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Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM

International Journal of Digital Earth 2023年第1期16卷 3601-3617页

作者： Wenbing Wu Tariq Sadad Mejdl Safran Sultan Alfarhood Xiaojian Yuan Big Data College Fuzhou University of International Studies and TradeFuZhouPeople’s Republic of China Department of Computer Science University of Engineering&TechnologyMardanPakistan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral ***,their ability to model relationships between data is *** convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid *** have *** computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale *** have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the *** addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs ***,support vector machine(SvM)is employed instead of softmax in the classification *** techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.

关键词： CNN hyperspectral images MiniGCN SVM

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Multi-Task Learning Network Optimization Based on Weight Adaptive 11

Multi-Task Learning Network Optimization Based on Weight Ada...

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11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Cao, Zizhuo Li, Yidong Ding, Chuntao Zhang, Zhuxi School of Computer Science and Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing100044 China General Logistics Information Center Beijing100036 China

ISBN: (纸本)9798331531904

Multi-task learning has emerged as a significant topic in artificial intelligence research, where a singular network model performs numerous tasks. This approach simultaneously processes multiple related tasks and shared knowledge, enhancing model generalization while increasing efficiency. This methodology provides innovative solutions to complex real-world problems. However, the single-model-based approach for multi-task learning suffers from inter-task interference in practice. Therefore, the exploration of more efficient multi-task learning strategies, aimed at balancing the synergy and conflict among tasks and minimizing the dependence on computational resources, is pivotal for the field's future progress. We propose a strategy that autonomously adjusts both the parameters and structures of the model to alleviate gradient interference in multi-task learning. This method includes integrating a lightweight, weight-adaptive module that enhances the network's ability to process tasks by optimally balancing parameter sharing and isolation. This adaptation enables the model to share common features across tasks while allocating distinct spaces for each task, thereby reducing interference. Our extensive experimental validation indicates that our framework surpasses other multi-task learning approaches, achieving joint optimization of tasks more effectively. This enhancement not only bolsters performance but also maintains an equilibrium between accuracy and inference speed. © 2024 IEEE.

关键词： Multi-task learning

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Subgroup-Adaptive Network for Robust Virtual Patient-Specific Quality Assurance 20

Subgroup-Adaptive Network for Robust Virtual Patient-Specifi...

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20th International Computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Zeng, Xiaoyang Ahmed, Awais Tunio, Muhammad Hanif Chengxiao, Yan Hou, Mengshu Xi, Rui School of Computer Science and Engineering University of Electronic Science and Technology of China - UESTC Sichuan611731 China School of Big Data and Artificial Intelligence Chengdu Technological University Sichuan611730 China

ISBN: (纸本)9798350318982

Deep learning-based automatic patient-specific quality assurance (PSQA) alleviates medical resource pressure and ensures the safety of patient treatment plans by predicting the actual dosage difference distribution or metrics. However, deep learning is often affected by extremely unbalanced data distributions, and in clinical practice, the number of normal plans is always much larger than that of abnormal plans. On the other hand, the distribution of patients' lesion sites could be more balanced. The performance of models is often seriously biased in these subgroups, posing a challenge to the reliability of deep learning in the clinical application of PSQA. In this work, we propose a subgroup-adaptive network to address the problem of subgroup drift in PSQA. Our experiments achieved a state-of-the-art (SOTA) MAE of 0.69. Our proposed work can significantly improve the reliability of deep learning in the practical clinical application of PSQA. © 2023 IEEE.

关键词： Quality assurance

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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A Novel Text Relation Extraction Model Based on PCNNs with Dynamic Learning Rates

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第4期7卷 921-934页

作者： He, Chen Yuan, Ying-Chun Liu, Zhen-Chao Guo, Jiang-Hua Department of Information Science and Technology Hebei Agricultural University No 289 Lingyusi Street Hebei Baoding China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural University China

Distant Supervised Relation Extraction (DSRE) is the mainstream relation extraction field, but most extraction models use a fixed learning rate, which leads to the model constantly learning noise labels when performing relation classification. PCNNs is widely used in distantly supervised relation extraction. In this paper, a model named ERNIE-DLRPCNNs is proposed, which combines pre-training model ERNIE and dynamic learning rate without fixed period into PCNNs. Globally, the convergence of the whole model is improved by gradient accumulation, and adds textual semantic representation with the ERNIE model. Locally, the whole training process is stratified by dynamic learning rate, and the learning rate of each layer is adjusted to improve the accuracy of the relational extraction model. When tested on the well-known "New York Times" (NYT) dataset, our ERNIE-DLRPCNNs model has a prediction accuracy of up to 82.0 percent, outperforming existing mainstream relational models such as GPCNNs in terms of P-R curve and AUC improvement. © 2022.

关键词： Extraction

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Research on Enhanced Perception and Attention-Driven Person Re-Identification 20

Research on Enhanced Perception and Attention-Driven Person ...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Wu, Cancan Li, Dong Zhang, Zhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan430065 China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9798350356328

When matching similarity among pedestrians in images, pedestrian re-identification algorithms are often disturbed by occlusions. A typical tactic is to improve the robustness of occlusion features in the model. However, this approach neglects the noise introduced by other pedestrians in the image, leading to limited learning of relevant information between images. To address these issues, this paper introduces a pedestrian re-identification model that leverages perception-enhanced and attention mechanisms. By enhancing the perception of non-pedestrian noise and erasing non-pedestrian features, we then employ a cross-attention mechanism to subtly embed global and local features for finer feature recognition. Finally, through context attention, we learn the interaction between pedestrian images to improve re-identification accuracy. Results from studies performed on the Market-1501, CUHK03-D, and Duke-ReID datasets indicate that the suggested model surpasses leading contemporary models regarding Rank-1 scores and mean Average Precision (mAP), while maintaining good robustness. © 2024 IEEE.

关键词： Reamers

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Towards Exploratory Query Optimization for Template-Based SQL Workloads 40

Towards Exploratory Query Optimization for Template-Based SQ...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Feng, Jieming Li, Zhanhuai Chen, Qun School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi'an China Key Laboratory of Big Data Storage and Management Ministry of Industry and Information Technology Northwestern Polytechnical University Shaanxi Xi'an China

ISBN: (纸本)9798350317152

SQL query optimization aims to choose an optimal Query Execution Plan (QEP) for a query. The existing optimizers usually choose the plan with the minimal execution cost. However, in some real scenarios (e.g., cloud OLAP), it is a business imperative to minimize the cost of an entire workload. Unfortunately, the greedy optimizers are prone to generating execution plans that are only suboptimal from the workload perspective. In this paper, we propose a novel QEP optimization approach for template-based SQL that aims to minimize the execution cost of an entire workload instead of individual queries. While choosing an execution plan for a query, the proposed approach considers a plan's impact on future query execution as well as its own execution cost. We first define an exploratory metric, Plan Exploration Value (PEV), to measure a plan's potential benefit to future query execution. Then, we model exploratory plan selection as an optimization problem with cost constraints, and present an efficient algorithm based on dynamic programming for its solution. Finally, we evaluate the performance of the proposed approach by a comparative study on benchmark workloads. Our extensive experiments have shown that it generates more efficient workload execution plans than the existing alternatives. © 2024 IEEE.

关键词： Dynamic programming

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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