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  • Open Access

    ARTICLE

    Research on the Application of Super Resolution Reconstruction Algorithm for Underwater Image

    Tingting Yang1, Shuwen Jia1, Hao Ma2, *

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1249-1258, 2020, DOI:10.32604/cmc.2020.05777

    Abstract Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of… More >

  • Open Access

    ARTICLE

    Empirical Comparisons of Deep Learning Networks on Liver Segmentation

    Yi Shen1, Victor S. Sheng1, 2, *, Lei Wang1, Jie Duan1, Xuefeng Xi1, Dengyong Zhang3, Ziming Cui1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1233-1247, 2020, DOI:10.32604/cmc.2020.07450

    Abstract Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases. In recent years, due to the great improvement of hard device, many deep learning based methods have been proposed for automatic liver segmentation. Among them, there are the plain neural network headed by FCN and the residual neural network headed by Resnet, both of which have many variations. They have achieved certain achievements in medical image segmentation. In this paper, we firstly select five representative structures, i.e., FCN, U-Net, Segnet, Resnet and Densenet, to investigate their performance on liver… More >

  • Open Access

    ARTICLE

    Prison Term Prediction on Criminal Case Description with Deep Learning

    Shang Li1, Hongli Zhang1, *, Lin Ye1, Shen Su2, Xiaoding Guo1, Haining Yu1, 3, Binxing Fang1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1217-1231, 2020, DOI:10.32604/cmc.2020.06787

    Abstract The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal case. Recent advances in deep learning frameworks inspire us to propose a two-step method to address this problem. To obtain a better understanding and more specific representation of the legal texts, we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment documents. By formalizing prison term prediction as a regression problem, we adopt the linear regression model and the neural network model to train… More >

  • Open Access

    ARTICLE

    Automatic Detection of Aortic Dissection Based on Morphology and Deep Learning

    Yun Tan1, #, Ling Tan2, #, Xuyu Xiang1, *, Hao Tang2, *, Jiaohua Qin1, Wenyan Pan1

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1201-1215, 2020, DOI:10.32604/cmc.2020.07127

    Abstract Aortic dissection (AD) is a kind of acute and rapidly progressing cardiovascular disease. In this work, we build a CTA image library with 88 CT cases, 43 cases of aortic dissection and 45 cases of health. An aortic dissection detection method based on CTA images is proposed. ROI is extracted based on binarization and morphology opening operation. The deep learning networks (InceptionV3, ResNet50, and DenseNet) are applied after the preprocessing of the datasets. Recall, F1-score, Matthews correlation coefficient (MCC) and other performance indexes are investigated. It is shown that the deep learning methods have much better performance than the traditional… More >

  • Open Access

    ARTICLE

    Classification and Research of Skin Lesions Based on Machine Learning

    Jian Liu1, Wantao Wang1, Jie Chen2, *, Guozhong Sun3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

    Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models… More >

  • Open Access

    ARTICLE

    A Performance Fault Diagnosis Method for SaaS Software Based on GBDT Algorithm

    Kun Zhu1, Shi Ying1, *, Nana Zhang1, Rui Wang1, Yutong Wu1, Gongjin Lan2, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1161-1185, 2020, DOI:10.32604/cmc.2020.05247

    Abstract SaaS software that provides services through cloud platform has been more widely used nowadays. However, when SaaS software is running, it will suffer from performance fault due to factors such as the software structural design or complex environments. It is a major challenge that how to diagnose software quickly and accurately when the performance fault occurs. For this challenge, we propose a novel performance fault diagnosis method for SaaS software based on GBDT (Gradient Boosting Decision Tree) algorithm. In particular, we leverage the monitoring mean to obtain the performance log and warning log when the SaaS software system runs, and… More >

  • Open Access

    ARTICLE

    Access Control Policy Based on Friend Circle

    Qin Liu1, Tinghuai Ma1, 2, *, Fan Xing1, Yuan Tian3, Abdullah Al-Dhelaan3, Mohammed Al-Dhelaan3

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1143-1159, 2020, DOI:10.32604/cmc.2020.04949

    Abstract Nowadays, the scale of the user’s personal social network (personal network, a network of the user and their friends, where the user we call “center user”) is becoming larger and more complex. It is difficult to find a suitable way to manage them automatically. In order to solve this problem, we propose an access control model for social network to protect the privacy of the central users, which achieves the access control accurately and automatically. Based on the hybrid friend circle detection algorithm, we consider the aspects of direct judgment, indirect trust judgment and malicious users, a set of multi-angle… More >

  • Open Access

    ARTICLE

    Stochastic Numerical Analysis for Impact of Heavy Alcohol Consumption on Transmission Dynamics of Gonorrhoea Epidemic

    Kamaleldin Abodayeh1, Ali Raza2, *, Muhammad Shoaib Arif2, Muhammad Rafiq3, Mairaj Bibi4, Muhammad Mohsin5

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1125-1142, 2020, DOI:10.32604/cmc.2020.08885

    Abstract This paper aims to perform a comparison of deterministic and stochastic models. The stochastic modelling is a more realistic way to study the dynamics of gonorrhoea infection as compared to its corresponding deterministic model. Also, the deterministic solution is itself mean of the stochastic solution of the model. For numerical analysis, first, we developed some explicit stochastic methods, but unfortunately, they do not remain consistent in certain situations. Then we proposed an implicitly driven explicit method for stochastic heavy alcohol epidemic model. The proposed method is independent of the choice of parameters and behaves well in all scenarios. So, some… More >

  • Open Access

    ARTICLE

    Stabilization for Equal-Order Polygonal Finite Element Method for High Fluid Velocity and Pressure Gradient

    T. Vu-Huu1, 2, C. Le-Thanh3, H. Nguyen-Xuan4, M. Abdel-Wahab5, 6, *

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1109-1123, 2020, DOI:10.32604/cmc.2020.07989

    Abstract This paper presents an adapted stabilisation method for the equal-order mixed scheme of finite elements on convex polygonal meshes to analyse the high velocity and pressure gradient of incompressible fluid flows that are governed by Stokes equations system. This technique is constructed by a local pressure projection which is extremely simple, yet effective, to eliminate the poor or even non-convergence as well as the instability of equal-order mixed polygonal technique. In this research, some numerical examples of incompressible Stokes fluid flow that is coded and programmed by MATLAB will be presented to examine the effectiveness of the proposed stabilised method. More >

  • Open Access

    ARTICLE

    High Speed Network Intrusion Detection System (NIDS) Using Low Power Precomputation Based Content Addressable Memory

    R. Mythili1, *, P. Kalpana2

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1097-1107, 2020, DOI:10.32604/cmc.2020.08396

    Abstract NIDS (Network Intrusion Detection Systems) plays a vital role in security threats to computers and networks. With the onset of gigabit networks, hardware-based Intrusion Detection System gains popularity because of its high performance when compared to the software-based NIDS. The software-based system limits parallel execution, which in turn confines the performance of a modern network. This paper presents a signature-based lookup technique using reconfigurable hardware. Content Addressable Memory (CAM) is used as a lookup table architecture to improve the speed instead of search algorithms. To minimize the power and to increase the speed, precomputation based CAM (PBCAM) can be used,… More >

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