Deep Learning and Pattern Recognition

Current Issue

Vol. 1 No. 1 (2024)
Published November 1, 2024
Deep Learning and Pattern Recognition

Deep Learning and Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks.

Articles

Zeyi Li; Dongfang Jia, Yucheng Guo
Fault analysis and reliability assessment of automobile clutches based on Bayes
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DOI: https://doi.org/10.71080/dlpr.v1i1.62
Bo Wang; Qingsheng Liu, Mulu Zhou
A temperature and humidity measurement method based on single chip microcomputer
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DOI: https://doi.org/10.71080/dlpr.v1i1.59
Peilin Wang; Tianyu Hu
Design and implementation of the electronic label authentication protocol based on digital signature
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DOI: https://doi.org/10.71080/dlpr.v1i1.60
Junqi Zhang; Feng Li, Bingbing Wang
Survey on Image clustering : Techniques, Challenges, and Future Perspectives
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DOI: https://doi.org/10.71080/dlpr.v1i1.63
Xiang Zhao
Survey: The Evolution and Future of Android Software Development
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DOI: https://doi.org/10.71080/dlpr.v1i1.64
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Aims and scope: Deep Learning and Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition and deep learning in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition.

We accepts papers making original contributions to the theory, methodology and application of pattern recognition and deep learning in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature.