approach via Dual-Branch Network","authors":"Lei Yang, H. Huang, Suli Bai, Yanhong Liu","doi":"10.1109/ACAIT56212.2022.10137944","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137944","url":null,"abstract":"Medical image segmentation is a basal and essential task for computer-aided diagnosis and quantification of diseases. However, robust and precise medical image segmentation is still a challenging task on account of much factors, such as complex backgrounds, overlapping structures, high variation of appearances and low contrast. Recently, with the strong support of deep convolutional neural networks (DCNNs), the encoder-decoder based segmentation networks have been the popular detection schemes for medical image analysis, yet image segmentation based on DCNNs still faces some limitations, such as restricted receptive field, limited information flow, etc. To address such challenges, a novel dual-branch deep residual U-Net network is proposed in this paper for medical image detection which provides more avenues for information flow to gather both high-level and low-level feature maps and a greater depth of contextual data.A residual U-Net network is constructed for efficient feature expression using residual learning, attention block, and feature expression. Meanwhile, fused with atrous spatial pyramid pooling (ASPP) block and squeeze-and-excitation (SE) block, The residual U-Net network is suggested to embed an attention fusion block to gather multi-scale contextual data. On the basis, To fully utilize local contextual data and increase segmentation precision, a dual-branch deep residual U-Net network is built by stacking two residual U-Net networks. Combined with multiple public benchmark data sets on medical images, including the CVC-ClinicDB, the GIAS set and LUNA16 set, experimental results indicate the superior ability of proposed segmentation network on medical image segmentation compared with other advanced segmentation models.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131022596","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137846
Yukun Huang
In order to improve the intrusion detection ability of multi-dimensional node combination mixed topology network, this paper proposes an intrusion detection method based on naive Bayes algorithm. Build a distributed structure model of intrusion data in the network, and conduct traffic statistics and feature analysis on the network through low-speed monitoring and combined frequency scanning, so as to extract abnormal traffic label features of data in the network. Then, according to the types of attacks, Detect the fuzzy clustering center of intrusion data. The fusion model of anomaly feature distribution of intrusion traffic sequence is established based on the clustering results. Based on this, detect the redundancy and correlation of intrusion information, then analyze the fuzzy weight analysis of intrusion traffic sequence, and complete adaptive learning. Finally, control the attack data, so as to achieve the extraction and detection of intrusion information features. The test results show that the intrusion data detection results obtained by this method have high accuracy, so it has good detection performance and strong anti-interference ability, which can be used to improve the network security and anti attack ability.
{"title":"Network Intrusion Detection Method Based on Naive Bayes Algorithm","authors":"Yukun Huang","doi":"10.1109/ACAIT56212.2022.10137846","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137846","url":null,"abstract":"In order to improve the intrusion detection ability of multi-dimensional node combination mixed topology network, this paper proposes an intrusion detection method based on naive Bayes algorithm. Build a distributed structure model of intrusion data in the network, and conduct traffic statistics and feature analysis on the network through low-speed monitoring and combined frequency scanning, so as to extract abnormal traffic label features of data in the network. Then, according to the types of attacks, Detect the fuzzy clustering center of intrusion data. The fusion model of anomaly feature distribution of intrusion traffic sequence is established based on the clustering results. Based on this, detect the redundancy and correlation of intrusion information, then analyze the fuzzy weight analysis of intrusion traffic sequence, and complete adaptive learning. Finally, control the attack data, so as to achieve the extraction and detection of intrusion information features. The test results show that the intrusion data detection results obtained by this method have high accuracy, so it has good detection performance and strong anti-interference ability, which can be used to improve the network security and anti attack ability.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129659428","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137933
Shahela Saif, Samabia Tehseen
Face analysis is one of the key research areas in the field of computer vision with applications in numerous areas. Face recognition, emotion recognition, and more recently deepfake detection have greatly benefited from the advancements in the field of face analysis. Our research attempts to identify useful facial features for analysis. We first analyze the effectiveness of geometric facial features for the purpose of emotion recognition. In later experiments, a fusion scheme was created based on the preliminary analysis,which tested the performance of these selected features for the identification of real and fake images. We include local image features in combination with geometric facial features to measure their effectiveness in fake image detection tasks. The promising results produced in this study can be used to perform a more in-depth analysis of face geometry and its result in facial analysis.
{"title":"Evaluating Effectiveness of Using Multi-Features to Differentiate Real from Fake Facial Images","authors":"Shahela Saif, Samabia Tehseen","doi":"10.1109/ACAIT56212.2022.10137933","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137933","url":null,"abstract":"Face analysis is one of the key research areas in the field of computer vision with applications in numerous areas. Face recognition, emotion recognition, and more recently deepfake detection have greatly benefited from the advancements in the field of face analysis. Our research attempts to identify useful facial features for analysis. We first analyze the effectiveness of geometric facial features for the purpose of emotion recognition. In later experiments, a fusion scheme was created based on the preliminary analysis,which tested the performance of these selected features for the identification of real and fake images. We include local image features in combination with geometric facial features to measure their effectiveness in fake image detection tasks. The promising results produced in this study can be used to perform a more in-depth analysis of face geometry and its result in facial analysis.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127768635","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137838
Jianhong Zou, Yihui Cui, Ting Zhao, Weihua Ouyang, Bei Luo, Qilie Liu
In the autonomous driving system, accurate scene perception and trajectory prediction are critical for collision avoidance and path planning of autonomous vehicles. This paper proposes a scene perception and trajectory prediction method based on graph attention mechanism to learn semantic and interaction information based on bird eye’s view (BEV) map. The method includes spatiotemporal pyramid network and graph attention network. The former uses spatiotemporal pyramid network to model the surrounding information to obtain scene features, and graph attention network models the interaction information of the surrounding traffic participants to obtain graph interactive features. Then, scene semantic features and graph interaction features are fused into a unified feature space to perform downstream pixel-level classification and trajectory prediction tasks. Compared with baseline method, the proposed method significantly improves the average classification accuracy and reduces the average error of trajectory prediction with high efficiency. Experimental results show that the proposed method has better performance and is more feasible for deployment in real-world automatic driving scenarios.
