Home / Journals / CMC / Vol.65, No.1, 2020
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  • Open AccessOpen Access

    ARTICLE

    Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

    Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104
    Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range… More >

  • Open AccessOpen Access

    ARTICLE

    V-Shaped Monopole Antenna with Chichena Itzia Inspired Defected Ground Structure for UWB Applications

    Naeem Ahmad Jan1, Saad Hassan Kiani2, Fazal Muhammad2, Daniyal Ali Sehrai2, Amjad Iqbal3, Muhammad Tufail4, Sunghwan Kim5, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 19-32, 2020, DOI:10.32604/cmc.2020.011091
    Abstract Due to rapid growth in wireless communication technology, higher bandwidth requirement for advance telecommunication systems, capable of operating on two or higher bands with higher channel capacities and minimum distortion losses is desired. In this paper, a compact Ultra-Wideband (UWB) V-shaped monopole antenna is presented. UWB response is achieved by modifying the ground plane with Chichen Itzia inspired rectangular staircase shape. The proposed V-shaped is designed by incorporating a rectangle, and an inverted isosceles triangle using FR4 substrate. The size of the antenna is 25 mm×26 mm×1.6 mm. The proposed V-shaped monopole antenna produces bandwidth response of 3 GHz Industrial,… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Control Measures of Stochastic Malaria Epidemic Model

    Muhammad Rafiq1, Ali Ahmadian2, *, Ali Raza3, Dumitru Baleanu4, Muhammad Sarwar Ahsan1, Mohammad Hasan Abdul Sathar5
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 33-51, 2020, DOI:10.32604/cmc.2020.010893
    Abstract Nonlinear stochastic modeling has significant role in the all discipline of sciences. The essential control measuring features of modeling are positivity, boundedness and dynamical consistency. Unfortunately, the existing stochastic methods in literature do not restore aforesaid control measuring features, particularly for the stochastic models. Therefore, these gaps should be occupied up in literature, by constructing the control measuring features numerical method. We shall present a numerical control measures for stochastic malaria model in this manuscript. The results of the stochastic model are discussed in contrast of its equivalent deterministic model. If the basic reproduction number is less than one, then… More >

  • Open AccessOpen Access

    ARTICLE

    Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis

    Joo-Chan Lee1, Hyun-Pyo Choi1, Jang-Hoon Kim1, Jun-Won Kim1, Da-Un Jung1, Ji-Ho Shin1, Jung-Taek Seo1, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 53-67, 2020, DOI:10.32604/cmc.2020.011251
    Abstract Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study,… More >

  • Open AccessOpen Access

    ARTICLE

    New Optimal Newton-Householder Methods for Solving Nonlinear Equations and Their Dynamics

    Syahmi Afandi Sariman1, Ishak Hashim1, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 69-85, 2020, DOI:10.32604/cmc.2020.010836
    Abstract The classical iterative methods for finding roots of nonlinear equations, like the Newton method, Halley method, and Chebyshev method, have been modified previously to achieve optimal convergence order. However, the Householder method has so far not been modified to become optimal. In this study, we shall develop two new optimal Newton-Householder methods without memory. The key idea in the development of the new methods is the avoidance of the need to evaluate the second derivative. The methods fulfill the Kung-Traub conjecture by achieving optimal convergence order four with three functional evaluations and order eight with four functional evaluations. The efficiency… More >

  • Open AccessOpen Access

    ARTICLE

    Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

    Bao Le Nguyen1, E. Laxmi Lydia2, Mohamed Elhoseny3, Irina V. Pustokhina4, Denis A. Pustokhin5, Mahmoud Mohamed Selim6, Gia Nhu Nguyen7, 8, K. Shankar9, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 87-107, 2020, DOI:10.32604/cmc.2020.011599
    Abstract In present digital era, an exponential increase in Internet of Things (IoT) devices poses several design issues for business concerning security and privacy. Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT. In this view, this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine (ACOMKSVM) with Elliptical Curve cryptosystem (ECC) for secure and reliable IoT data sharing. This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM… More >

  • Open AccessOpen Access

    ARTICLE

    Computational Analysis of the Oscillatory Mixed Convection Flow along a Horizontal Circular Cylinder in Thermally Stratified Medium

