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Nature Machine Intelligence - Springer Nature | 2024 Impact Factor:23.9|Cite Score:37.6|Q1

Nature Machine Intelligence Journal

Impact Factor and Journal Rank of Nature Machine Intelligence

  • About:Nature Machine Intelligence is a prestigious, peer-reviewed journal that focuses on research and perspectives in the field of artificial intelligence (AI) and machine learning (ML). As part of the Nature family of journals, it upholds the high standards of scientific rigor and impact associated with Nature publications. Nature Machine Intelligence is a high-impact, peer-reviewed journal that focuses on cutting-edge research and developments in the field of artificial intelligence (AI) and machine learning (ML). Published by Nature Research, the journal serves as a leading platform for the dissemination of significant advances in AI and ML, exploring their theoretical foundations, practical applications, and broader implications for society.
  • Objective: The primary aim of Nature Machine Intelligence is to publish the highest quality research that advances the field of AI and ML. The journal seeks to foster an interdisciplinary dialogue between researchers, practitioners, and policymakers, addressing both the technical and societal aspects of intelligent systems.
  • Content: The journal features a diverse array of content, including original research articles, reviews, perspectives, and commentary. Topics covered range from foundational theories and algorithms to innovative applications and the ethical, legal, and social implications of AI technologies.
  • Interdisciplinary Approach: The journal encourages interdisciplinary research that integrates AI and ML with other scientific and engineering disciplines. This approach facilitates innovative solutions to complex problems and promotes the application of AI across various fields.
  • Global Reach and Influence: As part of the prestigious Nature family of journals, "Nature Machine Intelligence" attracts contributions from leading researchers and institutions worldwide. This global perspective ensures that the journal covers a wide range of insights and developments from around the world, enhancing its relevance and impact.
  • Impact and Significance: The journal is highly regarded for its rigorous peer-review process, ensuring the publication of high-quality, impactful research. "Nature Machine Intelligence" is influential in shaping the future directions of AI and ML research, informing practitioners, academics, and policymakers, and contributing to the responsible development and deployment of AI technologies.

  • Editor-in-Chief:  Liesbeth Venema

  • The scope of Nature Machine Intelligence encompasses a comprehensive range of topics within artificial intelligence (AI) and machine learning (ML), emphasizing both foundational research and innovative applications. The journal aims to bridge the gap between theoretical advances and practical implementations, while also addressing the ethical, societal, and policy implications of intelligent technologies.
  • Key areas of focus include:
  • Core AI Techniques: Research on machine learning, deep learning, reinforcement learning, neural networks, natural language processing, computer vision, robotics, and other fundamental AI methodologies.
  • Interdisciplinary Applications: Application of AI and ML in diverse fields such as healthcare, biology, physics, social sciences, economics, environmental science, and engineering. This includes studies that demonstrate the impact and potential of AI in solving real-world problems.
  • Human-AI Interaction: Exploration of how humans interact with intelligent systems, encompassing human-computer interaction, cognitive computing, user experience, and the design of intuitive AI interfaces.
  • Ethical, Legal, and Societal Implications: Examination of the ethical, legal, and societal challenges posed by AI technologies, including issues of bias, fairness, transparency, accountability, privacy, and the development of policies and regulations to govern the use of AI.
  • AI in Autonomous Systems: Research on autonomous vehicles, drones, robotics, and other systems that operate with a high degree of independence and intelligence.
  • Theoretical Foundations: Studies on the mathematical, statistical, and computational foundations of AI and ML, including algorithm development, optimization techniques, and theoretical analysis.
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  • Print ISSN:  2522-5839

    Electronic ISSN:  

  • Abstracting and Indexing:  Scopus and SCIE

  • Imapct Factor 2024:  23.9

  • Subject Area and Category:  Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition , Human-Computer Interaction ,Software

  • Publication Frequency:  Monthly

  • H Index:  67

  • Best Quartile:

    Q1:  Artificial Intelligence

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  37.6

  • SNIP:  5.736

  • Journal Rank(SJR):  5.876