ACM Transactions on Intelligent Systems and Technology

Scope & Guideline

Unleashing the Potential of AI and Computational Science

Introduction

Explore the comprehensive scope of ACM Transactions on Intelligent Systems and Technology 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 ACM Transactions on Intelligent Systems and Technology in depth and align your research initiatives with current academic trends.
LanguageEnglish
ISSN2157-6904
PublisherASSOC COMPUTING MACHINERY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 2010 to 2024
AbbreviationACM T INTEL SYST TEC / ACM Trans. Intell. Syst. Technol.
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address1601 Broadway, 10th Floor, NEW YORK, NY 10019-7434

Aims and Scopes

ACM Transactions on Intelligent Systems and Technology (TIST) focuses on advancing the understanding and application of intelligent systems and technology across various domains. The journal aims to publish high-quality, innovative research that contributes to the development of intelligent systems, particularly in areas such as machine learning, data mining, artificial intelligence, and their applications.
  1. Intelligent Systems and Applications:
    The journal emphasizes research that explores the development and application of intelligent systems across various fields, including recommender systems, natural language processing, and autonomous systems.
  2. Machine Learning and Data Analytics:
    A significant focus is on machine learning methodologies and data analytics techniques, including deep learning, reinforcement learning, and their applications to real-world problems.
  3. Privacy and Security in Intelligent Systems:
    Research addressing privacy concerns and security challenges in intelligent systems, particularly in federated learning and data sharing contexts, is a core area of interest.
  4. Explainability and Fairness:
    The journal promotes studies that investigate the explainability of intelligent systems and fairness in algorithmic decision-making, ensuring that systems are transparent and equitable.
  5. Interdisciplinary Approaches:
    TIST encourages interdisciplinary research that combines insights from computer science, social sciences, and engineering to address complex societal challenges through intelligent systems.
ACM TIST is witnessing a dynamic evolution in its focus areas, with several themes gaining traction in recent years. These emerging scopes reflect the journal's responsiveness to the latest advancements and societal needs in the field of intelligent systems.
  1. Federated Learning and Privacy-Preservation:
    There is a growing emphasis on federated learning techniques that prioritize privacy and security, particularly in contexts such as healthcare and personalized services, as researchers seek to balance data utility with privacy concerns.
  2. Responsible AI and Ethical Considerations:
    Papers addressing the ethical implications of AI technologies, including responsible recommendation systems and fairness in algorithms, are increasingly prevalent, reflecting a societal push for accountability in technology.
  3. Explainable AI (XAI):
    The rise of explainable AI is notable, with a focus on making complex models understandable to users and stakeholders, facilitating trust and transparency in intelligent systems.
  4. Multimodal Learning:
    Research exploring the integration of multiple modalities (e.g., text, image, audio) for enhanced understanding and performance in intelligent systems is on the rise, highlighting the need for more comprehensive approaches to data.
  5. Graph Neural Networks (GNNs):
    GNNs are gaining popularity for their effectiveness in modeling complex relationships in data, particularly in social networks and recommendation systems, suggesting a trend towards more sophisticated network-based approaches.

Declining or Waning

While ACM TIST has consistently focused on various aspects of intelligent systems, certain themes appear to be declining in prominence over recent years. This may reflect shifts in research priorities or advancements in technology that make previous approaches less relevant.
  1. Traditional Data Mining Techniques:
    There is a noticeable decrease in publications centered around conventional data mining techniques, as the field shifts towards more advanced methodologies like deep learning and AI-driven analytics.
  2. Basic Algorithmic Approaches:
    Research papers that focus solely on fundamental algorithmic approaches without novel applications or enhancements are becoming less frequent, indicating a preference for innovative and application-driven studies.
  3. General Surveys on Established Topics:
    While surveys are valuable, the journal has seen fewer papers addressing well-established topics without significant new insights or developments, suggesting a shift towards more cutting-edge and emergent areas of research.

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