    Zia Ullah1, Muhammad Ashraf1, Saqib Zia2, Yuming Chu3, 4, Ilyas Khan5, *, Kottakkaran Sooppy Nisar6
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 109-123, 2020, DOI:10.32604/cmc.2020.011468
    Abstract The present work emphasizes the significance of oscillatory mixed convection stratified fluid and heat transfer characteristics at different stations of non-conducting horizontally circular cylinder in the presence of thermally stratified medium. To remove the difficulties in illustrating the coupled PDE’s, the finite-difference scheme with efficient primitive-variable formulation is proposed to transform dimensionless equations. The numerical simulations of coupled non-dimensional equations are computed in terms velocity of fluid, temperature and magnetic field which are computed to examine the fluctuating components of skin friction, heat transfer and current density for various emerging parameters. The governing parameters namely, thermally stratification parameter More >

  • Open AccessOpen Access

    ARTICLE

    The Robust Regression Methods for Estimating of Finite Population Mean Based on SRSWOR in Case of Outliers

    Mir Subzar1, Amer Ibrahim Al-Omari2, Ayed R. A. Alanzi3, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 125-138, 2020, DOI:10.32604/cmc.2020.010230
    Abstract The ordinary least square (OLS) method is commonly used in regression analysis. But in the presence of outlier in the data, its results are unreliable. Hence, the robust regression methods have been suggested for a long time as alternatives to the OLS to solve the outliers problem. In the present study, new ratio type estimators of finite population mean are suggested using simple random sampling without replacement (SRSWOR) utilizing the supplementary information in Bowley’s coefficient of skewness with quartiles. For these proposed estimators, we have used the OLS, Huber-M, Mallows GM-estimate, Schweppe GM-estimate, and SIS GM-estimate methods for estimating the… More >

  • Open AccessOpen Access

    ARTICLE

    Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Ayesha Atta2, 3, Allah Ditta4, Hani Alquhayz5, Muhammad Farhan Khan6, Atta-ur-Rahman7, Rizwan Ali Naqvi8
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 139-151, 2020, DOI:10.32604/cmc.2020.011416
    Abstract The innovation in technologies related to health facilities today is increasingly helping to manage patients with different diseases. The most fatal of these is the issue of heart disease that cannot be detected from a naked eye, and attacks as soon as the human exceeds the allowed range of vital signs like pulse rate, body temperature, and blood pressure. The real challenge is to diagnose patients with more diagnostic accuracy and in a timely manner, followed by prescribing appropriate treatments and keeping prescription errors to a minimum. In developing countries, the domain of healthcare is progressing day by day using… More >

  • Open AccessOpen Access

    ARTICLE

    A Key Recovery System Based on Password-Protected Secret Sharing in a Permissioned Blockchain

    Gyeong-Jin Ra1, Chang-Hyun Roh1, Im-Yeong Lee1, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 153-170, 2020, DOI:10.32604/cmc.2020.011293
    Abstract In today’s fourth industrial revolution, various blockchain technologies are being actively researched. A blockchain is a peer-to-peer data-sharing structure lacking central control. If a user wishes to access stored data, she/he must employ a private key to prove ownership of the data and create a transaction. If the private key is lost, blockchain data cannot be accessed. To solve such a problem, public blockchain users can recover the key using a wallet program. However, key recovery in a permissioned blockchain (PBC) has been but little studied. The PBC server is Honest-but-Curious (HBC), and should not be able to learn anything… More >

  • Open AccessOpen Access

    ARTICLE

    Generalized Model of Blood Flow in a Vertical Tube with Suspension of Gold Nanomaterials: Applications in the Cancer Therapy

    Anees Imtiaz1, Oi-Mean Foong2, Aamina Aamina1, Nabeel Khan1, Farhad Ali3, 4, *, Ilyas Khan5
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 171-192, 2020, DOI:10.32604/cmc.2020.011397
    Abstract Gold metallic nanoparticles are generally used within a lab as a tracer, to uncover on the presence of specific proteins or DNA in a sample, as well as for the recognition of various antibiotics. They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches. As the cancer cells are very smaller so for the infiltration, the properly sized nanoparticles have been injected in the blood. For this reason, gold nanoparticles are very effective. Keeping in mind the above applications, in the present work a generalized model of blood flow containing gold… More >

  • Open AccessOpen Access

    ARTICLE

    Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

    Ibrahim Arpaci1, Shadi Alshehabi2, Mostafa Al-Emran3, *, Mahmoud Khasawneh4, Ibrahim Mahariq4, Thabet Abdeljawad5, 6, 7, Aboul Ella Hassanien8, 9
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 193-204, 2020, DOI:10.32604/cmc.2020.011489
    (This article belongs to this Special Issue: Mathematical aspects of the Coronavirus Disease 2019 (COVID-19): Analysis and Control)
    Abstract People started posting textual tweets on Twitter as soon as the novel coronavirus (COVID-19) emerged. Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks. Therefore, this study aimed to analyze 43 million tweets collected between March 22 and March 30, 2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis. The results indicated that unigram terms were trended more frequently than bigram and trigram terms. A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic. The high-frequency… More >

