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  • Open Access


    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


    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


    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 >

  • Open Access


    A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

    Assia Maamar1,*, Khelifa Benahmed2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

    Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to… More >

  • Open Access


    A Learning Based Brain Tumor Detection System

    Sultan Noman Qasem1,2, Amar Nazar3, Attia Qamar4, Shahaboddin Shamshirband5,6,*, Ahmad Karim4

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 713-727, 2019, DOI:10.32604/cmc.2019.05617

    Abstract Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor. More >

  • Open Access


    Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images Based on Temporal and Spatial Data

    Zhe Liu1, Charlie Maere1,*, Yuqing Song1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

    Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes… More >

  • Open Access


    An Early Warning System for Curved Road Based on OV7670 Image Acquisition and STM32

    Xiaoliang Wang1, *, Wenhua Song1, Bowei Zhang1, Brandon Mausler2, Frank Jiang1, 3

    CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 135-147, 2019, DOI:10.32604/cmc.2019.05687

    Abstract Nowadays, the number of vehicles in China has increased significantly. The increase of the number of vehicles has also led to the increasingly complex traffic situation and the urgent safety measures in need. However, the existing early warning devices such as geomagnetic, ultrasonic and infrared detection have some shortcomings like difficult installation and maintenance. In addition, geomagnetic detection will damage the road surface, while ultrasonic and infrared detection will be greatly affected by the environment. Considering the shortcomings of the existing solutions, this paper puts forward a solution of early warning for vehicle turning meeting based on image acquisition and… More >

  • Open Access


    Frequency Shift Curve Based Damage Detection Method for Beam Structures

    Y. Zhang1,2, Z.H. Xiang1,3

    CMC-Computers, Materials & Continua, Vol.26, No.1, pp. 19-36, 2011, DOI:10.3970/cmc.2011.026.019

    Abstract Vibration based damage detection methods play an important role in the maintenance of beam structures such as bridges. However, most of them require the accurate measurement of structural mode shapes, which may not be easily satisfied in practice. Since the measurement of frequencies is more accurate than that of mode shapes, this paper proposes a frequency shift curve (FSC) method, based on the equivalence between the FSC due to auxiliary mass and the mode shape square, which has been demonstrated to be effective in structural damage detection. Two damage indices based on the FSC are developed, which are called the… More >

  • Open Access


    Fast Near-duplicate Image Detection in Riemannian Space by A Novel Hashing Scheme

    Ligang Zheng1,*, Chao Song2

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 529-539, 2018, DOI: 10.3970/cmc.2018.03780

    Abstract There is a steep increase in data encoded as symmetric positive definite (SPD) matrix in the past decade. The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices, which we sometimes call SPD manifold. One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix. Hashing is a popular method that can be used for the nearest neighbor search. However, hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry. Inspired by the idea… More >

  • Open Access


    SMK-means: An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

    Bo Xiao1, Zhen Wang2, Qi Liu3,*, Xiaodong Liu3

    CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 365-379, 2018, DOI: 10.3970/cmc.2018.01830

    Abstract In recent years, the rapid development of big data technology has also been favored by more and more scholars. Massive data storage and calculation problems have also been solved. At the same time, outlier detection problems in mass data have also come along with it. Therefore, more research work has been devoted to the problem of outlier detection in big data. However, the existing available methods have high computation time, the improved algorithm of outlier detection is presented, which has higher performance to detect outlier. In this paper, an improved algorithm is proposed. The SMK-means is a fusion algorithm which… More >

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