Credibility Assessment Method of Sensor Data Based on Multi-Source Heterogeneous Information Fusion

Yanling, Feng; Hu, Jixiong; Duan, Rui; Chen, Zhuming

doi:10.3390/s21072542

Cited by 7 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In terms of blockchain information credibility assessment, literature [16] proposes a comprehensive credibility calculation method that effectively integrates multidimensional evaluation data, but focuses only on the relevant evaluation without paying attention to the information source as an influencing factor; literature [17] proposes a data credibility assessment method based on multi-source heterogeneous information fusion, which shows better results in improving the convergence of credibility calculation, but ignores the information content itself credibility; literature [18] proposes a supervised machine learning method for user-generated content credibility assessment, but the method only focuses on relevant comment information, ignoring the attention to information content and its sources.…”

Section: Related Studiesmentioning

confidence: 99%

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Zhang,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Zhang,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…In terms of blockchain information credibility assessment, literature [15] proposes a comprehensive credibility calculation method that effectively integrates multidimensional evaluation data, but focuses only on the relevant evaluation without paying attention to the information source as an influencing factor; literature [16] proposes a data credibility assessment method based on multi-source heterogeneous information fusion, which shows better results in improving the convergence of credibility calculation, but ignores the information content itself credibility; literature [17] proposes a supervised machine learning method for user-generated content credibility assessment, but the method only focuses on relevant comment information, ignoring the attention to information content and its sources.…”

Section: Related Studiesmentioning

confidence: 99%

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

Blockchain-based applications are becoming more and more widespread in business operations. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterisation method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain.

show abstract

“…According to Figure 1, in the process of human motion posture fast recognition, the hand and leg motion data are extracted first, the motion features of the hand and leg are extracted separately [20], then the features are normalized to obtain the human hand and leg motion feature sets, the fusion of human motion posture information is completed by improving the typical correlation analysis method, and the fusion results are used as the input of the minimum distance classifier to output the human motion posture fast recognition results.…”

Section: Fast Fusion Recognition Model For Human Motionmentioning

confidence: 99%

Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion

Zhao

Jian-hui

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To address the problems of low feature extraction accuracy, large bias of human motion pose recognition and posture recognition error, poor recognition effect, and low recognition rate of traditional human motion posture fast recognition algorithm, we propose a human motion posture fast recognition algorithm using multimodal bioinformation fusion. First, wavelet packet decomposition with sample entropy is used to extract the human motion posture hand features such as kurtosis, time domain feature skewness, and frequency domain feature electromyogram (EMG) integral value and time domain features such as mean, standard deviation, and interquartile distance of leg motion amplitude. Second, after normalizing the two features, the human hand and leg motion feature set is obtained, and finally the feature set is used to construct a human motion posture fast recognition model based on multimodal bioinformation fusion, and the feature set is input into the recognition model, which completes the fusion of human motion posture information by improving the typical correlation analysis method, and the fusion result is used as the input of the minimum distance classifier to achieve human motion posture fast recognition. The results show that the proposed algorithm has high accuracy of feature extraction, small bias of human motion posture recognition, the posture recognition error is -0.21∼0.02, the recognition rate is always above 95%, and the practical application effect is good.

show abstract

Credibility Assessment Method of Sensor Data Based on Multi-Source Heterogeneous Information Fusion

Cited by 7 publications

References 28 publications

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Multi-source heterogeneous blockchain data quality assessment model for enterprise business activities

Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion

Contact Info

Product

Resources

About