  • Open AccessOpen Access

    ARTICLE

    Simulation of Water-Soil-Structure Interactions Using Incompressible Smoothed Particle Hydrodynamics

    Abdelraheem M. Aly1, 2, *, Mitsuteru Asai3, Ehab Mahmoud Mohamed4, 5
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 205-224, 2020, DOI:10.32604/cmc.2020.09227
    Abstract In the present work, an incompressible smoothed particle hydrodynamic (SPH) method is introduced to simulate water-soil-structure interactions. In the current calculation, the water is modelled as a Newtonian fluid. The soil is modelled in two different cases. In the first case, the granular material is considered as a fluid where a Bingham type constitutive model is proposed based on Mohr-Coulomb yield-stress criterion, and the viscosity is derived from the cohesion and friction angle. In addition, the fictitious suspension layers between water and soil depending on the concentration of soil are introduced. In the second case, Hooke’s law introduces elastic soil.… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamical Behavior and Sensitivity Analysis of a Delayed Coronavirus Epidemic Model

    Muhammad Naveed1, *, Dumitru Baleanu2, 3, 4, Muhammad Rafiq5, Ali Raza6, Atif Hassan Soori1, Nauman Ahmed7
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 225-241, 2020, DOI:10.32604/cmc.2020.011534
    Abstract Mathematical delay modelling has a significant role in the different disciplines such as behavioural, social, physical, biological engineering, and bio-mathematical sciences. The present work describes mathematical formulation for the transmission mechanism of a novel coronavirus (COVID-19). Due to the unavailability of vaccines for the coronavirus worldwide, delay factors such as social distance, quarantine, travel restrictions, extended holidays, hospitalization, and isolation have contributed to controlling the coronavirus epidemic. We have analysed the reproduction number and its sensitivity to parameters. If, More >

  • Open AccessOpen Access

    ARTICLE

    Network-Aided Intelligent Traffic Steering in 5G Mobile Networks

    Dae-Young Kim1, Seokhoon Kim2, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 243-261, 2020, DOI:10.32604/cmc.2020.011253
    Abstract Recently, the fifth generation (5G) of mobile networks has been deployed and various ranges of mobile services have been provided. The 5G mobile network supports improved mobile broadband, ultra-low latency and densely deployed massive devices. It allows multiple radio access technologies and interworks them for services. 5G mobile systems employ traffic steering techniques to efficiently use multiple radio access technologies. However, conventional traffic steering techniques do not consider dynamic network conditions efficiently. In this paper, we propose a network aided traffic steering technique in 5G mobile network architecture. 5G mobile systems monitor network conditions and learn with network data. Through… More >

  • Open AccessOpen Access

    ARTICLE

    A Structure Preserving Numerical Method for Solution of Stochastic Epidemic Model of Smoking Dynamics

    Ali Raza1, Muhammad Rafiq2, Nauman Ahmed3, Ilyas Khan4, *, Kottakkaran Sooppy Nisar5, Zafar Iqbal3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 263-278, 2020, DOI:10.32604/cmc.2020.011289
    Abstract In this manuscript, we consider a stochastic smoking epidemic model from behavioural sciences. Also, we develop a structure preserving numerical method to describe the dynamics of stochastic smoking epidemic model in a human population. The structural properties of a physical system include positivity, boundedness and dynamical consistency. These properties play a vital role in non-linear dynamics. The solution for nonlinear stochastic models necessitates the conservation of these properties. Unfortunately, the aforementioned properties of the model have not been restored in the existing stochastic methods. Therefore, it is essential to construct a structure preserving numerical method for a reliable analysis of… More >

  • Open AccessOpen Access

    ARTICLE

    Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 279-308, 2020, DOI:10.32604/cmc.2020.011001
    Abstract Software defect prediction plays an important role in software quality assurance. However, the performance of the prediction model is susceptible to the irrelevant and redundant features. In addition, previous studies mostly regard software defect prediction as a single objective optimization problem, and multi-objective software defect prediction has not been thoroughly investigated. For the above two reasons, we propose the following solutions in this paper: (1) we leverage an advanced deep neural network—Stacked Contractive AutoEncoder (SCAE) to extract the robust deep semantic features from the original defect features, which has stronger discrimination capacity for different classes (defective or non-defective). (2) we… More >

