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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Chen, Xiaoyan Zheng, Ziwen Liu, Liangxia Wang, Weili Lin, Qiang Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

SPECT imaging is widely used for displaying lesions of bone metastasis in nuclear medicine. With SPECT images in RGB format, in this work, we propose to develop automated classification models based on VGG framework, which can answer that whether or not an image contain lesions. Experimental evaluation conducted on a set of real-world data of SPECT imaging shows that the VGG13 model can effectively classify bone metastasis SPECT images, obtaining a value of 0.992, 0.99, 0.99 and 0.95 for accuracy Acc, precision Pre, recall Rec and F-1 scores, respectively. © 2021 IEEE.

关键词： Nuclear medicine

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Research on Arthritis Detection Method for SPECT Nuclear Medicine Bone Imaging 3

Research on Arthritis Detection Method for SPECT Nuclear Med...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Wang, Weili Cao, Yongchun Lin, Qiang Man, Zhengxing Li, Tongtong Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

SPECT bone imaging is an effective method for early diagnosis of bone metastases from malignant tumors. In addition to bone metastases, arthritis and other diseases will also appear as abnormal metabolic distribution areas in SPECT images. Early diagnosis of arthritis based on SPECT bone imaging is of great significance for the treatment of patients. This article takes SPECT bone imaging as the object to study and construct an arthritis disease classifier based on convolutional neural network. Firstly, data preprocessing is carried out to deal with the impact of small amount of medical image data and individual differences between imaging data on the classification effect of deep learning model. Secondly, a classifier suitable for low resolution and large-scale SPECT images is constructed based on VGGNet. Finally, based on a set of real SPECT bone imaging data sets to compare the arthritis diagnosis results of different depth classification models. The experimental results show that the classifier constructed in this paper can effectively detect arthritis diseases, and the accuracy, recall and F-1 values are 0.9309, 0.9511 and 0.9284, respectively. © 2021 IEEE.

关键词： Diseases

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CNN-Based Classifier for Diagnosing SPECT Bone Images 3

CNN-Based Classifier for Diagnosing SPECT Bone Images

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Guo, Yanru Chen, Xiaoyan Zheng, Ziwen Lin, Qiang School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

To assist nuclear medicine doctors for quickly and accurately identifying bone metastases, in this work, we develop a convolutional neural network to automatically classify whole-body SPECT bone scans. First, the original data of SPECT bone scans are augmented by using geometric transformation. Second, two deep model VGG5 and VGG7 are developed by following the famous VGG model, where a 2\times 1 maximum pooling is introduced before the first layer convolution. Experimental evaluation conducted on real-world SPECT images shows that the developed models are workable to classify SPECT images into categories of concern. © 2021 IEEE.

关键词： Convolutional neural networks

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Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale for Diagnosing Alzheimer’s Disease

Coordinate Attention Based 3D-CNN Using Ghost Multi-Scale fo...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaolu Lin Pinya Lu Jie Pan Hongqin Yang Xuemei Ding Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Provincial University Engineering Research Center for Big Data Analysis and Application Fujian Normal University Fuzhou China Xiamen Jingyi Zhikang Technology Co. Ltd Xiamen China Fujian Provincial Key Laboratory for Photonics Technology Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fuzhou China School of Computing Engineering and Intelligent Systems Ulster University Derry-Londonderry NI UK

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Alzheimer's disease (AD) is a neurodegenerative disease and mild cognitive impairment (MCI) is the early stage of AD. Previous studies have predominantly focused on binary classification using 3 dimensional - convolutional neural network (3D-CNN) for AD diagnosis, with limited progress in multi-classification. Moreover, the current 3D-CNNs often adopt a single-scale architecture with massive parameters growth. Additionally, obtaining precise location information of brain imaging data is crucial for improving the classification accuracy with 3D-CNN. Hence, we propose a multi-scale 3D-CNN based on coordinate attention mechanism to marvelously capture and integrate 3D features with fewer parameters, improving the accuracy of AD diagnosis. A total of 447 cognitively normal (CN), 512 MCI, and 358 AD sMRI images from the Alzheimer's Disease Neuroimaging Initiative datasets are used for multi-class classification task, yielding a classification accuracy of 92.8%. The model merely involves 2.41 M parameters and achieves the best classification results with the least number of parameters when compared to other representative CNN architectures including ResNet 18, ResNet 34, ConvNeXt tiny, and VGG 11. Through the ablation experiment, the addition of attention mechanism and the multi-scale classification enhances the classification performance by 4.5% and 1.5%, respectively. Furthermore, our model outperforms the other six existing studies in terms of accuracy for classifying AD vs. MCI vs. CN. Overall, this study underscores the efficacy of our approach for AD diagnosis, showcasing its utility in diagnosing AD patients and providing novel insights for diagnosing other neurological disorder diseases.

关键词： Neuroimaging Accuracy Attention mechanisms Three-dimensional displays Resonance Convolutional neural networks Alzheimer's disease

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Adjacent vertex distinguishing incidence coloring of semi-strong product of paths

Adjacent vertex distinguishing incidence coloring of semi-st...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Peng, Lizhen Tian, Shuangliang Lu, Qiqi Northwest Minzu University School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Lanzhou China

ISBN: (纸本)9781728186689

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers. © 2020 IEEE.

关键词： Coloring

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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2020 International Conference on Computer Science and Management Technology, ICCSMT 2020

作者： Lu, Qiqi Tian, Shuangliang Peng, Lizhen Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University School of Mathematics and Computer Science Lanzhou China

ISBN: (纸本)9781728186689

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined. © 2020 IEEE.

关键词： Coloring

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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin University of Electronic Technology Guilin541004 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology Guangxi Nanning530001 China College of Computing and Data Science Nanyang Technological University 639798 Singapore School of Electronic and Information Engineering The Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beihang University Beijing100191 China

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV’s flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents’ observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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