Sabna,
Farsana Muhammed,
- M.Tech Scholar Department of Electrical & Electronics Engineering, TKM College of Engineering, Kollam Kerala India
- Assistant Professor Department of Electrical & Electronics Engineering, TKM College of Engineering, Kollam Kerala India
Abstract
Computational models called artificial neural networks (ANNs) are modeled after the structure of the human brain. These models are designed to process information and learn from data. Artificial neural networks, or ANNs, are composed of interconnected artificial neurons layered to resemble the brain’s neural network.. Through training, ANNs adjust the connections between neurons based on labeled data, enabling them to recognize patterns and perform specific tasks. Despite their efficacy in areas such as image recognition and prediction, ANNs have limitations. They require substantial amounts of labeled data, can be challenging to interpret, and are susceptible to biases. However, their adaptability and capacity for continuous learning make them valuable tools in various applications, from computer vision to natural language processing. As research in artificial intelligence advances, ANNs are expected to drive further technological progress. This review paper explores the diverse applications of Artificial Neural Networks (ANNs) in fields such as wireless networking, internal combustion engines, and power systems. It explores the fundamentals of ANNs, their evolution, and training methods. The paper discusses ANNs use in weather prediction, evaluating construction project bids, and modeling industrial processes. It also examines challenges and advanced techniques in using ANNs. It also examines challenges and advanced techniques in using ANNs.
Keywords: Artificial Neural Network (ANN), Artificial Intelligence (AI), Machine Learning (ML), Computational models, Global Voltage Stability Index
[This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 11 |
| Issue | 02 |
| Received | June 6, 2024 |
| Accepted | July 7, 2024 |
| Published | July 18, 2024 |