  • Open AccessOpen Access

    ARTICLE

    Applying Stack Bidirectional LSTM Model to Intrusion Detection

    Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102
    Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

  • Open AccessOpen Access

    ARTICLE

    A Cache Replacement Policy Based on Multi-Factors for Named Data Networking

    Meiju Yu1, Ru Li1, *, Yuwen Chen2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.010831
    Abstract Named Data Networking (NDN) is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by. It greatly reduces network traffic and improves the speed of content distribution and retrieval. In order to make full use of the limited caching space in routers, it is an urgent challenge to make an efficient cache replacement policy. However, the existing cache replacement policies only consider very few factors that affect the cache performance. In this paper, we present a cache replacement policy based on multi-factors for NDN (CRPM), in which the content… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Algorithm for Mining Correlation Item Pairs

    Tao Li1, Yongzhen Ren1, *, Yongjun Ren2, Jinyue Xia3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 337-354, 2020, DOI:10.32604/cmc.2020.06462
    Abstract Apriori algorithm is often used in traditional association rules mining, searching for the mode of higher frequency. Then the correlation rules are obtained by detected the correlation of the item sets, but this tends to ignore low-support high-correlation of association rules. In view of the above problems, some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the algorithm has pruned the search space, it is not obvious that the performance of the running time based on the big data… More >

  • Open AccessOpen Access

    ARTICLE

    An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

    Ye Wang1, Bixin Liu2, Hongjia Wu1, Shan Zhao1, Zhiping Cai1, *, Donghui Li3, *, Cheang Chak Fong4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 355-367, 2020, DOI:10.32604/cmc.2020.09835
    Abstract With the continuous development of e-commerce, consumers show increasing interest in posting comments on consumption experience and quality of commodities. Meanwhile, people make purchasing decisions relying on other comments much more than ever before. So the reliability of commodity comments has a significant impact on ensuring consumers’ equity and building a fair internet-trade-environment. However, some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits. Those improper ways of self-profiting have severely ruined the entire online shopping industry. Aiming to detect and prevent these deceptive comments effectively, we construct a model… More >

  • Open AccessOpen Access

    ARTICLE

    Frequent Itemset Mining of User’s Multi-Attribute under Local Differential Privacy

    Haijiang Liu1, Lianwei Cui2, Xuebin Ma1, *, Celimuge Wu3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 369-385, 2020, DOI:10.32604/cmc.2020.010987
    Abstract Frequent itemset mining is an essential problem in data mining and plays a key role in many data mining applications. However, users’ personal privacy will be leaked in the mining process. In recent years, application of local differential privacy protection models to mine frequent itemsets is a relatively reliable and secure protection method. Local differential privacy means that users first perturb the original data and then send these data to the aggregator, preventing the aggregator from revealing the user’s private information. We propose a novel framework that implements frequent itemset mining under local differential privacy and is applicable to user’s… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

    Xinyue Lin1, Jin Liu1, *, Jianming Zhang2, Se-Jung Lim3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 387-404, 2020, DOI:10.32604/cmc.2020.010984
    Abstract Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train… More >

  • Open AccessOpen Access

    ARTICLE

    The Identification of the Wind Parameters Based on the Interactive Multi-Models

    Lihua Zhu1, Zhiqiang Wu1, Lei Wang2, Yu Wang1, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 405-418, 2020, DOI:10.32604/cmc.2020.010124
    Abstract The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. Considering the diversity of the… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Design of Mechanical Switch for the High Overload Environment

    Yu Wang1, Chen Liu1, Lei Wang2, Lihua Zhu1, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 419-432, 2020, DOI:10.32604/cmc.2020.010911
    Abstract The internal structure of the inertial measurement unit (IMU) in active state is easily damaged in the high overload environment. So that the IMU is usually required to be powered within the disappearance of the high overload. In this paper, a mechanical switch is designed to enable the IMU based on the analysis of the impact of high overload on the power-supply circuit. In which, parameters of mechanical switch are determined through theoretical calculation and data analysis. The innovation of the proposed structure lies in that the mechanical switch is triggered through the high overload process and could provide a… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Directional Reconstruction Algorithm for Panoramic Camera

