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Journal of Discrete Mathematical Sciences and Cryptography cover

Journal of Discrete Mathematical Sciences and Cryptography

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

CiteScore 2025: 2.7 (Q1)

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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Discrete Mathematical Sciences and Cryptography
Open Access Research Article

Enhance MRI images of lung cancer using hybrid transform 

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pp. 1897–1902Vol. 26Issue 7October 2023DOI: 10.47974/JDMSC-1682 Crossmark XML
Received:
06 Oct 2022
Published Online:
30 Oct 2023
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1682
Pages:
1897–1902

Abstract

This paper presents a novel application called the rational valued cyclic characters group, which extensively utilizes fuzzy partial differential equations in medicine and engineering. Among adult malignancies, acute interstitial pneumonia represents approximately 6% of cases, making it the most prevalent malignancy among individuals under 20. In recent years, The use of mathematical models to supplement experimental biological models in cancer research has increased. This paper proposes a new approach to address these challenges by developing a method based on filtering and optimizing radiographic images of damaged cells and differentiating parameters. The method utilizes the newly introduced rational valued cyclic group characters and partial fuzzy transformation. Notably, the suggested transform demonstrates significantly enhanced intelligibility compared to the fuzzy partial Laplace transform. Furthermore, this approach facilitates the analysis and categorization of disease progression and its impact on a patient’s response to treatment.

Keywords

Mathematical modelingRationally valued charactersCyclic groupĥ - conjugatePDEFPF-transformImage processingLung cancer imaging

Subject Classifications

62H3562M40

References

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