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7th International Conference on information Science and control Engineering, ICISCE 2020

作者： Huang, Jiayan Yu, Zhaochai Li, Zuoyong Zheng, Xiangpan Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9781728164069

Dust storm not only reduces visibility of natural scene, but also affects capture capability of outdoor monitoring equipment. Image dedusting aims to remove dust storm in images and make image scenes clearer. However, most of existing image enhancement-based methods have focused on defogging and deraining, and have paid little attentions to image dedusting. Therefore, research of image dedusting is necessary. In this paper, we presented an end-to-end network for single image dedusting. First, the proposed method uses an encoder module to encode a dusty image into image feature maps. Second, it uses the latest smoothed dilated residual convolution module to alleviate gridding artifacts caused by a widely-used dilated convolution. Third, it adopts an attention-based module to fuse image features at different levels and extract more contextual image information. Finally, it utilizes a decoder module, which decodes the feature maps, to obtain a residue, and adds it to the input dusty image to obtain the final image dedusting result. Experiments demonstrate the effectiveness of the proposed method in image dedusting. © 2020 IEEE.

关键词： Convolution

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural Computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Detecting community structures in social networks with particle swarm optimization

Detecting community structures in social networks with parti...

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2nd CCF Internet Conference of China, ICoC 2013

作者： Chen, Yuzhong Qiu, Xiaohui Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University China

ISBN: (纸本)9783642539589

Community detection in social networks is usually considered as an objective optimization problem. Due to the limitation of the objective function, the global optimum cannot describe the real partition well, and it is time consuming. In this paper, a novel PSO (particle swarm optimization) algorithm based on modularity optimization for community detection in social networks is proposed. Firstly, the algorithm takes similarity-based clustering to find core areas in the network, and then a modified particle swarm optimization is performed to optimize modularity in a new constructed weighted network which is compressed from the original one, and it is equivalent to optimize modularity in the original network with some restriction. Experiments are conducted in the synthetic and four real-world networks. The experimental results show that the proposed algorithm can effectively extract the intrinsic community structures of social networks. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Particle swarm optimization (PSO)

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis intelligent prediction Improved CNN

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An automatic generation method of cross-modal fuzzy creativity

An automatic generation method of cross-modal fuzzy creativi...

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作者： Zhang, Fuquan Wang, Yiou Wu, Chensheng Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China Beijing Institute of Science and Technology Information Beijing China

Digital creativity is creative expression derived from cultural creativity and information technology. In order to overcome the problem in the creative generation in the condition of fuzzy and uncertain ideas, an automatic generation method of cross-modal fuzzy creativity (AGMCFC) is proposed. In this subject, fuzzy creative data sets and learning retrieval network are constructed for the sake of extracting original creative data effectively. And the logical correlations between creative objects are acquired dynamically based on the graph neural network. Creative objects and creative styles are generated by using generative adversarial nets technology and style transfer technology, respectively. Then, the projectiles, boundary markers and location words of the creative scene objects are generated by analyzing related attributes of each entity. After adjusting the layout, creative works are automatically generated. A fuzzy creative generating environment is implemented. Experimental results show that the screened number of AGMCFC method is about twice as much as that of manual method, and the accuracy rate of AGMCFC method is improved compared with the manual method. AGMCFC method performs well at creative generation of fuzzy ideas automatically. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Graph neural networks

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Robust Monocular Visual Odometry using Optical Flows for Mobile Robots 35

Robust Monocular Visual Odometry using Optical Flows for Mob...

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第35届中国控制会议

作者： LI Haifeng HU Zunhe CHEN Xinwei College of Computer Science and Technology Civil Aviation University of China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Department of Computer Science Minjiang University

ISBN: (纸本)9781509009107

A robust optical flow-based visual odometry method using a single onboard camera is proposed in this *** improve the quality of the noisy optical flows,a correction method across multiple frames is ***,the optical flows in the plane at infinity are detected and removed as these optical flows have very low signal to noise ratio for robot translation ***,a RANSAC approach for robot ego-motion estimation is *** experiments are carried out and the results show that the proposed method is able to estimate the camera trajectory robustly with reasonable accuracy.