    Shi Qiu1, Bin Li2, *, Keyang Cheng3, Xiao Zhang2, Guifang Duan4, Feng Li5
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 433-443, 2020, DOI:10.32604/cmc.2020.09708
    Abstract of view. It can be applied in virtual reality, smart homes and other fields as well. A multi-directional reconstruction algorithm for panoramic camera is proposed in this paper according to the imaging principle of dome camera, as the distortion inevitably exists in the captured panorama. First, parameters of a panoramic image are calculated. Then, a weighting operator with location information is introduced to solve the problem of rough edges by taking full advantage of pixels. Six directions of the mapping model are built, which include up, down, left, right, front and back, according to the correspondence between cylinder and spherical… More >

  • Open AccessOpen Access

    ARTICLE

    Quantum Generative Model with Variable-Depth Circuit

    Yiming Huang1, *, Hang Lei1, Xiaoyu Li1, *, Qingsheng Zhu2, Wanghao Ren3, Xusheng Liu2, 4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 445-458, 2020, DOI:10.32604/cmc.2020.010390
    Abstract In recent years, an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm. The hybrid quantum-classical framework, which is constructed by a variational quantum circuit (VQC) and an optimizer, plays a key role in the latest quantum machine learning studies. Nevertheless, in these hybridframework-based quantum machine learning models, the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems. There are also few studies focused on comparing the performance of quantum generative models with different loss functions. In this… More >

  • Open AccessOpen Access

    ARTICLE

    An Approach for Radar Quantitative Precipitation Estimation Based on Spatiotemporal Network

    Shengchun Wang1, Xiaozhong Yu1, Lianye Liu2, Jingui Huang1, *, Tsz Ho Wong3, Chengcheng Jiang1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 459-479, 2020, DOI:10.32604/cmc.2020.010627
    Abstract Radar quantitative precipitation estimation (QPE) is a key and challenging task for many designs and applications with meteorological purposes. Since the Z-R relation between radar and rain has a number of parameters on different areas, and the rainfall varies with seasons, the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation. This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model (ST-QPE), which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations. We report on… More >

  • Open AccessOpen Access

    ARTICLE

    Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

    Yonghong Xie1, 3, Liangyuan Hu1, 3, Xingxing Chen2, 3, Jim Feng4, Dezheng Zhang1, 3, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 481-494, 2020, DOI:10.32604/cmc.2020.010297
    Abstract As one of the most valuable assets in China, traditional medicine has a long history and contains pieces of knowledge. The diagnosis and treatment of Traditional Chinese Medicine (TCM) has benefited from the natural language processing technology. This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning path. According to this reasoning path, we could infer the path from the symptoms to the syndrome and get all possibilities… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Method of Heart Failure Prediction Based on DPCNNXGBOOST Model

    Yuwen Chen1, 2, 3, *, Xiaolin Qin1, 3, Lige Zhang1, 3, Bin Yi4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 495-510, 2020, DOI:10.32604/cmc.2020.011278
    Abstract The occurrence of perioperative heart failure will affect the quality of medical services and threaten the safety of patients. Existing methods depend on the judgment of doctors, the results are affected by many factors such as doctors’ knowledge and experience. The accuracy is difficult to guarantee and has a serious lag. In this paper, a mixture prediction model is proposed for perioperative adverse events of heart failure, which combined with the advantages of the Deep Pyramid Convolutional Neural Networks (DPCNN) and Extreme Gradient Boosting (XGBOOST). The DPCNN was used to automatically extract features from patient’s diagnostic texts, and the text… More >

  • Open AccessOpen Access

    ARTICLE

    Image Processing of Manganese Nodules Based on Background Gray Value Calculation

    Hade Mao1, 2, Yuliang Liu1, 2, *, Hongzhe Yan1, 2, Cheng Qian3, Jing Xue4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 511-527, 2020, DOI:10.32604/cmc.2020.09841
    Abstract To troubleshoot two problems arising from the segmentation of manganese nodule images-uneven illumination and morphological defects caused by white sand coverage, we propose, with reference to features of manganese nodules, a method called “background gray value calculation”. As the result of the image procession with the aid this method, the two problems above are solved eventually, together with acquisition of a segmentable image of manganese nodules. As a result, its comparison with other segmentation methods justifies its feasibility and stability. Judging from simulation results, it is indicated that this method is applicable to repair the target shape in the image,… More >

  • Open AccessOpen Access

    ARTICLE

    Jointly Part-of-Speech Tagging and Semantic Role Labeling Using Auxiliary Deep Neural Network Model