{"title":"Spatiotemporal Pyramid Aggregation and Graph Attention for Scene Perception and Tajectory Prediction","authors":"Jianhong Zou, Yihui Cui, Ting Zhao, Weihua Ouyang, Bei Luo, Qilie Liu","doi":"10.1109/ACAIT56212.2022.10137838","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137838","url":null,"abstract":"In the autonomous driving system, accurate scene perception and trajectory prediction are critical for collision avoidance and path planning of autonomous vehicles. This paper proposes a scene perception and trajectory prediction method based on graph attention mechanism to learn semantic and interaction information based on bird eye’s view (BEV) map. The method includes spatiotemporal pyramid network and graph attention network. The former uses spatiotemporal pyramid network to model the surrounding information to obtain scene features, and graph attention network models the interaction information of the surrounding traffic participants to obtain graph interactive features. Then, scene semantic features and graph interaction features are fused into a unified feature space to perform downstream pixel-level classification and trajectory prediction tasks. Compared with baseline method, the proposed method significantly improves the average classification accuracy and reduces the average error of trajectory prediction with high efficiency. Experimental results show that the proposed method has better performance and is more feasible for deployment in real-world automatic driving scenarios.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128820039","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137972
Xing Zhou, Yaping Wan
Causal relation is the cornerstone of human understanding and exploration of the world. Inferring causal relations between things has been of interest to researchers. Most traditional methods are designed purely for discrete or continuous data, yet mixed data are widely available. This paper proposes a causal discovery method based on a hybrid structural equation model. The main idea is to formulate a nonlinear causal mechanism for mixed data through a hybrid structural equation model, while incorporating the ideas of structural equation and probabilistic noise in likelihood maximization, which realizes efficient causal inference on mixed data. Experimental results on synthetic and real-world datasets show that the method improves the accuracy of causal inference for mixed data and it’s robust to anomalous data.
{"title":"Causal Discovery Based on Hybrid Structural Equation Model","authors":"Xing Zhou, Yaping Wan","doi":"10.1109/ACAIT56212.2022.10137972","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137972","url":null,"abstract":"Causal relation is the cornerstone of human understanding and exploration of the world. Inferring causal relations between things has been of interest to researchers. Most traditional methods are designed purely for discrete or continuous data, yet mixed data are widely available. This paper proposes a causal discovery method based on a hybrid structural equation model. The main idea is to formulate a nonlinear causal mechanism for mixed data through a hybrid structural equation model, while incorporating the ideas of structural equation and probabilistic noise in likelihood maximization, which realizes efficient causal inference on mixed data. Experimental results on synthetic and real-world datasets show that the method improves the accuracy of causal inference for mixed data and it’s robust to anomalous data.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126721617","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 : 2022-12-09DOI: 10.1109/ACAIT56212.2022.10137979
Yan Hu, Gaodi Xu, Jie Shen, Houqun Yang, Shumeng He
Blockchain technology has attracted much attention since its emergence. Its unique characteristics of decentralization, trustworthiness and tamper-proof provide the possibility to build a more secure and effective data sharing platform. This paper first discusses the relevant knowledge of data sharing technology, explains how block chain realizes data sharing, and then analyzes existing data sharing schemes. It is also classified according to its core technology, so that researchers can quickly understand the existing data sharing schemes based on block chain, and can judge and choose research direction and technical route according to their own needs. This is also the value of this study. Finally, this paper analyzes the performance of four shared data schemes using experimental data from literature, and predicts the future development of sharing technology.
{"title":"Research on Secure Data Sharing Technology of Block Chain","authors":"Yan Hu, Gaodi Xu, Jie Shen, Houqun Yang, Shumeng He","doi":"10.1109/ACAIT56212.2022.10137979","DOIUrl":"https://doi.org/10.1109/ACAIT56212.2022.10137979","url":null,"abstract":"Blockchain technology has attracted much attention since its emergence. Its unique characteristics of decentralization, trustworthiness and tamper-proof provide the possibility to build a more secure and effective data sharing platform. This paper first discusses the relevant knowledge of data sharing technology, explains how block chain realizes data sharing, and then analyzes existing data sharing schemes. It is also classified according to its core technology, so that researchers can quickly understand the existing data sharing schemes based on block chain, and can judge and choose research direction and technical route according to their own needs. This is also the value of this study. Finally, this paper analyzes the performance of four shared data schemes using experimental data from literature, and predicts the future development of sharing technology.","PeriodicalId":398228,"journal":{"name":"2022 6th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114445877","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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