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Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

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Frontiers of Computer Science 2021年第6期15卷 165-173页

作者： Xia-an BI Yiming XIE Hao WU Luyun XU Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha 410081China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China Business School of Hunan Normal University Changsha 410081China

Mild cognitive impairment(MCI)as the potential sign of serious cognitive decline could be divided into two stages,i.e.,late MCI(LMCI)and early MCI(EMCI).Although the different cognitive states in the MCI progression have been clinically defined,effective and accurate identification of differences in neuroimaging data between these stages still needs to be further *** this paper,a new method of clustering-evolutionary weighted support vector machine ensemble(CEWSVME)is presented to investigate the alterations from cognitively normal(CN)to EMCI to *** CEWSVME mainly includes two *** first step is to build multiple SVM classifiers by randomly selecting samples and *** second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs,thereby improving the final classification ***,we extracted the optimal features to detect the differential brain regions in MCI progression,and confirmed that these differential brain regions changed dynamically with the development of *** exactly,this study found that some brain regions only have durative effects on MCI progression,such as parahippocampal gyrus,posterior cingulate gyrus and amygdala,while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the *** work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.

关键词： machine learning MCI progression optimal feature extraction differential brain regions functional magnetic resonance imaging

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation 48

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Wenjing Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc Beijing100085 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods. © 2023 IEEE.

关键词： Convolutional neural network Deep refinement Multi-decision Ultrasound image segmentation

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An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 658-670页

作者： Yang Jia Haijuan Wang Weiguang Chen Yagang Wang Bin Yang School of Computer Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anShaanxiChina Department of Radiology Xi'an Honghui HospitalXi'anChina

Fracture is one of the most common and unexpected *** not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve *** computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection ***,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult *** this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture *** authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local *** authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art *** authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

关键词： attention mechanism cascade R-CNN fracture detection X-ray image

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Online optimization scheduling for scientific workflows with deadline constraint on hybrid clouds

Online optimization scheduling for scientific workflows with...

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作者： Lin, Bing Guo, Wenzhong Lin, Xiuyan School of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China

The tremendous parallel computing ability of cloud computing encourages investigators to research its drawbacks and advantages on processing large-scale scientific applications such as workflows. The current cloud market is composed of numerous diverse public clouds and a local private cloud, and workflow scheduling is one of the biggest challenges on hybrid clouds due to the highly fragmented cloud market with respect to service provisions, pricing models, and bandwidths. In this paper, we propose an online-scheduling strategy for continuous submitted scientific workflows on hybrid clouds, which aims to complete the deadline-constrained applications as more as possible at a lower price. Firstly, a hierarchical iterative application partition (HIAP) algorithm is proposed to partition the application into a set of dependent tasks. Moreover, many online-scheduling algorithms cooperated with HIAP are presented to finish the workflows with a low average payment. Our strategy takes into account the basic characteristics on hybrid clouds such as bandwidth constraints, data transfer cost and computational cost. Various well-known workflows are used for evaluating the multiple scheduling algorithms and the results show that the MLF_ID approach can achieve a promising performance. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Data transfer

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Attack intent analysis method based on attack path graph 9

Attack intent analysis method based on attack path graph

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9th International Conference on Communication and network Security, ICCNS 2019

作者： Chen, Biqiong Liu, Yanhua Li, Shijin Gao, Xiaoling College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

With the rapid development of network technology, network security problems are gradually increasing, and the network attack situation is very severe. In a complex attack scenario, timely detection of potential attack behaviors and timely identification and pre-judgment of attack intentions are important components of security risks. However, the attack behavior in the network presents complexity, multi-step and uncertainty, which brings new technical challenges to attack intent analysis. Aiming at the problem that the attack intention of multi-step complex attack is difficult to identify, this paper proposes an attack intention analysis method based on attack path graph. Firstly, aiming at the multi-step complex attack behavior analysis problem, the key asset assessment technology is used to find out the key assets in the network system, and the hypothetical attack intention is generated according to the security protection requirements of the network system. Then, it is difficult to manually construct the attack path map in the large-scale network, and the automatic generation of the attack path map is realized. Finally, a method of network attack intent identification is proposed and a calculation method of attack intent probability is designed, which improves the efficiency and accuracy of attack intent recognition. © 2019 Association for computing Machinery.

