Frontiers in Applied Mathematics and Statistics

Scope & Guideline

Driving Progress in Applied Mathematics and Statistics

Introduction

Delve into the academic richness of Frontiers in Applied Mathematics and Statistics with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN-
PublisherFRONTIERS MEDIA SA
Support Open AccessNo
Country-
Type-
Converge-
AbbreviationFRONT APPL MATH STAT / Front. Appl. Math. Stat.
Frequency1 issue/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressAVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE CH-1015, SWITZERLAND

Aims and Scopes

Frontiers in Applied Mathematics and Statistics focuses on the intersection of mathematical theories and statistical methodologies, providing a platform for innovative research that addresses real-world problems through mathematical modeling and statistical analysis.
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
The journal is experiencing a dynamic evolution in its research focus, with several emerging themes gaining traction. This section outlines these trending topics and their significance in the current research landscape.
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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

While the journal continues to thrive in various research areas, certain themes and methodologies appear to be declining in prominence over the years. This section highlights these waning scopes.
  1. 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.
  2. 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.
  3. 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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