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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Li, Shuhua Samonte, Mary Jane C. Yan, Fenglong School of Information Technology Mapua University Manila Philippines School of Computer Science and Technology Guangdong University of Science and Technology Dongguan China School of Computer & Software Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350385991

With the rapid development of deep learning, the medical image analysis method has become one of the most concerned issues in the field of medical imaging diagnosis. Medical image analysis and processing are essential for effective treatment and excellent prognosis of medical lesions. In this paper, High-definition computed tomography (CT) images of multi-type lesions are analyzed. To solve the problem of insufficient medical images, Mosaic is used within DeepLesion database for data combination and enhancement. In addition, images from COVID-CT are incorporated into the dataset. Concerning data noise, different kinds of filtering methods are tested and noise removal is performed using non-local mean filtering. Based on deep separable convolution (DSC), the accuracy of the training network is improved by using the enhanced dataset. The experimental results show that the average accuracy of the model is 80.5% at 29.23 frames per second, which satisfies the real-time detection. It provides a feasibility study for the practical application of computer-aided diagnosis. © 2024 IEEE.

关键词： computerized tomography

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Land cover change detection with hyperspectral remote sensing images: A survey

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information Fusion 2025年 123卷

作者： Lv, Zhiyong Zhang, Ming Sun, Weiwei Lei, Tao Benediktsson, Jón Atli Liu, Tongfei Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Province Xi'An710048 China The department of Geography and Spatial Information Techniques Ningbo University Zhejiang Province NingBo315211 China The School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Shaanxi Province Xi'An710021 China The Faculty of Electrical and Computer Engineering The Rocket Force University of Engineering Reykjavik Iceland

Land cover change detection with hyperspectral remote sensing images (HyperCD), which focuses on monitoring land cover change on the Earth's surface, has attracted increasing attention due to its advantages and wide applications in capturing detailed information. Over the past two decades, significant progress has been made in HyperCD, with numerous methods successfully applied to real-world datasets. However, a comprehensive survey on the current state of HyperCD remains lacking. To advance the future research of HyperCD, this study presents a comprehensive survey of the published papers on HyperCD between 2000 and 2023. In this survey, the existing studies were analyzed from different perspectives, including the problems and challenges of HyperCD, open-access resource datasets and code analysis, a detailed taxonomy of major techniques for HyperCD, and potential future research directions. The primary objective of this survey is to provide peer researchers a "one-stop" understanding, offering a clear and systematic overview of the current challenges, major techniques, opportunities, and future research direction for HyperCD. Compared with the current studies about HyperCD, the main contribution of this survey lies in addressing the lack of comprehensive reviews within the HyperCD research community. © 2025 Elsevier B.V.

关键词： Surveying

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Gradient based restoration of coal mine images obtained by underground wireless transmissions

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Mining science and technology 2011年第6期21卷 809-813页

作者： Lu Zhaolin Qian Jiansheng Li Leida School of Computer Science and Technology China University of Mining ＆ Technology Xuzhou 221008 China School of Information and Electrical Engineering China University of Mining ＆ Technology Xuzhou 221008 China

Curvature-driven diffusion (CDD) principles were used to develop a novel gradient based image restora- tion algorithm. The algorithm fills in blocks of missing data in a wireless image after transmission through the network. When images are transmitted over fading channels, especially in the severe circum- stances of a coal mine, blocks of the image may be destroyed by the effects of noise. Instead of using com- mon retransmission query protocols the lost data is reconstructed by using the adaptive curvature-driven diffusion (ACDD) image restoration algorithm in the gradient domain of the destroyed image. Missing blocks are restored by the method in two steps: In step one, the missing blocks are filled in the gradient domain by the ACDD algorithm; in step two, and the image is reconstructed from the reformed gradients by solving a Poisson equation. The proposed method eliminates the staircase effect and accelerates the convergence rate. This is demonstrated by experimental results.

