检索结果-内蒙古大学图书馆

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Mingyang Wang Ying Lu Quanyuan Xu College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration on Southwest Forestry University Kunming China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Spodoptera frugiperda (fall armyworm, FAW) is a pest that poses a significant threat to global agriculture, with its larvae exhibiting unique morphological characteristics and varying degrees of harm at different instar stages. This study proposes a method that incorporates an improved self-attention mechanism to achieve rapid and accurate identification of FAW larval instars. This method can be seamlessly integrated into convolutional neural networks (CNNs), enhancing recognition performance by focusing on the global features of images. Furthermore, this research has constructed an image dataset of FAW larvae at various developmental stages under field and laboratory conditions to validate the effectiveness of the proposed method. Experimental results demonstrate that our method improves the recognition accuracy across various CNN architectures, achieving accuracy enhancements of 5.49%, 7.38%, 5.79%, and 5.59% on Alexnet, vgg16, resnet50, and resnet101, respectively. Our approach provides technicians with a convenient, intelligent, and precise tool to identify FAW larval instar stages.

关键词： Image segmentation Accuracy Image resolution Image recognition Memory management Lighting Pattern recognition Convolutional neural networks Residual neural networks Image classification

来源：评论

学校读者我要写书评

暂无评论

GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

引用

International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

来源：评论

学校读者我要写书评

暂无评论

Identification of key Genes as Potential Drug Targets for Gastric Cancer

引用

Tsinghua Science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

来源：评论

学校读者我要写书评

暂无评论

CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

CoGAP: A Personalized Federated Learning Method Using Collaborative Optimization for Medical Image Classification

CoGAP: A Personalized Federated Learning Method Using Collab...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Shenhai Li, Congyu Wen, Sian Gao, Xi Yu, Lei College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing China Emergency Department The Second Affiliated Hospital of Chongqing Medical University Chongqing China

ISBN: (纸本)9798350368741

Federated learning (FL) has been widely used in medical image processing to protect data privacy, but it has issues with data heterogeneity. Personalized federated learning have emerged to tackle these issues but often focuses too much on personalized models at the expense of global models. To address these problem, we propose a personalized federated learning method CoGAP, using collaborative optimization to enhance both personalized and global model performance. On the client side, it employs adaptive weight aggregation to initialize personalized models and uses a training strategy based on L2 regularization. On the server side, it implements a gradient accumulation-based aggregation method. These modules facilitate a collaborative optimization process where the global model guides the personalized models, and the personalized models provide positive feedback to the global model, ultimately leading to mutual improvement for both. We conducted extensive comparative experiments on the OCT2017 dataset, as well as the BloodMnist and PathMnist (subsets from MedMnist), to evaluate both global and personalized models. Results show that CoGAP own a unique strength which not only outperforms other personalized federated learning methods in personalization capability while also remarkably achieving satisfying global generalization. Code is available at https://***/lcyCQUPT/CoGAP. © 2025 IEEE.

关键词： collaborative optimization federated learning medical image classification

来源：评论

学校读者我要写书评

暂无评论

Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

引用

18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Dual-Branch Attention Transformer for Visual Commonsense Reasoning

Dual-Branch Attention Transformer for Visual Commonsense Rea...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Xuerui Ma Zongwen Bai Meili Zhou Yiqun Gao Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Visual Commonsense Reasoning (VCR) is a challenging task that requires a model to select the correct answer in the context of a given image and question, while also providing a reasonable explanation to justify the chosen answer. In this paper, we propose a novel dual-branch attention network model that effectively models dense intra-modal and inter-modal interactions. Our model utilizes a self-attention mechanism to attend to visual and textual information independently, while employing a bidirectional guided attention mechanism to facilitate effective interaction between different modalities, thus correlating visual information with textual information. By stacking multiple dual-branch attention modules (DBAs), our model is capable of deeply exploring data features and extracting more powerful and informative prior features. By achieving a common focus on visual and textual information, we enhance the subtlety of our understanding and significantly improve our ability to perform visual reasoning. Our comprehensive and qualitative assessments on the public VCR dataset illustrate that our methodology surpasses most models, providing a new solution for Visual Commonsense Reasoning tasks.

关键词： Visualization Accuracy Computational modeling Stacking Semantics Feature extraction Transformers data models Commonsense reasoning Video recording

来源：评论

学校读者我要写书评

暂无评论

Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in intelligent Digital Healthcare 18th

Resource Binding and Module Placement Algorithms for Conti...

引用

18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yang, Zhongliao Huang, Huichang Liu, Zeyi Dong, Chen Xu, Li College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Biochips

来源：评论

学校读者我要写书评

暂无评论

Collaborative Optimization of Truck-Drone Based on Multi-agent Reinforcement Learning 11th

Collaborative Optimization of Truck-Drone Based on Multi-a...

引用

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Zhang, Wenhao Wang, Guoyin Liu, Qun Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing 401331 China

ISBN: (纸本)9783031927409

This study proposes a novel Multi-Head Attention-enhanced Proximal Policy Optimization (MHA-PPO) algorithm for collaborative truck-drone delivery systems in dynamic logistics scenarios. To address the limitations of traditional multi-agent reinforcement learning in handling complex spatiotemporal dependencies and real-time decision-making, we integrate a multi-head attention mechanism into the PPO framework, enabling adaptive feature fusion of heterogeneous agent observations (e.g., drone positions, truck routes, and package urgency levels). A key innovation lies in the design of a variable emergency mechanism that models a random urgency generation module and dynamically adjusts task priorities based on the urgency of customer demand for packages. The system is implemented in a high-fidelity Unity simulation environment incorporating 3D urban terrain, realistic drone aerodynamics, and traffic flow patterns. Comparative experiments against baseline algorithms (PPO, Multi-Agent Proximal Policy Optimization (MAPPO), Multi-Head Attention Deep Deterministic Policy Gradient (MHA-DDPG)) demonstrate that our MHA-PPO achieves superior performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-agent problem Reinforcement learning Task allocation Truck-drone cooperative delivery

来源：评论

学校读者我要写书评

暂无评论

A New Model Based on Differential Evolutionary Algorithm and Markov Clustering for Identifying Protein Complexes

A New Model Based on Differential Evolutionary Algorithm and...

引用

Chinese Control Conference (CCC)

作者： Zengyu Feng Shouheng Tuo Tianrui Chen School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an Shaanxi China

As the material basis for all life activities, proteins play a crucial role in performing life activities. Most real-life proteins perform various functions in the form of protein complexes, so it is essential for understanding of life activities to accurately identify protein complexes. Most of the existing approaches learn protein interactions directly from protein topology, ignoring the biological characteristics of the actual proteins. Nevertheless, actual protein complexes are usually composed of protein cores and protein attachments. In this study, a new MP-DE algorithm is designed from the core-attachment structure to generate protein complex cores using Markov clustering, while searching for attached proteins in the second-order neighborhood of core protein complexes based on differential evolution(DE) algorithm. The experimental results show that the proposed method has high detection accuracy and efficiency on the current mainstream protein-protein interaction(PPI) database.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：