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Dynamic Graph Transformer for Brain Disorder Diagnosis

IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4388-4400页

作者： Shehzad, Ahsan Zhang, Dongyu Yu, Shuo Abid, Shagufta Xia, Feng Dalian University of Technology School of Software Technology Dalian116620 China Dalian University of Technology School of Foreign Languages School of Software Technology Dalian116024 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Dynamic brain networks play a pivotal role in diagnosing brain disorders by capturing temporal changes in brain activity and connectivity. Previous methods often rely on sliding-window approaches for constructing these networks using fMRI data. However, these methods face two key limitations: a fixed temporal length that inadequately captures brain activity dynamics and a global spatial scope that introduces noise and reduces sensitivity to localized dysfunctions. These challenges can lead to inaccurate brain network representations and potential *** address these challenges, we propose BrainDGT, a dynamic Graph Transformer model designed to enhance the construction and analysis of dynamic brain networks for more accurate diagnosis of brain disorders. BrainDGT leverages adaptive brain states by deconvolving the Hemodynamic Response Function (HRF) within individual functional brain modules to generate dynamic graphs, addressing the limitations of fixed temporal length and global spatial scope. The model learns spatio-temporal local features through attention mechanisms within these graphs and captures global interactions across modules using adaptive fusion. This dual-level integration enhances the model's ability to analyze complex brain connectivity patterns. We validate BrainDGT's effectiveness through classification experiments on three fMRI datasets (ADNI, PPMI, and ABIDE), where it outperforms state-of-the-art methods. By enabling adaptive, localized analysis of dynamic brain networks, BrainDGT advances neuroimaging and supports the development of more precise diagnostic and treatment strategies in biomedical research. © 2013 IEEE.

关键词： Functional neuroimaging

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Research on Image Defogging Algorithm Based on Improved FFA-Net

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 634-641页

作者： Qinrong, Li Chi, Ma Qiang, Guo Hui, Hu School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Images captured under severe weather conditions, such as haze and fog, suffer from image quality degradation caused by atmospheric particle diffusion. This degradation manifests as color fading, reduced contrast, and adversely affects the performance of various computer vision tasks. To address this, this paper presents an end-to-end feature fusion attention network (FFA-Net) designed to directly restore haze-free images. By incorporating the SSIM loss into the original loss function, the proposed method effectively captures the visual disparities between the estimated defogged image and the authentic haze-free image. Additionally, it mitigates the color distortion problem inherent in the original algorithm. To address the challenge of low brightness in input images, a low illumination enhancement module is introduced, seamlessly integrated with the FFA-Net defogging method. Subsequently, a comparative analysis of different defogging algorithms is conducted using two distinct foggy datasets. Multiple evaluation metrics are employed to assess the performance of these algorithms. The findings indicate that our algorithm significantly outperforms others in terms of objective indicators such as PSNR and SSIM, as well as visual effects. © (2024), (International Association of Engineers). All rights reserved.

关键词： Image enhancement

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An Apricot Detection Algorithm in Complex Environments Based on Improved YOLOv7

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2135-2144页

作者： Guo, Qiang Ma, Chi Hu, Hui School of Computer Science and Software Engineering University of Science and Technology LiaoNing AnShan114051 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

Apricot detection is a prerequisite for counting and harvesting tasks. Existing algorithms face challenges in adapting to the impacts of complex environmental factors such as lighting variations, shadows, dense foliage, and the uneven distribution of samples in mechanized apricot harvesting. This paper proposes an enhanced model, YOLOv7-DC, based on YOLOv7, to address these challenges. YOLOv7-DC preprocesses diverse apricot tree samples to accommodate real-world harvesting detection scenarios. To improve model inference speed and detection accuracy, the detection network is redesigned with a new feature fusion method. DCNv2 is embedded within the efficient layer aggregation network (ELAN), and PConv is introduced to replace conventional convolutions, reducing the parameter impact of DCNv2. The training process incorporates the CBAM attention mechanism to enhance spatial and channel information. The ConvMixer architecture captures spatial and channel relationships transmitted to the detection head through the attention mechanism, improving the model’s detection accuracy for each specific classification sample. Experimental results show that YOLOv7-DC maintains good detection speed and recognition rates across various classification tasks. The improved model achieves a 6.2% increase in average detection accuracy compared to previous algorithms, with a 13% reduction in model parameters. YOLOv7-DC is better suited for handling imbalanced samples and complex environmental scenarios. © (2024), (International Association of Engineers). All rights reserved.

