Challenges in Neural Networks: Theory, Algorithms, and Applications is a peer-reviewed journal dedicated to addressing the complexities and challenges in neural network development. It serves as a key platform for researchers, practitioners, and enthusiasts from various fields such as computer vision, natural language processing, robotics, healthcare, and finance. The journal publishes original research, reviews, and perspectives that advance the theory, algorithms, and practical deployment of neural network models. It emphasizes the importance of reproducibility, fairness, interpretability, and ethical considerations in research. The journal promotes interdisciplinary collaboration, encourages the exchange of ideas between academia and industry, and maintains high scientific standards through rigorous peer review. It aims to foster a vibrant community that drives innovation and contributes to significant societal breakthroughs by providing a forum for cutting-edge research and real-world applications in Neural Networks.

Current Issue

Vol. 1 (2025): Challenges in Neural Networks: Theory, Algorithms, and Applications
Challenges in Neural Networks: Theory, Algorithms, and Applications

Publication date: 31.12.2025
Description: Challenges in Neural Networks: Theory, Algorithms, and Applications focuses on addressing key issues in neural networks, including theory, algorithms, and practical deployment.
Publishing Model:The journal is currently accepting manuscripts for the current issue (Vol. 1, 2025). Embracing the Continuous Article Publishing (CAP) model, Challenges in Neural Networks: Theory, Algorithms, and Applications publishes articles promptly after the peer-review process.
Manuscript Types Accepted: Original Research Articles, Review Articles, Perspectives.

Published: 2025-12-31
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