Comparison of Artificial Neutral Network and Convolution Neural Network
International Research Journal of Modernization in Engineering Technology and Science · 2024
This research paper delves into the significant roles of Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) in artificial intelligence, comparing their structures, applications, and challenges.ANNs, known for their versatility, are widely used in various fields, including finance for risk assessment and in healthcare for predictive modeling.Their architecture allows them to process different types of data and learn complex patterns effectively.Conversely, CNNs are specifically engineered for handling visual data, employing a multi-layered approach to automatically detect and learn intricate features from images.This specialization makes them particularly effective in applications like image classification, video analysis, and even facial recognition.Recent advancements in techniques such as dropout regularization and batch normalization are helping to mitigate these challenges.Dropout regularization reduces overfitting by randomly dropping units during training, while batch normalization improves training speed and model stability. II. LITERATURE REVIEW This literature review looks at research on Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs).It covers what they are, how they have developed over time, where they are used, the challenges they face, and what the future might hold for them. Key ApplicationsANNs are used in many fields.For example, in finance, they can predict stock prices, and in healthcare, they help diagnose diseases from patient data.Hastie et al. (2009) demonstrated how ANNs can analyze complex relationships in financial datasets.CNNs have transformed image-related tasks.They are widely used in facial recognition,