关键词： Apricot biloba detection Attention mechanism Feature fusion YOLOv7

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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE Engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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Multi-View Ensemble Clustering Algorithm Weighted by Kernel Density Estimation 17

Multi-View Ensemble Clustering Algorithm Weighted by Kernel ...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Hu, Hong Li, Xuejun Liao, Jing School of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9798331507398

Most of the existing ensemble clustering algorithms improve the performance by weighting the basic clusters to reduce the influence of low-quality basic clusters on the final clustering results. Low-quality base clustering can be understood as misclassifying sample points, which are presented as discrete points in the co-association matrix. So, we proposed a multi-view ensemble clustering algorithm based on the weighting of kernel density estimation, starting from the density distribution of discrete points in the co-association matrix. Firstly, the different sets of basic clusters are made into a co-association matrix using the evidence accumulation model. Secondly, we converted the co-association matrix into a sparse matrix, and then we calculated the density distribution weights of the nonzero elements in the matrix using kernel density estimation. Next we let these weights multiply with the sparse matrix. Thirdly, we reduce the weighted sparse matrix to the shape of the initial co-association matrix to obtain the density-weighted co-association matrix. Next, we use the K-means clustering algorithm on the weighted co-association matrix to obtain the final clustering results. Finally, the algorithm is subjected to comparison experiments and ablation experiments on five commonly used datasets. The experimental results show that the proposed ensemble clustering algorithm with kernel density estimation weighting performs better than other comparative algorithms. © 2024 IEEE.

关键词： Matrix algebra

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A U-Net-Based Multi-Scale Attention Neural Network for Removing EEG Artifacts 17

A U-Net-Based Multi-Scale Attention Neural Network for Remov...

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Chen, Xingyun Su, Bo Liu, Minxian Jiang, Xianwei Wei, Mingzhi Nie, Yutang School of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9798331507398

Electroencephalography (EEG) is a highly random and nonlinear time series signal, and it is easily affected by other physiological artifacts. The interference from various physiological artifacts is detrimental to subsequent EEG signal analysis. Therefore, denoising EEG signals is a crucial step in EEG analysis. In recent years, deep learning-based methods have shown superior performance compared to traditional methods in removing EEG artifacts. This paper proposes a U-Net-based multi-scale attention neural network to eliminate ocular and electromyographic artifacts in single-channel EEG signals. This network is divided into multiple modules based on different sizes of convolution kernels, utilizing our designed multi-scale gated convolution interaction module, allowing our model to focus on the reconstruction information of the contaminated segments. After passing through the multi-scale gated convolution interaction module, the data is downsampled by channel summation to enhance features. Then, through a U-Net-based convolution interaction module, which consists of several denoising modules connected in parallel at different scales and built on one-dimensional convolutions, we achieve the final reconstruction of the EEG signal through feature fusion and dimensionality reduction using a linear layer. Various experiments validate that our proposed network model can reconstruct clean EEG time series, achieving good performance on two quantitative metrics (relative root mean square error and correlation coefficient) compared to other representative models. © 2024 IEEE.

关键词： Signal denoising

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HASHL: Dynamic Hash Verification for Detecting and Preventing Eclipse Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 23524-23535页

作者： He, Daojing Gong, Wei Tu, Chen Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong

With the rapid development of blockchain technology, P2P networks are facing increasing security threats, among which Eclipse attacks, as a type of network isolation attack, have seriously affected the normal operation of the network and the integrity of data. To address this challenge, this study implements node authentication and dynamic reputation evaluation by leveraging a dynamic hash computation mechanism that integrates challenge strings, node identifiers, and the latest active time, ensuring the uniqueness of node identities and the authenticity of operations. Based on a dynamic hash chain behavior evaluation mechanism, node behaviors are quantified across three dimensions: integrity, consistency, and temporal consistency, enabling precise identification of anomalous nodes. Furthermore, a network prevention repository framework is proposed, which dynamically adjusts the trust index of nodes by combining historical behavior with real-time data, effectively detecting and defending against stealthy Eclipse attacks. In addition, extensive testing on both Bitcoin and Ethereum platforms has shown that the method proposed in this study not only can effectively coexist on these two platforms, but also significantly improves the security and stability of the network, effectively reducing the occurrence of Eclipse attacks. © 2014 IEEE.

