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IEEE Transactions on Neural Networks and Learning Systems | 2025 Impact Factor:9.7 | Cite Score:23.7 | Q1

IEEE Transactions on Neural Networks and Learning Systems Journal

Impact Factor and Journal Rank of IEEE Transactions on Neural Networks and Learning Systems

  • About: The IEEE Transactions on Neural Networks and Learning Systems(TNNLS) is a prestigious journal published by the IEEE Computational Intelligence Society. It focuses on research advancements in neural networks, machine learning, and related areas, with a particular emphasis on the theoretical foundations, algorithms, methodologies, and applications of these technologies.
  • Content: TNNLS publishes high-quality research articles that present novel contributions to the theory, algorithms, and applications of neural networks and learning systems. The journal-s content spans both foundational research and practical applications, addressing the needs of researchers, practitioners, and educators in the field.
  • Interdisciplinary Approach: TNNLS adopts an interdisciplinary approach, drawing upon concepts and methodologies from computer science, artificial intelligence, neuroscience, cognitive science, statistics, and engineering. This interdisciplinary perspective enables the journal to address complex challenges and explore innovative solutions in the domain of neural networks and learning systems.
  • Audience: The journal is targeted at a diverse audience, including researchers, academics, students, engineers, and practitioners interested in neural networks, machine learning, and related areas. The articles published in TNNLS are accessible to readers with varying levels of expertise, making the journal suitable for both specialists and generalists in the field.
  • Global Reach and Impact: TNNLS enjoys a broad international readership and impact, with contributions from leading researchers and institutions worldwide. The journal-s articles are frequently cited and influential in the field of neural networks and learning systems, reflecting its significance and relevance to the research community.
  • Educational Resource: In addition to research articles, TNNLS publishes tutorials and review papers that provide educational resources for students, educators, and practitioners. These articles offer insights into fundamental concepts, methodologies, and best practices in neural networks and learning systems, supporting academic learning and professional development.

  • Editor-in-Chief:  Yongduan Song

  • Scope: IEEE Transactions on Neural Networks and Learning Systems (TNNLS) is a prestigious journal that focuses on research related to neural networks, machine learning, and computational intelligence. Its scope encompasses a wide range of topics within these areas, with a particular emphasis on the theory, design, and application of neural networks and learning systems.
  • Here are some key aspects of the scope of TNNLS:
  • Neural Network Models and Architectures: Research on the design, analysis, and implementation of various types of neural network models and architectures, including feedforward neural networks, recurrent neural networks, convolutional neural networks (CNNs), deep neural networks (DNNs), spiking neural networks, and other biologically-inspired models.
  • Learning Algorithms and Optimization Techniques: Development and analysis of learning algorithms and optimization techniques for training neural networks, including supervised learning, unsupervised learning, reinforcement learning, evolutionary algorithms, meta-learning, transfer learning, and deep learning algorithms.
  • Neurodynamics and Computational Neuroscience: Studies on the dynamics, behavior, and computational properties of neural networks, drawing insights from computational neuroscience, neurobiology, and cognitive science to inform the design and analysis of artificial neural networks and learning systems.
  • Learning Theory and Generalization: Theoretical analysis of learning processes, generalization properties, convergence properties, and performance guarantees of neural networks and learning systems, including studies on overfitting, bias-variance tradeoff, and sample complexity.
  • Applications of Neural Networks and Learning Systems: Application-oriented research that applies neural networks and learning systems to solve real-world problems in various domains, including but not limited to computer vision, natural language processing, speech recognition, robotics, autonomous systems, biomedical engineering, finance, and cybersecurity.
  • Computational Intelligence and Hybrid Systems: Integration of neural networks with other computational intelligence techniques, such as fuzzy systems, evolutionary algorithms, swarm intelligence, and expert systems, to develop hybrid intelligent systems with enhanced capabilities and performance.
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  • Print ISSN:  2162-237X

    Electronic ISSN:   2162-2388

  • Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

  • Imapct Factor 2025:  9.7

  • Subject Area and Category:  Computer Sciences, Electronics and Telecommunications

  • Publication Frequency:  Monthly

  • H Index:  284

  • Best Quartile:

    Q1:  Artificial Intelligence

    Q2:  

    Q3:  

    Q4:  

  • Cite Score:  23.7

  • SNIP:  3.289

  • Journal Rank(SJR):  3.153