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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM61925
Volume:
10 |
4 |
April 2026
Pages:
1-2
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Gandhinagar University, Gandhinagar, Gujarat, India.
Department of Computer Science
Gandhinagar University, Gandhinagar, Gujarat, India.
Neural Networks have become a key technology in Artificial Intelligence for solving complex problems such as pattern recognition, prediction, and decision-making. Inspired by the human brain, they learn from data and improve performance over time. This paper presents an overview of neural network architecture, types (feedforward, convolutional, and recurrent), and training methods, including backpropagation and optimisation techniques. It also highlights applications in healthcare, finance, natural language processing, and computer vision. Advantages such as high accuracy and automation are discussed, along with challenges such as computational cost, overfitting, and lack of interpretability. Future advancements in explainable AI and efficient models are also considered.
KEYWORDS
Neural Networks are a key technology in Artificial Intelligence, enabling intelligent systems and improving decision-making across industries. They have revolutionised the way machines learn and interact with data by enabling them to analyse complex patterns and make accurate predictions. Their application in fields such as healthcare, finance, transportation, and communication has significantly enhanced efficiency, accuracy, and automation. Furthermore, neural networks continue to evolve with advancements in deep learning, making them more powerful and capable of solving real-world problems.
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Publication Date
5/1/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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