关键词： visual odometry optical flows mobile robot

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Customer churn warning with machine learning 5th

Customer churn warning with machine learning

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5th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2018

作者： Wen, Zuotian Yan, Jiali Zhou, Liya Liu, Yanxun Zhu, Kebin Guo, Zhu Li, Yan Zhang, Fuquan Bank of China Beijing100091 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjing University Fuzhou350121 China

ISBN: (纸本)9783030037659

Customer churn refers to the phenomenon of suspension of cooperation between customers and enterprises due to the implementation of various marketing methods. The customer churn warning refers to revealing the customer churn pattern hidden behind the data by analyzing the payment behavior, business behavior and basic attributes of the customer within a certain period of time, predicting the probability of the customer’s loss in the future and the possible reasons, and then guide the company to carry out customer retention work. After the forecast, the system can list the possible lost customers. And then the marketers can conduct precise marketing and improve marketing success rate. In this paper, we present a algorithm named Customer Churn Warning (CCW) to alert customers to churn. © Springer Nature Switzerland AG 2019.

关键词： Machine learning

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A novel method of detecting microangioma in retinal image based on visual characteristics 10

A novel method of detecting microangioma in retinal image ba...

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10th International Conference on information Technology in Medicine and Education, ITME 2019

作者： Cao, Xinrong Lin, Hongkai Kang, Hongliang College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (纸本)9781728139173

Diabetic retinopathy (DR), an ocular complication of diabetes could threaten vision, has become one of the common causes of blindness in adults' working age. The detection of microangioma (MA), the first feature and persists with the development of DR, is an important step in the diagnosis and follow-up of DR. However, due to complexity of retinal image and the randomness of abnormal lesions, the detection of MA is more susceptible to the influence of background texture noise and other structures in retinal image. A novel method for detecting MA based on visual features is proposed. It establishes an observation function and uses multi-range mean filtering to eliminate texture noise and vascular network. Then, the detection target is confirmed by the visual shape features. A series of tests in the different public retinal images databases show that the proposed method is effective and would be useful for computer-aided diagnosis of early screening of DR. © 2019 IEEE.

关键词： Computer aided diagnosis

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SDUW-Net: An Effective Retinal Vessel Segmentation Model 1

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3rd International Conference on Machine Learning for Cyber Security, ML4CS 2020

作者： Lin, Hongkai Kang, Hongliang Cao, Xinrong College of Computer and Control Engineering Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

ISBN: (数字)9783030624637

ISBN: (纸本)9783030624620

Retinal vessel segmentation is the main step in the analysis of fundus images. However, gray-scales’ uneven distribution, complex structure, and serious noise interference bring difficulties of automatic retinal vessels segmentation on fundus images. To solve these problems, we present an effective retinal vessel segmentation model, SDUW-Net, in this paper. The same scale dense connection is designed to improve U-Net’s structure and remove different scales dense connections to accelerate the training speed. We use skip connections to merges the features between shallow layers and deep layers to retain more features that may be lost in the process of down sampling and convolution. Experimental results on the DRIVE dataset show that the retinal vessels can be effectively segmented out by our proposed SDUW-Net, which has AUC of 0.9811 with low computation and short training time. © 2020, Springer Nature Switzerland AG.

关键词： Ophthalmology

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A comparative investigation of citation content characteristics between academic monographs and papers 16

A comparative investigation of citation content characterist...

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16th International Conference on Scientometrics and Informetrics, ISSI 2017

作者： Yuzhuo, Wang Chengzhi, Zhang Nanjing University of Science and Technology Nanjing China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Cited frequency of academic papers (APs) or academic monographs (AMs) are used to evaluate their academic influence usually. This frequency-based method does not consider detailed citation information, e.g. citation content or context, in citing paper. Citation context or content can reveal different citing motivation and behavior of authors. Citation content analysis (CCA) of APs or AMs can improve performance of their influence assessment. AMs are different from APs and they have characteristics in many ways. If we know the difference of citation content between AMs and APs, a more appropriate CCA method for AMS will be explored based on the existing CCA method of APs. Therefore, a comparison of citation content between AMs and APs is investigated in this paper. We annotated 39 academic monographs with 13, 539 citation sentences and 7, 756 academic papers with 590, 740 citation sentences respectively. Furthermore, we compared the location, mention times and length of citation content between AMs and APs and analyzed cause of the difference. The results quantitatively show that there are differences in location and length of citation between AMs and APs, while citation mention of the two is similar.

关键词： Academic monograph Academic paper Characteristic of citation content

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