    Yatian Shen1, Yubo Mai2, Xiajiong Shen2, Wenke Ding2, *, Mengjiao Guo3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 529-541, 2020, DOI:10.32604/cmc.2020.011139
    Abstract Previous studies have shown that there is potential semantic dependency between part-of-speech and semantic roles. At the same time, the predicate-argument structure in a sentence is important information for semantic role labeling task. In this work, we introduce the auxiliary deep neural network model, which models semantic dependency between part-of-speech and semantic roles and incorporates the information of predicate-argument into semantic role labeling. Based on the framework of joint learning, part-of-speech tagging is used as an auxiliary task to improve the result of the semantic role labeling. In addition, we introduce the argument recognition layer in the training process of… More >

  • Open AccessOpen Access

    ARTICLE

    Accurate Multi-Scale Feature Fusion CNN for Time Series Classification in Smart Factory

    Xiaorui Shao1, Chang Soo Kim1, *, Dae Geun Kim2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 543-561, 2020, DOI:10.32604/cmc.2020.011108
    Abstract Time series classification (TSC) has attracted various attention in the community of machine learning and data mining and has many successful applications such as fault detection and product identification in the process of building a smart factory. However, it is still challenging for the efficiency and accuracy of classification due to complexity, multi-dimension of time series. This paper presents a new approach for time series classification based on convolutional neural networks (CNN). The proposed method contains three parts: short-time gap feature extraction, multi-scale local feature learning, and global feature learning. In the process of short-time gap feature extraction, large kernel… More >

  • Open AccessOpen Access

    ARTICLE

    Ore Image Segmentation Method Based on U-Net and Watershed

    Hui Li1, Chengwei Pan2, 3, Ziyi Chen1, Aziguli Wulamu2, 3, *, Alan Yang4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 563-578, 2020, DOI:10.32604/cmc.2020.09806
    Abstract Ore image segmentation is a key step in an ore grain size analysis based on image processing. The traditional segmentation methods do not deal with ore textures and shadows in ore images well Those methods often suffer from under-segmentation and over-segmentation. In this article, in order to solve the problem, an ore image segmentation method based on U-Net is proposed. We adjust the structure of U-Net to speed up the processing, and we modify the loss function to enhance the generalization of the model. After the collection of the ore image, we design the annotation standard and train the network… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Terrain Debris Recognition Network Based on 3D Remote Sensing Data

    Xu Han1, #, Huijun Yang1, 4, *, Qiufeng Shen1, #, Jiangtao Yang2, Huihui Liang1, Cancan Bao1, Shuang Cang3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 579-596, 2020, DOI:10.32604/cmc.2020.011262
    Abstract Although predecessors have made great contributions to the semantic segmentation of 3D indoor scenes, there still exist some challenges in the debris recognition of terrain data. Compared with hundreds of thousands of indoor point clouds, the amount of terrain point cloud is up to millions. Apart from that, terrain point cloud data obtained from remote sensing is measured in meters, but the indoor scene is measured in centimeters. In this case, the terrain debris obtained from remote sensing mapping only have dozens of points, which means that sufficient training information cannot be obtained only through the convolution of points. In… More >

  • Open AccessOpen Access

    ARTICLE

    A Covert Communication Method Using Special Bitcoin Addresses Generated by Vanitygen

    Lejun Zhang1, 2, Zhijie Zhang1, Weizheng Wang3, Rasheed Waqas1, Chunhui Zhao1, 4, Seokhoon Kim5, Huiling Chen6, *
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 597-616, 2020, DOI:10.32604/cmc.2020.011554
    Abstract As an extension of the traditional encryption technology, information hiding has been increasingly used in the fields of communication and network media, and the covert communication technology has gradually developed. The blockchain technology that has emerged in recent years has the characteristics of decentralization and tamper resistance, which can effectively alleviate the disadvantages and problems of traditional covert communication. However, its combination with covert communication thus far has been mostly at the theoretical level. The BLOCCE method, as an early result of the combination of blockchain and covert communication technology, has the problems of low information embedding efficiency, the use… More >

  • Open AccessOpen Access

    ARTICLE

    Semi-GSGCN: Social Robot Detection Research with Graph Neural Network

    Xiujuan Wang1, Qianqian Zheng1, *, Kangfeng Zheng2, Yi Sui1, Jiayue Zhang1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 617-638, 2020, DOI:10.32604/cmc.2020.011165
    Abstract Malicious social robots are the disseminators of malicious information on social networks, which seriously affect information security and network environments. Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks. Supervised classification based on manual feature extraction has been widely used in social robot detection. However, these methods not only involve the privacy of users but also ignore hidden feature information, especially the graph feature, and the label utilization rate of semi-supervised algorithms is low. Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection… More >

