Corresponding Author's : Dr.Kavita Mittal

Abstract :

Visual intelligence, the ability of machines to interpret and understand visual data, has been significantly advanced by Convolutional Neural Networks (CNNs). This study investigates the enhancement of visual intelligence performance through the optimization of CNN architectures. By comparing basic CNN models with optimized versions, incorporating advanced techniques such as deeper convolutional layers, batch normalization, dropout, and data augmentation, the aim is to improve accuracy and robustness in image detection and classification tasks. The experiments are carried out on benchmark datasets and the results demonstrate that optimized CNNs substantially outperform their basic counterparts, achieving higher training and validation accuracies. These findings highlight the critical role of architectural refinements and regularization techniques in advancing visual intelligence capabilities. This research presents a novel approach that underscores the capability of optimized CNNs to drive future innovations in the area of visual intelligence, offering more accurate and reliable visual data interpretation for real life applications.

Keywords :

Deep Learning, CNN Optimization, Batch Normalization, Dropout, Regularization Techniques, Implementation Code.

Article Details

Unique Paper ID: 2111

Publication Volume & Issue: VOLUME 4 , ISSUE 3

Page(s): 1-9

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