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9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Xu, Changlin Kong, Lingzhuo School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of Ning Xia Province Yinchuan750021 China

ISBN: (纸本)9781643684703

As a tool for studying uncertain information, cloud model is of great significance in uncertain artificial intelligence and data mining. Among them, the backward cloud transformation method is one of the important algorithms of cloud model, which can realize the conversion from quantitative data to qualitative concepts. In this paper, a dynamic incremental backward cloud transformation algorithm is proposed to solve the problem that the estimated value of hyper entropy is imaginary when the backward cloud transformation method is used to calculate hyper entropy. First, according to the formation characteristics of cloud drops, the generated samples and the original samples are fused as new samples by dynamically and randomly generating samples, and then the hyper entropy is estimated until the estimated value of hyper entropy is real. Secondly, the stability and convergence of the algorithm proposed in this paper are analyzed through simulation experiments. The experimental results show that the new dynamic incremental backward cloud transformation algorithm solves the problem that the hyper entropy estimation value is imaginary while the estimation error is small and the stability is good. Finally, the algorithm is applied to brain CT segmentation, and the results show that the algorithm is effective and practical. © 2023 The authors and IOS Press.

关键词： data mining

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An iterative algorithm based on simpson methods for solving fixed point problem of nonexpansive mappings

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 13-20页

作者： He, Long Zhu, Li-Jun Fu, Yuanmin School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Data Processing of Ning Xia Province North Minzu University Yinchuan750021 China School of Mathematics and Information Science North Minzu University Yinchuan750021 China

Fixed point methods have been studied extensively by the Scholars. The purpose of this paper is to suggest an iterative algorithm based on Simpson methods for solving fixed point problem of nonexpansive mappings in Hilbert spaces. Weak convergence of the presented methods has been proved for finding fixed points of nonexpansive mappings. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

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Large-Scale Fingerprint data Retrieval Based C-Means Clustering Model

Large-Scale Fingerprint Data Retrieval Based C-Means Cluster...

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information Technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Decai Wang xia Chang Weibing Zhang Yuelin Gao The Key Laboratory of Intelligent Information and Big Data Processing of Ningxia Province Yinchuan China School of Math and Information Science North Minzu University Yinchuan China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In the field of biometric identification, fingerprints, as one of the most unique and persistent biometric features, are widely used for identification. With the popularity of fingerprint recognition in society, how to quickly retrieval fingerprints is a challenging task in large-scale databases. In order to ensure the accuracy of fingerprints retrieval , we propose a new retrieval model based on minutia features. We introduce fuzzy C-means clustering algorithm and Dijkstra *** C-means clustering is used to extract the detail point features in the fingerprint, Dijkstra algorithm to solve the shortest path of the minutia *** number of features and the shortest path distance are compared to determine whether the fingerprints are the same. In this way, our method can achieve fast retrieval. In addition, we conducted extensive experiments on a large-scale datasets and tested the correctness of the experiments by different filtering levels to demonstrate the superiority of our method for fingerprint retrieval.

关键词： Accuracy Filtering databases Smart cities Filtration Clustering algorithms Fingerprint recognition

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