关键词： Uncertainty analysis

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.

关键词： Reinforcement learning

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A comprehensive assessment method of asset importance for key asset protection 9

A comprehensive assessment method of asset importance for ke...

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9th International Conference on Communication and network Security, ICCNS 2019

作者： Gao, Xiaoling Liu, Yanhua Qiu, Yanbin Liu, Huajun College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

In a given system network, an important prerequisite for security risk control is how to accurately calculate the impact of different host assets in the topology on the spread of attack risk. In this regard, we propose a comprehensive asset importance assessment model for critical asset protection. First, the importance of local static assets in the topology was evaluated using three methods: degree centrality, mediation centrality, and closeness centrality. At the same time, PageRank was used to assess the importance of local dynamic assets for asset level changes. By combining the above two methods, and using K-CORE to evaluate the core position of the host in the network topology as the global weight indicator of the importance of assets. The comprehensive evaluation results of the importance of topology-oriented assets was finally obtained, and the validity of the model was verified through the experiment of the simulation ER random network. © 2019 Association for computing Machinery.

关键词： network topology

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A services development approach for smart home based on natural language instructions 31

A services development approach for smart home based on natu...

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31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Chen, Yiyan Liu, Zhanghui Huang, Zhiming Hu, Chuanshumin Chen, Xing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)1891706489

With development of the infrastructures supporting smart home which has entered in a new stage featured by intelligent services. The services based on natural language instructions are aimed at simplifying and improving our lives. To customize and develop these services more efficiently, this paper proposes an approach to model and execute services based on natural language instructions at runtime which introduces the knowledge graph into development process. Firstly, a concept model of knowledge graph is introduced for smart home services. Secondly, we put forward the mechanism to construct the instance of knowledge graph for smart home services. Finally, rules of transformation are provided to achieve mapping from natural language instructions to the services of knowledge graph. We evaluate our model on a prototype system and the experimental results show that our approach can develop services at runtime and effectually reduce the lines of code by 90%. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Software engineering

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Text Guided Road Marking Point Cloud Completion 6

Text Guided Road Marking Point Cloud Completion

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： He, Xu Xu, Qihong Luo, Huan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350388374

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.

关键词： Road and street markings

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Syntactic and Semantic Features Based Relation Extraction in Agriculture Domain 15th

Syntactic and Semantic Features Based Relation Extraction in...

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15th Web information Systems and Applications Conference, WISA 2018

作者： Liu, Zhanghui Chen, Yiyan Dai, Yuanfei Guo, Chenhao Zhang, Zuwen Chen, Xing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030029333

Relation extraction plays an important role in many natural language processing tasks, such as knowledge graph and question answering system. Up to the present, many of the former relation methods work directly on the raw word sequence, so it is often subject to a major limitation: the lack of semantic information, which leads to the problem of the wrong category. This paper presents a novel method to extract relation from Chinese agriculture text by incorporating syntactic parsing feature and word embedding feature. This paper uses word embedding to capture the semantic information of the word. On the basis of the traditional method, this paper integrates the dependency parsing, the core predicate and word embedding features, using the naive Bayes model, support vector machine (SVM) and decision tree to build experiment. We use the websites knowledge base to construct a dataset and evaluate our approach. Experimental results show that our proposed method achieves good performance on the agriculture dataset. The dataset and the word vectors trained by Word2Vec are available at Github (https://***/A-MuMu/*** ). © 2018, Springer Nature Switzerland AG.

关键词： Agriculture

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