  • Open AccessOpen Access

    ARTICLE

    Identifying Game Processes Based on Private Working Sets

    Jinfeng Li1, Li Feng1, *, Longqing Zhang2, Hongning Dai1, Lei Yang1, Liwei Tian1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 639-651, 2020, DOI:10.32604/cmc.2020.010309
    Abstract Fueled by the booming online games, there is an increasing demand for monitoring online games in various settings. One of the application scenarios is the monitor of computer games in school computer labs, for which an intelligent game recognition method is required. In this paper, a method to identify game processes in accordance with private working sets (i.e., the amount of memory occupied by a process but cannot be shared among other processes) is introduced. Results of the W test showed that the memory sizes occupied by the legitimate processes (e.g., the processes of common native windows applications) and game… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

    Fei Li1, *, Jiayan Zhang1, Edward Szczerbicki2, Jiaqi Song1, Ruxiang Li 1, Renhong Diao1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264
    Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency… More >

  • Open AccessOpen Access

    ARTICLE

    Comprehensive Information Security Evaluation Model Based on Multi-Level Decomposition Feedback for IoT

    Jinxin Zuo1, 3, Yueming Lu1, 3, *, Hui Gao2, 3, Ruohan Cao2, 3, Ziyv Guo2, 3, Jim Feng4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 683-704, 2020, DOI:10.32604/cmc.2020.010793
    Abstract The development of the Internet of Things (IoT) calls for a comprehensive information security evaluation framework to quantitatively measure the safety score and risk (S&R) value of the network urgently. In this paper, we summarize the architecture and vulnerability in IoT and propose a comprehensive information security evaluation model based on multi-level decomposition feedback. The evaluation model provides an idea for information security evaluation of IoT and guides the security decision maker for dynamic protection. Firstly, we establish an overall evaluation indicator system that includes four primary indicators of threat information, asset, vulnerability, and management, respectively. It also includes eleven… More >

  • Open AccessOpen Access

    ARTICLE

    Ultrasound Speckle Reduction Based on Histogram Curve Matching and Region Growing

    Jinrong Hu1, Zhiqin Lei1, Xiaoying Li2, *, Yongqun He3, Jiliu Zhou1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 705-722, 2020, DOI:10.32604/cmc.2020.09878
    Abstract The quality of ultrasound scanning images is usually damaged by speckle noise. This paper proposes a method based on local statistics extracted from a histogram to reduce ultrasound speckle through a region growing algorithm. Unlike single statistical moment-based speckle reduction algorithms, this method adaptively smooths the speckle regions while preserving the margin and tissue structure to achieve high detectability. The criterion of a speckle region is defined by the similarity value obtained by matching the histogram of the current processing window and the reference window derived from the speckle region in advance. Then, according to the similarity value and tissue… More >

  • Open AccessOpen Access

    ARTICLE

    A Distributed Privacy Preservation Approach for Big Data in Public Health Emergencies Using Smart Contract and SGX

    Jun Li1, 2, Jieren Cheng2, *, Naixue Xiong3, Lougao Zhan4, Yuan Zhang1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 723-741, 2020, DOI:10.32604/cmc.2020.011272
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract Security and privacy issues have become a rapidly growing problem with the fast development of big data in public health. However, big data faces many ongoing serious challenges in the process of collection, storage, and use. Among them, data security and privacy problems have attracted extensive interest. In an effort to overcome this challenge, this article aims to present a distributed privacy preservation approach based on smart contracts and Intel Software Guard Extensions (SGX). First of all, we define SGX as a trusted edge computing node, design data access module, data protection module, and data integrity check module, to achieve… More >

  • Open AccessOpen Access

    ARTICLE

    An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

    Peipeng Yu1, Zhihua Xia1, *, Jianwei Fei1, Sunil Kumar Jha1, 2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 743-760, 2020, DOI:10.32604/cmc.2020.011391
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms… More >

  • Open AccessOpen Access

    ARTICLE

    Identification of Crop Diseases Based on Improved Genetic Algorithm and Extreme Learning Machine

    Linguo Li1, 2, Lijuan Sun1, Jian Guo1, Shujing Li2, *, Ping Jiang3
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 761-775, 2020, DOI:10.32604/cmc.2020.010158
    Abstract As an indispensable task in crop protection, the detection of crop diseases directly impacts the income of farmers. To address the problems of low crop-disease identification precision and detection abilities, a new method of detection is proposed based on improved genetic algorithm and extreme learning machine. Taking five different typical diseases with common crops as the objects, this method first preprocesses the images of crops and selects the optimal features for fusion. Then, it builds a model of crop disease identification for extreme learning machine, introduces the hill-climbing algorithm to improve the traditional genetic algorithm, optimizes the initial weights and… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

