IEEE INTELLIGENT SYSTEMS

Scope & Guideline

Empowering Innovation in AI and Networks

Introduction

Immerse yourself in the scholarly insights of IEEE INTELLIGENT SYSTEMS with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN1541-1672
PublisherIEEE COMPUTER SOC
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2001 to 2024
AbbreviationIEEE INTELL SYST / IEEE Intell. Syst.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314

Aims and Scopes

The journal 'IEEE Intelligent Systems' serves as a pivotal platform for disseminating research that intersects artificial intelligence, machine learning, and data analytics. It aims to explore and innovate in intelligent systems that enhance human capabilities and decision-making processes.
  1. Artificial Intelligence and Machine Learning:
    The journal focuses on the development and application of AI and machine learning techniques across various domains, including natural language processing, computer vision, and robotics.
  2. Ethics and Responsible AI:
    A consistent theme is the exploration of ethical considerations in AI, including trustworthiness, bias, and the societal impacts of deploying intelligent systems.
  3. Multimodal Systems and Integration:
    Research on integrating various modalities, such as text, image, and audio data, to create more robust and effective intelligent systems is a core area of focus.
  4. Graph-Based Learning and Neural Networks:
    The journal emphasizes novel approaches utilizing graph structures and neural networks to solve complex problems in areas like social networks, recommendation systems, and anomaly detection.
  5. Human-Computer Interaction and Collaborative Systems:
    Studies that enhance the interaction between humans and intelligent systems, including collaborative AI and conversational agents, are a significant aspect of the journal's scope.
  6. Applications of Intelligent Systems:
    Practical applications of intelligent systems across diverse fields such as healthcare, finance, and smart cities are consistently highlighted, showcasing the real-world impact of research.
The journal is witnessing exciting trends and emerging themes that reflect the evolving landscape of intelligent systems. This section outlines these burgeoning areas of research, indicating their relevance and potential impact.
  1. Explainable AI (XAI):
    A growing emphasis on explainability and transparency in AI systems is evident. Researchers are increasingly focusing on methods that allow AI decisions to be understood and trusted by users.
  2. Neurosymbolic AI:
    The integration of neural networks and symbolic reasoning is gaining momentum, as researchers explore how these approaches can complement each other to create more robust AI systems.
  3. Adversarial Machine Learning:
    Research on adversarial attacks and defenses is trending, reflecting concerns about the security and reliability of AI systems in real-world applications.
  4. AI for Social Good:
    There is a rising interest in applying AI technologies to address social challenges, including healthcare, education, and environmental issues, highlighting the potential for positive societal impact.
  5. Federated Learning and Privacy-Preserving Techniques:
    As data privacy becomes increasingly critical, federated learning and other privacy-preserving methodologies are emerging as key areas of research, allowing for collaborative learning without compromising user data.

Declining or Waning

While 'IEEE Intelligent Systems' continues to thrive in many areas, some themes have begun to see a decline in focus over recent years. This section highlights these waning topics, indicating shifts in research priorities.
  1. Traditional AI Techniques:
    There is a noticeable decrease in publications focusing solely on traditional AI techniques, such as rule-based systems or simple machine learning models, as the field shifts towards more complex and hybrid approaches.
  2. Generalized AI Concepts:
    Broad discussions surrounding generalized AI concepts without specific applications are becoming less prominent, as the journal favors targeted, application-driven research.
  3. Basic Data Mining Techniques:
    The prevalence of papers centered around basic data mining methods has diminished, as more sophisticated approaches, particularly those leveraging deep learning and advanced analytics, gain traction.
  4. Static System Designs:
    Research on static or non-adaptive intelligent system designs is declining, reflecting a shift towards dynamic, adaptive systems that can learn and evolve over time.
  5. Single-Modal Analysis:
    Studies focusing exclusively on single-modal data analysis are less frequently published, as the trend moves towards multimodal approaches that combine various data types for better outcomes.

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