关键词： Image restoration Curvature-driven diffusion Gradient domain Wireless image transmission Poisson equation

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A Derivative Matrix-Based Covert Communication Method in Blockchain

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computer Systems science & Engineering 2023年第7期46卷 225-239页

作者： Xiang Zhang Xiaona Zhang Xiaorui Zhang Wei Sun Ruohan Meng Xingming Sun School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China School of Hydrology and Water Resources Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Key Laboratory of Hydrometeorological Disaster Mechanism andWarning ofMinistry ofWater Resources Nanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) NanjingUniversity of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert ***,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication ***,this paper proposes a derivation matrix-based covert communication method in *** uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of ***,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real ***,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption *** results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

关键词： Covert communication blockchain concealment security capacity derivation

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Learning label-specific features for decomposition-based multi-class classification

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Frontiers of computer science 2023年第6期17卷 101-110页

作者： Bin-Bin JIA Jun-Ying LIU Jun-Yi HANG Min-Ling ZHANG College of Electrical and Information Engineering Lanzhou University of TechnologyLanzhou 730050China School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Ministry of Education) Southeast UniversityNanjing 210096China

Multi-class classification can be solved by decomposing it into a set of binary classification problems according to some encoding rules,e.g.,one-vs-one,one-vs-rest,error-correcting output *** works solve these binary classification problems in the original feature space,while it might be suboptimal as different binary classification problems correspond to different positive and negative *** this paper,we propose to learn label-specific features for each decomposed binary classification problem to consider the specific characteristics containing in its positive and negative ***,to generate the label-specific features,clustering analysis is respectively conducted on the positive and negative examples in each decomposed binary data set to discover their inherent information and then label-specific features for one example are obtained by measuring the similarity between it and all cluster *** clearly validate the effectiveness of learning label-specific features for decomposition-based multi-class classification.

关键词： machine learning multi-class classification error-correcting output codes label-specific features

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Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis

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computers, Materials & Continua 2018年第6期55卷 419-433页

作者： Shengqun Fang Zhiping Cai Wencheng Sun Anfeng Liu Fang Liu Zhiyao Liang Guoyan Wang College of Computer National University of Defense TechnologyChangsha 410073China Computer Science and Technology Central South UniversityChangsha 999078China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China Faculty of Information Technology Macao University of Science and TechnologyMacao 410073MO. Xuzhou University of Technology Xuzhou 221002China

By using efficient and timely medical diagnostic decision making,clinicians can positively impact the quality and cost of medical ***,the high similarity of clinical manifestations between diseases and the limitation of clinicians’knowledge both bring much difficulty to decision making in ***,building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical *** this paper,we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories,and compare this method with the traditional medical expert system to verify the *** select the best subset of patient features,we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square *** evaluate the feature selection methods and diagnostic models from two aspects,false negative rate(FNR)and *** experiments have conducted on a real-world Chinese electronic medical record *** evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods,and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system.

关键词： Medical expert system EMR multi-label classification feature selection class discriminative degree

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Adaptive sparse and dense hybrid representation with nonconvex optimization

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Frontiers of computer science 2020年第4期14卷 65-78页

作者： Xuejun WANG Feilong CAO Wenjian WANG School of Computer Science and Technology Shanxi UniversityTaiyuan030006China School of Science China Jiliang UniversityHangzhou310018China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Taiyuan030006China

Sparse representation has been widely used in signal processing,pattern recognition and computer vision *** achievements have been made in both theoretical researches and practical ***,there are two limitations on the application of *** is that sufficient training samples are required for each class,and the other is that samples should be *** order to alleviate above problems,a sparse and dense hybrid representation(SDR)framework has been proposed,where the training dictionary is decomposed into a class-specific dictionary and a non-class-specific *** putsℓ1 constraint on the coefficients of class-specific ***,it over-emphasizes the sparsity and overlooks the correlation information in class-specific dictionary,which may lead to poor classification *** overcome this disadvantage,an adaptive sparse and dense hybrid representation with non-convex optimization(ASDR-NO)is proposed in this *** trace norm is adopted in class-specific dictionary,which is different from general *** doing so,the dictionary structure becomes adaptive and the representation ability of the dictionary will be ***,a non-convex surrogate is used to approximate the rank function in dictionary decomposition in order to avoid a suboptimal solution of the original rank minimization,which can be solved by iteratively reweighted nuclear norm(IRNN)*** experiments conducted on benchmark data sets have verified the effectiveness and advancement of the proposed algorithm compared with the state-of-the-art sparse representation methods.

