Paper Key : IRJ************898
Author: Jaswinder Singh,Revanshu Silu
Date Published: 13 Nov 2024
Abstract
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.Despite their individual strengths, both ANNs and CNNs face notable challenges. ANNs can struggle with overfitting, leading to poor performance on unseen data, while CNNs often demand high computational resources, which can limit their application in resource- constrained environments. Moreover, both types of networks typically function as "black boxes," raising concerns about the interpretability of their decision-making processes.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.
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