Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
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All Journal Journal of ICT, Design, Engineering and Technological Science
Hayden Bin Nor Azman
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Mechanical Engineering

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A Custom CNN Architecture for Image Recognition using CIFAR‑10 Hayden Bin Nor Azman; Abdul Salam Shah
Journal of ICT, Design, Engineering and Technological Science Volume 8, Issue 2
Publisher : Journal of ICT, Design, Engineering and Technological Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33150/JITDETS-8.2.6

Abstract

Deep Learning algorithms have remained prominent in image classification across various domains. CNNs have modernized the process, enabling automated classification in new fields. Traditional models suffer from lower accuracy largely due to manual feature extraction. To address challenges in modern algorithms, this paper proposes a CNN‑based model for the multi‑class problem using the CIFAR‑10 dataset. The hyperparameters have been carefully selected to achieve higher accuracy while avoiding overfitting. Data augmentation and dropout layers contributed to achieving 85.49% accuracy
Co-Authors Abdul Salam Shah