    Shi Li1, Xinyan Cao1, *, Yiting Nan2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 777-788, 2020, DOI:10.32604/cmc.2020.010870
    Abstract Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local and global features. We also… More >

  • Open AccessOpen Access

    ARTICLE

    Identification of Parameters in 2D-FEM of Valve Piping System within NPP Utilizing Seismic Response

    Ruiyuan Xue1, Shurong Yu1, *, Xiheng Zhang1
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 789-805, 2020, DOI:10.32604/cmc.2020.011340
    Abstract Nuclear power plants (NPP) contain plenty of valve piping systems (VPS’s) which are categorized into high anti-seismic grades. Tasks such as seismic qualification, health monitoring and damage diagnosis of VPS’s in its design and operation processes all depend on finite element method. However, in engineering practice, there is always deviations between the theoretical and the measured responses due to the inaccurate value of the structural parameters in the model. The structure parameters identification of VPS within NPP is still an unexplored domain to a large extent. In this paper, the initial 2Dfinite element model (FEM) for VPS with a DN80… More >

  • Open AccessOpen Access

    ARTICLE

    Image Deblurring of Video Surveillance System in Rainy Environment

    Jinxing Niu1, *, Yajie Jiang1, Yayun Fu1, Tao Zhang1, Nicola Masini2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 807-816, 2020, DOI:10.32604/cmc.2020.011044
    Abstract Video surveillance system is used in various fields such as transportation and social life. The bad weather can lead to the degradation of the video surveillance image quality. In rainy environment, the raindrops and the background are mixed, which lead to make the image degradation, so the removal of the raindrops has great significance for image restoration. In this article, after analyzing the inter-frame difference method in detecting and removing raindrops, a background difference method is proposed based on Gaussian model. In this method, the raindrop is regarded as a moving object relative to the background. The principle and procedure… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Chinese Essay Scoring Based on Deep Learning

    Shuai Yuan1, Tingting He2, 3, *, Huan Huang4, Rui Hou5, Meng Wang6
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 817-833, 2020, DOI:10.32604/cmc.2020.010471
    Abstract Writing is an important part of language learning and is considered the best approach to demonstrate the comprehensive language skills of students. Manually grading student essays is a time-consuming task; however, it is necessary. An automated essay scoring system can not only greatly improve the efficiency of essay scoring, but also provide more objective score. Therefore, many researchers have been exploring automated essay scoring techniques and tools. However, the technique of scoring Chinese essays is still limited, and its accuracy needs to be enhanced further. To improve the accuracy of the scoring model for a Chinese essay, we propose an… More >

  • Open AccessOpen Access

    ARTICLE

    RFID Based Non-Preemptive Random Sleep Scheduling in WSN

    Tianle Zhang1, Lihua Yin1, Xiang Cui1, *, Abhishek Behl2, Fuqiang Dong3, Ziheng Cheng4, Kuo Ma4
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 835-845, 2020, DOI:10.32604/cmc.2020.06050
    Abstract In Wireless Sensor Network (WSN), because battery and energy supply are constraints, sleep scheduling is always needed to save energy while maintaining connectivity for packet delivery. Traditional schemes have to ensure high duty cycling to ensure enough percentage of active nodes and then derogate the energy efficiency. This paper proposes an RFID based non-preemptive random sleep scheduling scheme with stable low duty cycle. It employs delay tolerant network routing protocol to tackle the frequent disconnections. A low-power RFID based non-preemptive wakeup signal is used to confirm the availability of next-hop before sending packet. It eliminates energy consumption of repeated retransmission… More >

  • Open AccessOpen Access

    ARTICLE

    Automated Dimensioning Method of Engineering Drawings for Mechanical Products Based on Curve Chain

    Gui Li1, *, Peng Yang2
    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 847-863, 2020, DOI:10.32604/cmc.2020.011398
    Abstract An automated method based on the curve chain was proposed for dimensioning of engineering drawings for the mechanical products. According to the internal relation between the features of 3D model feature and elements of 2D drawing, the curve chain was established to reflect the geometric topological structure between the elements. It divides the dimensions into the absolute dimensions within the cure chain and the relative dimensions between the curve chains. The parallel and lengthy relationship between the drawing elements of the constructed X and Y parallel matrix was solved to remove redundant elements in the curve chain and labeled the… More >

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