Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (22,113)
  • Open Access

    ARTICLE

    Online Burst Events Detection Oriented Real-Time Microblog Message Stream

    Guozhong Dong1,2,*, Jun Gao3, Liang Huang4, Chunlei Shi1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 213-225, 2019, DOI:10.32604/cmc.2019.05601

    Abstract The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table.… More >

  • Open Access

    ARTICLE

    A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

    Hangjun Zhou1,2,*, Guang Sun1,3, Sha Fu1, Wangdong Jiang1, Juan Xue1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 179-192, 2019, DOI:10.32604/cmc.2019.05214

    Abstract With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach for fraud detection in online… More >

  • Open Access

    ARTICLE

    Super-Resolution Reconstruction of Images Based on Microarray Camera

    Jiancheng Zou1,*, Zhengzheng Li1, Zhijun Guo1, Don Hong2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 163-177, 2019, DOI:10.32604/cmc.2019.05795

    Abstract In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array… More >

  • Open Access

    ARTICLE

    A High Gain, Noise Cancelling 3.1-10.6 GHz CMOS LNA for UWB Application

    Xiaorong Zhao1, Hongjin Zhu1, Peizhong Shi1, Chunpeng Ge2, Xiufang Qian1,*, Honghui Fan1, Zhongjun Fu1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 133-145, 2019, DOI:10.32604/cmc.2019.05661

    Abstract With the rapid development of ultra-wideband communications, the design requirements of CMOS radio frequency integrated circuits have become increasingly high. Ultra-wideband (UWB) low noise amplifiers are a key component of the receiver front end. The paper designs a high power gain (S21) and low noise figure (NF) common gate (CG) CMOS UWB low noise amplifier (LNA) with an operating frequency range between 3.1 GHz and 10.6 GHz. The circuit is designed by TSMC 0.13 μm RF CMOS technology. In order to achieve high gain and flat gain as well as low noise figure, the circuit uses many technologies. To improve… More >

  • Open Access

    ARTICLE

    A Novel Reversible Data Hiding Scheme Based on Lesion Extraction and with Contrast Enhancement for Medical Images

    Xingxing Xiao1, Yang1,*, Rui Li2, Weiming Zhang3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 101-115, 2019, DOI:10.32604/cmc.2019.05293

    Abstract The medical industry develops rapidly as science and technology advance. People benefit from medical resource sharing, but suffer from privacy leaks at the same time. In order to protect patients’ privacy and improve quality of medical images, a novel reversible data hiding (RDH) scheme based on lesion extraction and with contrast enhancement is proposed. Furthermore, the proposed scheme can enhance the contrast of medial image's lesion area directly and embed high-capacity privacy data reversibly. Different from previous segmentation methods, this scheme first adopts distance regularized level set evolution (DRLSE) to extract lesion and targets at the lesion area accurately for… More >

  • Open Access

    ARTICLE

    Defense Strategies Against Network Attacks in Cyber-Physical Systems with Analysis Cost Constraint Based on Honeypot Game Model

    Wen Tian1, Xiaopeng Ji1,*, Weiwei Liu1, Guangjie Liu1, Rong Lin1,2, Jiangtao Zhai3, Yuewei Dai3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 193-211, 2019, DOI:10.32604/cmc.2019.05290

    Abstract Cyber-physical system (CPS) is an advanced system that integrats physical processes, computation and communication resources. The security of cyber-physical systems has become an active research area in recent years. In this paper, we focus on defensive strategies against network attacks in CPS. We introduce both low- and highinteraction honeypots into CPS as a security management tool deliberately designed to be probed, attacked and compromised. In addition, an analysis resource constraint is introduced for the purpose of optimizing defensive strategies against network attacks in CPS. We study the offensive and defensive interactions of CPS and model the offensive and defensive process… More >

  • Open Access

    ARTICLE

    An Improved MDS-MAP Localization Algorithm Based on Weighted Clustering and Heuristic Merging for Anisotropic Wireless Networks with Energy Holes

    Jing Wang1,*, Xiaohe Qiu1, Yuanfei Tu1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 227-244, 2019, DOI:10.32604/cmc.2019.05281

    Abstract The MDS-MAP (multidimensional scaling-MAP) localization algorithm utilize almost merely connectivity information, and therefore it is easy to implement in practice of wireless sensor networks (WSNs). Anisotropic networks with energy hole, however, has blind communication spots that cause loss of information in the merging phase of MDSMAP. To enhance the positioning accuracy, the authors propose an MDS-MAP (CH) algorithm which can improve the clustering and merging strategy. In order to balance the effect of energy consumption and the network topology stabilization, we present a weighted clustering scheme, which considers the residual energy, the degree of connectivity nodes and node density. As… More >

  • Open Access

    ARTICLE

    Relation Extraction for Massive News Texts

    Libo Yin1, Xiang Meng2, Jianxun Li3, Jianguo Sun2,*

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 275-285, 2019, DOI:10.32604/cmc.2019.05556

    Abstract With the development of information technology including Internet technologies, the amount of textual information that people need to process daily is increasing. In order to automatically obtain valuable and user-informed information from massive amounts of textual data, many researchers have conducted in-depth research in the area of entity relation extraction. Based on the existing research of word vector and the method of entity relation extraction, this paper designs and implements an method based on support vector machine (SVM) for extracting English entity relationships from massive news texts. The method converts sentences in natural language into a form of numerical matrix… More >

  • Open Access

    ARTICLE

    Improved Fully Convolutional Network for Digital Image Region Forgery Detection

    Jiwei Zhang1, Yueying Li2, Shaozhang Niu1,*, Zhiyi Cao1, Xinyi Wang1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 287-303, 2019, DOI:10.32604/cmc.2019.05353

    Abstract With the rapid development of image editing techniques, the image splicing behavior, typically for those that involve copying a portion from one original image into another targeted image, has become one of the most prevalent challenges in our society. The existing algorithms relying on hand-crafted features can be used to detect image splicing but unfortunately lack precise location information of the tampered region. On the basis of changing the classifications of fully convolutional network (FCN), here we proposed an improved FCN that enables locating the spliced region. Specifically, we first insert the original images into the training dataset that contains… More >

  • Open Access

    ARTICLE

    Network Embedding-Based Anomalous Density Searching for Multi-Group Collaborative Fraudsters Detection in Social Media

    Chengzhang Zhu1, 2, Wentao Zhao2, *, Qian Li1, Pan Li2, Qiaobo Da3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 317-333, 2019, DOI:10.32604/cmc.2019.05677

    Abstract Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects… More >

Displaying 18981-18990 on page 1899 of 22113. Per Page