Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Emary, Eid
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : International Journal of Electrical and Computer Engineering

 1 
Enhancing training performance for small models using data-centric approaches El-Khoribi, Reda A.; Emary, Eid; Hassan, Amr Essam
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2951-2964

Abstract

In this work, we propose a new system to improve the performance of classification models by applying data-centric principles. The system optimizes datasets by removing poor-quality samples and generating high-quality synthetic data. We tested the system on various classification models and datasets, measuring its performance with accuracy, precision, recall, and F1-score. The results showed significant improvements in classification performance, highlighting the effectiveness of this data-centric approach. While the scalability to large-scale datasets is still an open question, it offers great potential for future research. This approach could be valuable in critical areas like healthcare, finance, and autonomous systems, where high-quality data is crucial. Future work could explore advanced data augmentation, adapting the system for different data types like text and time-series, and extending it to semi-supervised and unsupervised learning. Our findings emphasize the importance of data quality in achieving better model performance, often overlooked in favor of model architecture. By advancing data-centric artificial intelligence (AI), this work offers a practical framework for researchers and practitioners to optimize datasets and improve machine learning systems.
Co-Authors El-Khoribi, Reda A. Hassan, Amr Essam