Published in arXiv Preprint, 2025
QiVC-Net introduces the Quantum-inspired Variational Convolution (QiVC) framework, a novel learning paradigm that integrates probabilistic inference, variational optimization, and quantum-inspired transformations into convolutional neural networks.
Published in Biomedical Signal Processing and Control, 2025
In this research, we propose a dynamic memory fusion framework for adaptive multi-loss function penalizing in real-time, which dynamically adjusts the weighting of loss functions based on historical loss values and incorporates an auxiliary loss function and class-balanced dice loss to improve segmentation performance. Download Manuscript
Published in Expert Systems with Applications, 2024
In this research, we propose a probabilistic gated recurrent unit (P-GRU) model for accurate Bitcoin price prediction, outperforming traditional deep learning models in capturing market volatility and uncertainty
Published in International Conference on Machine Vision and Image Processing (MVIP), 2020
In this research, we propose a system that combines Deep Neural Networks and Augmented Reality to recognize faces and display information about individuals. Download Manuscript
Published in Under Preparation, 2025
We propose a multimodal physiological modeling framework combining Supervised Contrastive Representation Learning and a unified Mixture Density Fusion module to achieve reliable, uncertainty-aware driving behavior classification.
Published in Under Preparation, 2025
In this research, we propose a novel approach to enhancing the U-Net architecture for brain tumor segmentation using BRATS dataset. By integrating Weighted Inception Module and Adaptive Distributed Attention Mechanism, our proposed Dynamic U-Net achieves superior segmentation accuracy and computational efficiency compared to traditional U-Net variants.