Intelligent Data Analysis - IOS Press - Impact Factor

Intelligent Data Analysis - IOS Press | 2024 Impact Factor:0.8 | Cite Score:2.2 | Q3

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. PeÃ±a

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.