Journals / CMC / Vol.64, No.3
Table of Content


  • ARTICLE

    Intelligent Forecasting Model of COVID-19 Novel Coronavirus Outbreak Empowered with Deep Extreme Learning Machine

    Muhammad Adnan Khan1, *, Sagheer Abbas2, Khalid Masood Khan1, Mohammad A. Al Ghamdi3, Abdur Rehman2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1329-1342, 2020, DOI:10.32604/cmc.2020.011155
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract An epidemic is a quick and widespread disease that threatens many lives and damages the economy. The epidemic lifetime should be accurate so that timely and remedial steps are determined. These include the closing of borders schools, suspension of community and commuting services. The forecast of an outbreak effectively is a very necessary but difficult task. A predictive model that provides the best possible forecast is a great challenge for machine learning with only a few samples of training available. This work proposes and examines a prediction model based on a deep extreme learning machine (DELM). This methodology is used… More >

  • ARTICLE

    Data Driven Modelling of Coronavirus Spread in Spain

    G. N. Baltas1, *, F. A. Prieto1, M. Frantzi2, C. R. Garcia-Alonso1, P. Rodriguez1, 3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1343-1357, 2020, DOI:10.32604/cmc.2020.011243
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More >

  • ARTICLE

    On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning

    Mohammad Shorfuzzaman1, *, Mehedi Masud1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1359-1381, 2020, DOI:10.32604/cmc.2020.011326
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights… More >

  • ARTICLE

    Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19

    Ramazan Ünlü1, Ersin Namlı2, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1383-1399, 2020, DOI:10.32604/cmc.2020.011335
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory… More >

  • ARTICLE

    Mathematical Analysis of Novel Coronavirus (2019-nCov) Delay Pandemic Model

    Muhammad Naveed1, Muhammad Rafiq2, Ali Raza3, Nauman Ahmed4, Ilyas Khan5, *, Kottakkaran Sooppy Nisar6, Atif Hassan Soori1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1401-1414, 2020, DOI:10.32604/cmc.2020.011314
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract In this manuscript, the mathematical analysis of corona virus model with time delay effect is studied. Mathematical modelling of infectious diseases has substantial role in the different disciplines such as biological, engineering, physical, social, behavioural problems and many more. Most of infectious diseases are dreadful such as HIV/AIDS, Hepatitis and 2019-nCov. Unfortunately, due to the non-availability of vaccine for 2019- nCov around the world, the delay factors like, social distancing, quarantine, travel restrictions, holidays extension, hospitalization and isolation are used as key tools to control the pandemic of 2019-nCov. We have analysed the reproduction number RnCov of delayed model. Two… More >

  • ARTICLE

    COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

    Yixian Zhang1, Jieren Cheng2, *, Yifan Yang2, Haocheng Li2, Xinyi Zheng2, Xi Chen2, Boyi Liu3, Tenglong Ren4, Naixue Xiong5
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More >

  • ARTICLE

    A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

    Xiaorui Zhang1, 2, *, Wenfang Zhang1, Wei Sun2, Tong Xu1, Sunil Kumar Jha3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1435-1452, 2020, DOI:10.32604/cmc.2020.011359
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest regions. Then the integrity of… More >

  • ARTICLE

    What is Discussed about COVID-19: A Multi-Modal Framework for Analyzing Microblogs from Sina Weibo without Human Labeling

    Hengyang Lu1, *, Yutong Lou2, Bin Jin2, Ming Xu2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1453-1471, 2020, DOI:10.32604/cmc.2020.011270
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract Starting from late 2019, the new coronavirus disease (COVID-19) has become a global crisis. With the development of online social media, people prefer to express their opinions and discuss the latest news online. We have witnessed the positive influence of online social media, which helped citizens and governments track the development of this pandemic in time. It is necessary to apply artificial intelligence (AI) techniques to online social media and automatically discover and track public opinions posted online. In this paper, we take Sina Weibo, the most widely used online social media in China, for analysis and experiments. We collect… More >