关键词： sparse representation trace norm nonconvex optimization low rank matrix recovery iteratively reweighted nuclear norm

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Irregular sleep pattern identification and analysis from social media dataset using hybrid deep learning based attention mechanism

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Data and information Management 2025年

作者： Islam, Mohammed Jawwadul Rafi, Mohammad Fahad Al Podder, Pranto Siddika, Aysha Kabir, Moumy Khan, Euna Mehnaz Islam, Najmul Mukta, Saddam Department of Computer Science and Engineering United International University (UIU) Bangladesh Web Communication Design Information Technology University of Southern Denmark Denmark Department of Computer and Systems Sciences Stockholm University Sweden Department of Computer Science and Engineering University of Minnesota United States LUT School of Engineering Science LUT University Finland

Following an irregular bedtime routine and having different amounts of sleep each night might increase a person's risk of obesity, cardiovascular problems, high blood pressure, insulin levels, and other metabolic problems. Similarly, in recent times, social media platforms have gained popularity among users for sharing their interests, thoughts, and opinions. Through social media activities, researchers have been able to mine the text data generated on these platforms to investigate and understand users' behaviors and habits. In this paper, we examine a total of 2,468,697 tweets to identify users' irregular sleeping patterns (ISP) using psycholinguistic and contextual features from their tweets. We conduct a linguistic analysis to understand the factors influencing users' psychological behavior and word use patterns, and find a correlation with their irregular sleeping patterns. We observe that users who have irregular sleeping patterns use anger, anxiety, death, and future categories of words in their tweets largely. In contrast, users with irregular sleeping patterns tend to use positive emotions, family, and other categories of words in their tweets. Building upon our findings, we develop a hybrid prediction model that predicts users' irregular sleeping patterns from psycholinguistic features with an accuracy of 91%. We examine the application of social media data for the early identification of irregular sleep patterns and their related mental and psychological concerns while investigating design prospects for future health technologies to enhance the monitoring and support of healthy sleep behavior. © 2025

关键词： Attention mechanism Deep learning Irregular sleeping patterns Psycholinguistic features Social media analysis Tweet analysis

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Quantum Remote State Preparation Based on Quantum Network Coding

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computers, Materials & Continua 2022年第10期73卷 119-132页

作者： Zhen-Zhen Li Zi-Chen Li Yi-Ru Sun Haseeb Ahmad Gang Xu Xiu-Bo Chen School of Information Engineering Beijing Institute of Graphic CommunicationBeijing102600China School of Computer Science and Information Engineering Zhejiang Gongshang UniversityHangzhou310018China Information Security Center State key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Department of Computer Science National Textile UniversityFaisalabad37610Pakistan School of Information Science and Technology North China University of TechnologyBeijing100144China Advanced Cryptography and System Security Key Laboratory of Sichuan Province China

As an innovative theory and technology,quantum network coding has become the research hotspot in quantum network *** this paper,a quantum remote state preparation scheme based on quantum network coding is *** with the general quantum remote state preparation schemes,our proposed scheme brings an arbitrary unknown quantum state finally prepared remotely through the quantum network,by designing the appropriate encoding and decoding steps for quantum network *** is worth mentioning,from the network model,this scheme is built on the quantum k-pair network which is the expansion of the typical bottleneck network—butterfly ***,it can be treated as an efficient quantum network preparation scheme due to the characteristics of network coding,and it also makes the proposed scheme more applicable to the large-scale quantum *** addition,the fact of an arbitrary unknown quantum state remotely prepared means that the senders do not need to know the desired quantum ***,the security of the proposed scheme is ***,this scheme can always achieve the success probability of 1 and 1-max flow of value ***,the communication efficiency of the proposed scheme is ***,the proposed scheme turns out to be practicable,secure and efficient,which helps to effectively enrich the theory of quantum remote state preparation.

关键词： Quantum remote state preparation quantum network coding quantum k-pair network

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Research on SOA and component oriented technology in development of large system

Research on SOA and component oriented technology in develop...

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2010 International Symposium on Computational Intelligence and Design, ISCID 2010

作者： Li, Baoan Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9780769541983

SOA is a new methodology to be used in the software development in recently. But the interrelated standards and specifications about SOA are very complicated and confused for most users. After analyzing the misapprehending on the application of SOA, it gives some reasoning and suggestion for the uses of SOA, and shows a sample about the light-weight SOA product model. A new approach based on SOA and Web service component oriented technologies has been proposed and applied in the refinery enterprise. The relevant technologies about SOA and Web service to realize ERP and other large systems have been researched in detail. © 2010 IEEE.© 2010 IEEE.

关键词： Web services

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