Impact Factor and Journal Rank of Intelligent Data Analysis

About:

Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. It covers both theoretical advancements and practical applications in the field.

Impact and Significance

Intelligent Data Analysis plays a crucial role in advancing the field of Artificial Intelligence and data analysis:

Theoretical Advancements: Publishes cutting-edge research contributing to AI techniques and methodologies.
Practical Applications: Features applications of AI in various domains, enhancing real-world data analysis.
Interdisciplinary Approach: Bridges research across disciplines, fostering collaboration and innovation.
Knowledge Exchange: Facilitates the exchange of ideas between researchers, practitioners, and academics.
Technological Advancements: Contributes to the development of tools and techniques for data-driven decision-making.

Types of Articles Accepted

Intelligent Data Analysis welcomes different types of contributions, including:

Research Papers: Original research articles presenting new findings and methodologies.
Survey Papers: Comprehensive reviews of the state-of-the-art in specific areas of data analysis.
Application Papers: Case studies and applications demonstrating the use of AI techniques in practice.
Methodological Papers: Development of new algorithms, models, or frameworks for data analysis.
Short Communications: Brief reports on emerging research or preliminary findings.

Journal Home:  Journal Homepage

Editor-in-Chief:  JM. Peña

scope: Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. It covers both theoretical advancements and practical applications in the field. Scope: The journal encompasses a wide range of topics related to Artificial Intelligence techniques in data analysis, including:

Data Visualization: Techniques and tools for visualizing complex datasets to facilitate data understanding and exploration.

Data Pre-processing: Methods and algorithms for cleaning, transforming, and preparing data for analysis.

Data Engineering: Techniques for designing and optimizing data pipelines and architectures.

Database Mining Techniques: Methods for extracting useful patterns and knowledge from large datasets stored in databases.

Use of Domain Knowledge in Data Analysis: Integration of domain-specific knowledge to enhance data analysis processes and results.

Big Data Applications: Applications of AI techniques in handling and analyzing massive volumes of data.

Evolutionary Algorithms: Optimization techniques inspired by natural evolution for solving complex data analysis problems.

Machine Learning: Algorithms and models for automated learning and prediction from data.

Neural Networks and Fuzzy Logic: Applications and advancements in artificial neural networks and fuzzy logic systems for data analysis.

Statistical Pattern Recognition: Techniques for identifying patterns and structures in data using statistical methods.

Knowledge Filtering and Post-processing: Methods for refining and extracting actionable knowledge from raw data analysis results.

Print ISSN:  1088-467X

Electronic ISSN:  1571-4128

Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

Imapct Factor 2023:  0.9

Subject Area and Category:  Computer Sciences, Mathematics

Publication Frequency:  Bimonthly

H Index:  52

Best Quartile:

Q1:  

Q2:  

Q3:  Artificial Intelligence

Q4:  

Cite Score:  2.2

SNIP:  0.502

Journal Rank(SJR):  0.322

Latest Articles:   Latest Articles in Intelligent Data Analysis

Guidelines for Authors: Intelligent Data Analysis Author Guidelines

Paper Submissions: Paper Submissions in Intelligent Data Analysis

Publisher:  IOS Press

Country:  United Kingdom