关键词： Blockchain

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MANet: A Deep Learning Framework for Flow Field Prediction 17

MANet: A Deep Learning Framework for Flow Field Prediction

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17th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Xiao, LiWei Chen, YongHui Wang, FuPan Fan, Shuai School of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9798331507398

Traditional methods for predicting airfoil flow fields primarily rely on computational fluid dynamics (CFD) simulations and wind tunnel experiments. However, solving the N avier-Stokes (NS) equations typically requires significant memory and computational cost, while wind tunnel experiments demand substantial human and financial resources. This presents a bottleneck for large-scale engineering designs, such as aerodynamic shape optimization. Deep learning models offer a highly efficient solution for rapid flow field simulation by significantly reducing costs while maintaining high accuracy. A novel deep learning model, which integrates convolutional attention mechanisms and multiple decoders into a modified U-N et network, has been proposed to further enhance model precision and lightweight performance. This model reduces parameter size by 75%, improves feature extraction capability, and mitigates the mutual influence between multiple prediction variables. Comparative experiments show that this approach reduces parameter size by about 42 %, mean absolute error (MAE) by 20%, and mean squared error (MSE) by 12% compared to the traditional U-Net network. © 2024 IEEE.

关键词： Shape optimization

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Robust style injection for person image synthesis

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CAAI Transactions on Intelligence technology 2025年第2期10卷 402-414页

作者： Yan Huang Jianjun Qian Shumin Zhu Jun Li Jian Yang School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingChina School of Fashion and Textiles The Hong Kong Polytechnic UniversityHong KongChina

Person Image Synthesis has been widely used in fashion with extensive application *** point of this task is how to synthesise person image from a single source image under arbitrary *** methods generate the person image with target pose well;however,they fail to preserve the fine style details of the source *** address this problem,a robust style injection(RSI)model is proposed,which is a coarse-to-fine framework to synthesise target the person *** develops a simple and efficient cross-attention based module to fuse the features of both source semantic styles and target pose for achieving the coarse aligned *** adaptive instance normalisation is employed to enhance the aligned features in conjunction with source semantic ***,source semantic styles are further injected into the positional normalisation scheme to avoid the fine style details erosion caused by massive *** training losses,optimal transport theory in the form of energy distance is introduced to constrain data distribution to refine the texture style ***,the authors’model is capable of editing the shape and texture of garments to the target style *** experiments demonstrate that the authors’RSI achieves better performance over the state-of-art methods.

关键词： computer vision image reconstruction virtual try‐on

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Attention Temperature Matters in ViT-Based Cross-Domain Few-Shot Learning 38

Attention Temperature Matters in ViT-Based Cross-Domain Few-...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yixiong Ma, Ran Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology China

Cross-domain few-shot learning (CDFSL) is proposed to transfer knowledge from large-scale source-domain datasets to downstream target-domain datasets with only a few training samples. However, Vision Transformer (ViT), as a strong backbone network to achieve many top performances, is still under-explored in the CDFSL task in its transferability against large domain gaps. In this paper, we find an interesting phenomenon of ViT in the CDFSL task: by simply multiplying a temperature (even as small as 0) to the attention in ViT blocks, the target-domain performance consistently increases, even though the attention map is downgraded to a uniform map. In this paper, we delve into this phenomenon for an interpretation. Through experiments, we interpret this phenomenon as a remedy for the ineffective target-domain attention caused by the query-key attention mechanism under large domain gaps. Based on it, we further propose a simple but effective method for the CDFSL task to boost ViT's transferability by resisting the learning of query-key parameters and encouraging that of non-query-key ones. Experiments on four CDFSL datasets validate the rationale of our interpretation and method, showing we can consistently outperform state-of-the-art methods. Our codes are available at https://***/Zoilsen/Attn_Temp_CDFSL. © 2024 Neural information processing systems foundation. All rights reserved.

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