Frontiers in Applied Mathematics and Statistics
Scope & Guideline
Driving Progress in Applied Mathematics and Statistics
Introduction
Aims and Scopes
- Mathematical Modeling:
The journal emphasizes the development and application of mathematical models across various domains, including biology, finance, epidemiology, and engineering. This includes both theoretical advancements and practical implementations. - Statistical Analysis:
It covers a wide range of statistical techniques, from traditional methods to modern machine learning approaches, aimed at analyzing complex data sets and drawing meaningful inferences. - Interdisciplinary Research:
Frontiers in Applied Mathematics and Statistics encourages research that bridges multiple disciplines, integrating methodologies from mathematics, statistics, computer science, and domain-specific fields. - Applied Computational Techniques:
The journal highlights the use of computational methods and simulations in solving mathematical problems and modeling real-world systems, showcasing advancements in algorithms and numerical methods. - Data-Driven Approaches:
There is a consistent focus on data-driven methodologies, including machine learning and statistical learning, to enhance modeling accuracy and decision-making processes in various applications.
Trending and Emerging
- Epidemiological Modeling:
There is a surge in research related to mathematical modeling of infectious diseases, particularly in the context of COVID-19, emphasizing the need for effective public health strategies and intervention measures. - Machine Learning and AI Applications:
The incorporation of machine learning and artificial intelligence in statistical modeling and data analysis is on the rise, reflecting a broader trend in the field towards leveraging computational power for enhanced predictive capabilities. - Complex Systems and Network Theory:
Research exploring complex systems, including network dynamics and interactions, is increasingly featured, highlighting the importance of understanding interconnected systems in various fields. - Fractional Calculus and Dynamic Systems:
The application of fractional calculus in modeling dynamic systems is gaining interest, providing new insights into modeling phenomena exhibiting memory and hereditary properties. - Data Science and Big Data Analytics:
There is a growing emphasis on data science methodologies, particularly in the context of big data analytics, addressing challenges in high-dimensional data and complex data structures.
Declining or Waning
- Traditional Statistical Methods:
There is a noticeable decline in the publication of papers focusing solely on traditional statistical methods, as the field increasingly favors advanced computational techniques and machine learning approaches. - Purely Theoretical Mathematics:
Research that is heavily theoretical and lacks practical application appears to be less frequent, as the journal shifts towards studies that demonstrate real-world applicability and interdisciplinary collaboration. - Low-Complexity Models:
Simplistic models that do not account for complex interactions or multi-faceted dynamics are becoming less common, as researchers seek more robust and realistic representations of systems.
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