Contemporary Journal of Allied Research in Mathematics and Statistics
The Contemporary Journal of Allied Research in Mathematics and Statistics is an esteemed international, open-access, peer-reviewed publication dedicated to advancing the fields of mathematics and statistics. Published bimonthly, this journal provides a rigorous platform for scholars, researchers, and practitioners to disseminate significant, high-quality research that contributes to the understanding and development of mathematical and statistical theories, methods, and applications across diverse disciplines and geographic boundaries. We welcome interdisciplinary research that integrates mathematics and statistics perspectives with other fields, such as computer science, data science, and applied sciences.
Journal Metrics:
- Acceptance Rate: 33%
- Impact Factor: 7.50 (2024)
Submission and Publication:
Authors are cordially invited to submit full-length, original, and unpublished research articles for consideration.
Latest Articles
REVOLUTIONIZING PDE SOLUTIONS: ANNEALING ALGORITHM AND POLYNOMIAL REGRESSION INTEGRATION
Published by: Qiang Zhang , Lingyun Li
Pages: 1-11
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This paper presents a novel approach for efficiently solving global solutions to partial differential equations (PDEs) using a combination of the Annealing Algorithm and Polynomial Regression tailored specifically for the Feynman-Kac formulation. By integrating the Annealing Algorithm with Polynomial Regression techniques, the proposed method offers enhanced accuracy and computational
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BEYOND TRADITIONAL APPROACHES: ENHANCING PDE SOLUTIONS WITH K-NEAREST NEIGHBOR APPROACH
Published by: Hui Wang , Lei Zhang
Pages: 12-23
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This paper introduces a novel approach, utilizing a modified method based on the K-nearest neighbor approach, for solving global solutions to partial differential equations (PDEs) through the Feynman-Kac formula. By integrating the K-nearest neighbor approach with the Feynman-Kac formula, this method offers enhanced accuracy and efficiency in obtaining global solutions to PDEs across various
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PREDICTIVE ANALYTICS IN DEMOGRAPHY: BIRTH POPULATION FORECASTING WITH GRAY VERHULST MODEL
Published by: Mingwei Liu , Yuxuan Wang
Pages: 24-31
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Forecasting birth population is integral to population studies, aiding policy formulation, development planning, resource allocation, economic growth, and social issue research. Accurate predictions serve as a foundation for sustainable development and population health enhancement. This paper addresses birth population prediction through mathematical modeling, a vital endeavor undertaken by
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