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  • Open Access

    ARTICLE

    Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature

    Anjie Peng1, Kang Deng1, Shenghai Luo1, Hui Zeng1, 2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2217-2231, 2020, DOI:10.32604/cmc.2020.011006 - 16 September 2020

    Abstract The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we… More >

  • Open Access

    ARTICLE

    Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone

    Qiqiang Chen1, *, Xinxin Gan2, Wei Huang1, Jingjing Feng1, H. Shim3

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191 - 16 September 2020

    Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales More >

  • Open Access

    ARTICLE

    Adaptive Binary Coding for Scene Classification Based on Convolutional Networks

    Shuai Wang1, Xianyi Chen2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2065-2077, 2020, DOI:10.32604/cmc.2020.09857 - 16 September 2020

    Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed More >

  • Open Access

    ARTICLE

    Study on Multi-Label Classification of Medical Dispute Documents

    Baili Zhang1, 2, 3, *, Shan Zhou1, Le Yang1, Jianhua Lv1, 2, Mingjun Zhong4

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1975-1986, 2020, DOI:10.32604/cmc.2020.010914 - 16 September 2020

    Abstract The Internet of Medical Things (IoMT) will come to be of great importance in the mediation of medical disputes, as it is emerging as the core of intelligent medical treatment. First, IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution. Second, IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff. This information includes recommendation of similar historical cases, guidance for medical treatment, alerting of hired dispute profiteers etc. The multi-label classification of medical dispute documents (MDDs) plays an important… More >

  • Open Access

    ARTICLE

    Analysis of Feature Importance and Interpretation for Malware Classification

    Dong-Wook Kim1, Gun-Yoon Shin1, Myung-Mook Han2, *

    CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1891-1904, 2020, DOI:10.32604/cmc.2020.010933 - 16 September 2020

    Abstract This study was conducted to enable prompt classification of malware, which was becoming increasingly sophisticated. To do this, we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified. Initially, the analysis features were extracted using Cuckoo Sandbox, an open-source malware analysis tool, then the features were divided into five categories using the extracted information. The 804 extracted features were reduced by 70% after selecting only the most suitable ones for malware classification using a learning model-based feature selection More >

  • Open Access

    ARTICLE

    A Multi-Label Classification Method for Vehicle Video

    Yanqiu Cao1, Chao Tan1, Genlin Ji1, *

    Journal on Big Data, Vol.2, No.1, pp. 19-31, 2020, DOI:10.32604/jbd.2020.01003 - 07 September 2020

    Abstract In the last few years, smartphone usage and driver sleepiness have been unanimously considered to lead to numerous road accidents, which causes many scholars to pay attention to autonomous driving. For this complexity scene, one of the major challenges is mining information comprehensively from massive features in vehicle video. This paper proposes a multi-label classification method MCM-VV (Multi-label Classification Method for Vehicle Video) for vehicle video to judge the label of road condition for unmanned system. Method MCM-VV includes a process of feature extraction and a process of multi-label classification. During feature extraction, grayscale, lane… More >

  • Open Access

    REVIEW

    A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis

    Wan Azani Mustafa1,*, Afiqah Halim1, Khairul Shakir Ab Rahman2

    Oncologie, Vol.22, No.2, pp. 53-63, 2020, DOI:10.32604/oncologie.2020.013660

    Abstract Cervical cancer develops as cells transformation in the cervix of a female that connects the uterus to the vagina. This cancer may impact the columnal epithelial cells of the cervix and therefore can be expanded to the lymphatic and circulatory system (metastasize), sometimes the kidneys, liver, prostate, vagina, and rectum. Many of the cervical cancer patients survived by taking early prevention by undergoing a Pap Smear Test. However, the result of the test usually takes a few weeks which is extremely time-consuming especially at the government hospital. The purpose of this research was to study… More >

  • Open Access

    ARTICLE

    Classification-Based Fraud Detection for Payment Marketing and Promotion

    Shuo He1,∗, Jianbin Zheng1,†, Jiale Lin2,‡, Tao Tang1,§, Jintao Zhao1,¶, Hongbao Liu1,ll

    Computer Systems Science and Engineering, Vol.35, No.3, pp. 141-149, 2020, DOI:10.32604/csse.2020.35.141

    Abstract Nowadays, many payment service providers use the discounts and other marketing strategies to promote their products. This also raises the issue of people who deliberately take advantage of such promotions to reap financial benefits. These people are known as ‘scalper parties’ or ‘econnoisseurs’ which can constitute an underground industry. In this paper, we show how to use machine learning to assist in identifying abnormal scalper transactions. Moreover, we introduce the basic methods of Decision Tree and Boosting Tree, and show how these classification methods can be applied in the detection of abnormal transactions. In addition,… More >

  • Open Access

    ARTICLE

    Classifications of Stations in Urban Rail Transit based on the Two-step Cluster

    Wei Li1, 2, 3, Min Zhou1, *, Hairong Dong1

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 531-538, 2020, DOI:10.32604/iasc.2020.013930

    Abstract Different types of stations have different functional roles in the urban rail transit network. Firstly, based on the characteristics of the urban rail transit network structure, the time series features and passenger flow features of the station smart card data are extracted. Secondly, we use the principal component analysis method to select the suitable clustering variables. Finally, we propose a station classification model based on the two-step cluster method. The effectiveness of the proposed method is verified in the Beijing subway. The results show that the proposed model can successfully identify the types of urban More >

  • Open Access

    ARTICLE

    Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification

    R. Uma Maheswari1,*, R. Umamaheswari2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 479-488, 2020, DOI:10.32604/iasc.2020.013924

    Abstract To enhance the predictive condition-based maintenance (CBMS), a reliable automatic Drivetrain fault detection technique based on vibration monitoring is proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at different spatial locations to measure the vibration from multiple vibration sources. In this work, multi-channel signals are fused and monocomponent modes of oscillation are reconstructed by the Multivariate Empirical Mode Decomposition (MEMD) Technique. Noise assisted methodology is adapted to palliate the mixing of modes with common frequency scales. The instantaneous amplitude envelope and instantaneous frequency are estimated with the Hilbert transform. Low order and high More >

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