Published in Submitted to WACV 2025, 2024
We propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation.
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Published in Submitted to AAAI 2025, 2024
This study proposes the Temporal Convolutional Network Former (TCNFormer) for short-term (12-hour) wind speed forecasting.
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Published in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2024
This paper presents the External Attention Vision Transformer (EAViT) model, a novel approach designed to enhance audio classification accuracy.
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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.
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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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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