INTELLIGENCE

Scope & Guideline

Bridging Disciplines for a Deeper Insight into Intelligence

Introduction

Immerse yourself in the scholarly insights of INTELLIGENCE 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
ISSN0160-2896
PublisherELSEVIER SCIENCE INC
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1977 to 2024
AbbreviationINTELLIGENCE / Intelligence
Frequency6 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressSTE 800, 230 PARK AVE, NEW YORK, NY 10169

Aims and Scopes

The journal 'INTELLIGENCE' focuses on the intersection of artificial intelligence, data science, and their applications across various domains. It aims to advance the understanding and development of intelligent systems, methodologies, and frameworks that leverage data for improved decision-making and problem-solving.
  1. Artificial Intelligence and Machine Learning:
    The journal publishes research on various AI methodologies, including neural networks, natural language processing, and machine learning techniques applied to real-world problems.
  2. Data Management and FAIR Principles:
    A strong emphasis is placed on the management of data, particularly in relation to the FAIR principles (Findable, Accessible, Interoperable, and Reusable), ensuring that data is handled responsibly and effectively.
  3. Knowledge Graphs and Semantic Technologies:
    Research on knowledge representation, knowledge graphs, and semantic technologies is a core area, focusing on how these tools can enhance understanding and accessibility of information.
  4. Interdisciplinary Applications:
    The journal encourages interdisciplinary research, showcasing applications of intelligence in diverse fields such as healthcare, environmental science, and social sciences.
  5. Ethics and Societal Impact of AI:
    A unique contribution of the journal is its focus on the ethical implications and societal impacts of AI technologies, addressing concerns related to privacy, bias, and transparency.
Recent publications in 'INTELLIGENCE' indicate several emerging themes that reflect the journal's evolving focus. These trends highlight the journal's commitment to addressing contemporary challenges and leveraging new technologies.
  1. Generative AI and Language Models:
    There is a growing trend in research related to generative AI, particularly around language models like ChatGPT, showcasing interest in their applications, limitations, and ethical considerations.
  2. Sustainable Data Practices:
    Emerging themes around sustainable data management practices and the integration of environmental considerations into AI research are becoming increasingly prominent.
  3. AI in Healthcare and Biomedicine:
    The journal is seeing a rise in publications that apply AI methodologies specifically to healthcare, including diagnostics and patient management, reflecting the urgent need for innovative solutions in this sector.
  4. Contextual and Ethical AI:
    Research focusing on the contextual application of AI and the ethical implications of AI technologies is gaining traction, indicating a shift towards more responsible and transparent AI practices.
  5. Interdisciplinary Approaches to AI Challenges:
    There is an increasing emphasis on interdisciplinary research that combines insights from various fields to tackle complex AI challenges, particularly in societal contexts.

Declining or Waning

While 'INTELLIGENCE' has consistently highlighted various research areas, some themes appear to be losing prominence in recent publications. This decline may reflect shifts in research focus or changing technological landscapes.
  1. Traditional Statistical Methods:
    There has been a noticeable decrease in publications focused solely on traditional statistical methods, as the journal leans more towards advanced AI and machine learning approaches.
  2. Generalized Data Science without Specific Applications:
    Papers that discuss data science in a broad sense without specific applications or case studies are becoming less frequent, as the journal favors targeted research with clear implications.
  3. Outdated Technologies and Frameworks:
    Research focusing on older technologies or frameworks not aligned with current trends in AI and data management is declining, as the journal prioritizes innovative and contemporary approaches.

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