Intelligent Data Analysis

Scope & Guideline

Transforming Data into Knowledge for Tomorrow's Innovators

Introduction

Explore the comprehensive scope of Intelligent Data Analysis through our detailed guidelines, including its aims and scope. Stay updated with trending and emerging topics, and delve into declining areas to understand shifts in academic interest. Our guidelines also showcase highly cited topics, featuring influential research making a significant impact. Additionally, discover the latest published papers and those with high citation counts, offering a snapshot of current scholarly conversations. Use these guidelines to explore Intelligent Data Analysis in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN1088-467x
PublisherIOS PRESS
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 1997 to 2024
AbbreviationINTELL DATA ANAL / Intell. Data Anal.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressNIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal "Intelligent Data Analysis" focuses on the intersection of data analysis and intelligent systems, emphasizing the development and application of advanced computational techniques to solve complex problems across various domains. The journal's core areas reflect a commitment to innovative methodologies and practical applications in data science.
  1. Machine Learning and Artificial Intelligence:
    The journal publishes research on machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques, highlighting their applications across diverse fields such as healthcare, finance, and social networks.
  2. Data Mining and Knowledge Discovery:
    A significant focus is placed on data mining techniques aimed at discovering patterns, trends, and insights from large datasets, with methodologies including clustering, classification, and association rule mining.
  3. Big Data Analytics:
    Papers often explore the challenges and solutions related to processing and analyzing big data, including the use of distributed computing and advanced statistical methods to derive meaningful insights.
  4. Predictive Analytics and Decision Support Systems:
    Research includes the development of predictive models and decision support systems that leverage historical data to inform future actions, particularly in domains such as healthcare, finance, and urban planning.
  5. Graph and Network Analysis:
    The analysis of complex networks and relationships, including social networks, biological networks, and transportation systems, is a recurring theme, employing graph theory and network-based algorithms.
  6. Natural Language Processing and Text Mining:
    The journal covers advancements in natural language processing (NLP), focusing on techniques for text classification, sentiment analysis, and information extraction from unstructured data.
  7. Time Series Analysis and Forecasting:
    Research on methodologies for time series forecasting and anomaly detection is prevalent, addressing applications in finance, healthcare monitoring, and environmental data.
The journal has recently seen a shift towards several trending and emerging themes that reflect the current landscape of data analysis and intelligent systems. This section highlights those themes that are gaining traction among researchers.
  1. Deep Learning Approaches:
    There is a significant increase in publications leveraging deep learning techniques, particularly in areas such as image processing, natural language processing, and time series forecasting, showcasing their effectiveness in handling complex data.
  2. Explainable AI (XAI):
    The growing demand for transparency in AI models has led to an increase in research focused on explainable AI, aiming to make machine learning models interpretable and understandable to users.
  3. Federated Learning and Privacy-Preserving Techniques:
    Emerging themes include federated learning and other privacy-preserving techniques, which are essential for analyzing sensitive data while maintaining user privacy.
  4. Transfer Learning and Domain Adaptation:
    Research on transfer learning and domain adaptation techniques is gaining traction, particularly in scenarios where labeled data is scarce, allowing for knowledge transfer from related domains.
  5. Integration of IoT and Data Analytics:
    The intersection of Internet of Things (IoT) technology and data analytics is a rising theme, focusing on real-time data processing and analysis from connected devices for smart applications.
  6. Anomaly Detection and Predictive Maintenance:
    There is an increasing focus on anomaly detection techniques, particularly in industrial applications, where predictive maintenance is critical for operational efficiency.

Declining or Waning

While "Intelligent Data Analysis" continues to evolve, some themes have shown signs of declining prominence in recent years. This section identifies those areas that seem to be receiving less attention in the journal's publications.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in the publication of papers focused on traditional statistical techniques, as the field shifts towards more complex machine learning and deep learning methodologies.
  2. Rule-Based Systems:
    Research centered on rule-based approaches for data analysis is becoming less prevalent, with a growing preference for data-driven methods that leverage learning algorithms.
  3. Basic Data Visualization Techniques:
    Papers that primarily focus on basic data visualization techniques without integrating them into more complex analytical frameworks are appearing less frequently.
  4. Classical Optimization Algorithms:
    There is a waning interest in classical optimization algorithms in favor of more sophisticated metaheuristic and evolutionary algorithms that offer better performance in complex scenarios.
  5. Static Data Analysis:
    The focus on static data analysis methods is declining as dynamic and real-time data processing becomes more critical in applications across various domains.

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