International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 7 Issue 6
November-December 2025

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Neural Networks: A Thematic Review of Architectures, Optimization, and Real-World Impact

Author(s) Ms. Mahi Singh Rana, Shivam Prakash
Country India
Abstract Neural networks have emerged as transformative computational frameworks driving innovations across artificial intelligence, computer vision, natural language processing, and scientific computing. This comprehensive review examines the evolution, architectures, optimization methodologies, and real-world applications of neural networks. We analyze fundamental architectures including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. The review explores training methodologies encompassing gradient-based optimization, regularization techniques, and convergence strategies. Through thematic organization, we synthesize insights from recent literature highlighting breakthroughs in computer vision, language understanding, medical diagnosis, and autonomous systems. Critical analysis reveals persistent challenges including data efficiency, interpretability, robustness, and computational demands. We identify research gaps and future directions emphasizing efficient architectures, explainable AI, continual learning, and ethical deployment. Illustrated with comprehensive diagrams, this review serves researchers and practitioners seeking holistic understanding of neural network theory and practice.
Keywords Neural networks, Deep learning, Convolutional networks, Recurrent networks, Transformer architecture, Optimization algorithms, Computer vision, Natural language processing, Machine learning applications
Field Computer Applications
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-29
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62040
Short DOI https://doi.org/hbdsn4
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