COMPUTATIONAL INTELLIGENCE

Scope & Guideline

Advancing the Frontiers of Artificial Intelligence and Mathematics

Introduction

Delve into the academic richness of COMPUTATIONAL INTELLIGENCE 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
ISSN0824-7935
PublisherWILEY
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1985 to 2024
AbbreviationCOMPUT INTELL-US / Comput. Intell.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal 'Computational Intelligence' focuses on the intersection of computer science, artificial intelligence, and various application domains to advance knowledge and innovation in computational methodologies.
  1. Artificial Intelligence and Machine Learning:
    The core focus is on developing and applying AI and machine learning techniques across various domains, including healthcare, telecommunications, and environmental monitoring.
  2. Data Analysis and Predictive Modeling:
    The journal emphasizes advanced data analysis techniques, predictive modeling, and decision-making frameworks using AI-driven approaches.
  3. Multimodal and Multiscale Learning:
    Research often explores the integration of multimodal data sources and multiscale learning methods to enhance model performance and application range.
  4. Healthcare Applications:
    A significant portion of the research is dedicated to healthcare, including disease diagnosis, medical imaging analysis, and patient monitoring systems.
  5. Security and Privacy in Computing:
    The journal addresses the challenges of security and privacy in computational systems, particularly in the context of IoT and data protection.
  6. Optimization Techniques:
    Innovative optimization methodologies utilizing AI, such as genetic algorithms and reinforcement learning, are frequently discussed to solve complex problems.
Recent publications have highlighted several trending and emerging themes within the journal, reflecting the evolving landscape of computational intelligence.
  1. Explainable AI (XAI):
    With increasing concerns about AI decision-making transparency, research on explainable AI has gained traction, focusing on methods to make AI outputs understandable to users.
  2. Federated Learning and Privacy-Preserving Techniques:
    The rise of IoT and data privacy concerns has led to a growing interest in federated learning, which allows for model training without compromising individual data privacy.
  3. Deep Learning in Healthcare:
    Deep learning applications in healthcare, particularly for medical image analysis and disease prediction, are rapidly expanding, showcasing the potential of AI in improving patient outcomes.
  4. AI for Environmental Monitoring:
    Research focusing on the application of AI for monitoring and managing environmental issues, such as climate change and resource management, is on the rise.
  5. Integration of AI with IoT:
    The convergence of AI and IoT technologies is increasingly prevalent, with studies exploring smart systems that leverage AI for enhanced decision-making and automation.
  6. Multimodal Learning:
    There is a growing emphasis on multimodal learning approaches that integrate various data types (e.g., text, image, sensor data) to improve model performance and applicability.

Declining or Waning

As the field of computational intelligence evolves, certain themes have shown a decline in publication frequency or relevance.
  1. Traditional Statistical Methods:
    There is a noticeable reduction in the focus on traditional statistical methods for data analysis, as the field shifts towards more advanced machine learning techniques.
  2. Rule-Based Systems:
    The prominence of rule-based expert systems appears to be waning, with a shift towards learning-based approaches that can adapt and improve over time.
  3. Basic Neural Network Models:
    While neural networks remain a significant area of research, simpler architectures are being overshadowed by more complex models like deep learning and hybrid architectures.
  4. Single-Domain Applications:
    Research that focuses exclusively on single-domain applications is declining, as there is a growing trend towards interdisciplinary approaches that integrate multiple domains and data types.
  5. Conventional Image Processing Techniques:
    The interest in conventional image processing techniques is decreasing as more researchers adopt deep learning methods for image analysis and recognition.

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