Publications

Zea Mays Leaf Disease Classification using Swin Transformer

Published in INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE APPLICATIONS (SIGMAA 2023), 2023

This study employs the Swin Transformer for accurate classification of corn leaf diseases, achieving a high accuracy of 95.9%, surpassing traditional methods.

Smart manufacturing with transfer learning under limited data: Towards Data-Driven Intelligences

Published in Journal Materials Today Communications (Impact Factor: 3.8), 2023

This study develops a new transfer learning-based Deep Neural Network model to predict the material removal rate in electrical discharge machining (EDM), overcoming the limitation of traditional machine learning methods that require large datasets.

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A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Published in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2022

This study classifies bearing defects using a Vision Transformer (ViT) on Case Western Reserve University data, converting vibration signals into 2D time-frequency images for input, achieving 98.8% accuracy.

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