  • ARTICLE

    An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

    Bingjie Yan1, Jun Wang1, Zhen Zhang2, Xiangyan Tang1, *, Yize Zhou1, Guopeng Zheng1, Qi Zou1, Yao Lu1, Boyi Liu3, Wenxuan Tu4, Neal Xiong5
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1473-1490, 2020, DOI:10.32604/cmc.2020.011317
    (This article belongs to this Special Issue: Artificial Intelligence and Information Technologies for COVID-19)
    Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More >

  • ARTICLE

    Four-Step Iteration Scheme to Approximate Fixed Point for Weak Contractions

    Wasfi Shatanawi1, 2, 3, *, Anwar Bataihah4, Abdalla Tallafha4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1491-1504, 2020, DOI:10.32604/cmc.2020.010365
    Abstract Fixed point theory is one of the most important subjects in the setting of metric spaces since fixed point theorems can be used to determine the existence and the uniqueness of solutions of such mathematical problems. It is known that many problems in applied sciences and engineering can be formulated as functional equations. Such equations can be transferred to fixed point theorems in an easy manner. Moreover, we use the fixed point theory to prove the existence and uniqueness of solutions of such integral and differential equations. Let X be a non-empty set. A fixed point for a self-mapping TMore >

  • ARTICLE

    Generalized Marshall Olkin Inverse Lindley Distribution with Applications

    Rashad Bantan1, Amal S. Hassan2, Mahmoud Elsehetry3, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1505-1526, 2020, DOI:10.32604/cmc.2020.010887
    Abstract In this article, a new generalization of the inverse Lindley distribution is introduced based on Marshall-Olkin family of distributions. We call the new distribution, the generalized Marshall-Olkin inverse Lindley distribution which offers more flexibility for modeling lifetime data. The new distribution includes the inverse Lindley and the Marshall-Olkin inverse Lindley as special distributions. Essential properties of the generalized Marshall-Olkin inverse Lindley distribution are discussed and investigated including, quantile function, ordinary moments, incomplete moments, moments of residual and stochastic ordering. Maximum likelihood method of estimation is considered under complete, Type-I censoring and Type-II censoring. Maximum likelihood estimators as well as approximate… More >

  • ARTICLE

    Secure Sharing Scheme of Sensitive Data in the Precision Medicine System

    Deukhun Kim1, Heejin Kim2, Jin Kwak3, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1527-1553, 2020, DOI:10.32604/cmc.2020.010535
    Abstract Numerous industries, especially the medical industry, are likely to exhibit significant developments in the future. Ever since the announcement of the precision medicine initiative by the United States in 2015, interest in the field has considerably increased. The techniques of precision medicine are employed to provide optimal treatment and medical services to patients, in addition to the prevention and management of diseases via the collection and analysis of big data related to their individual genetic characteristics, occupation, living environment, and dietary habits. As this involves the accumulation and utilization of sensitive information, such as patient history, DNA, and personal details,… More >

  • ARTICLE

    Benchmarking Approach to Compare Web Applications Static Analysis Tools Detecting OWASP Top Ten Security Vulnerabilities

    Juan R. Bermejo Higuera1, *, Javier Bermejo Higuera1, Juan A. Sicilia Montalvo1, Javier Cubo Villalba1, Juan José Nombela Pérez1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1555-1577, 2020, DOI:10.32604/cmc.2020.010885
    Abstract To detect security vulnerabilities in a web application, the security analyst must choose the best performance Security Analysis Static Tool (SAST) in terms of discovering the greatest number of security vulnerabilities as possible. To compare static analysis tools for web applications, an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project (OWASP) Top Ten project is required. The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and… More >

  • ARTICLE

    A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

    Ahmed Y. Hamed1, Monagi H. Alkinani2, M. R. Hassan3, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1579-1586, 2020, DOI:10.32604/cmc.2020.010881
    Abstract Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations—i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total… More >

  • ARTICLE

    Gain-Enhanced Metamaterial Based Antenna for 5G Communication Standards

    Daniyal Ali Sehrai1, Fazal Muhammad1, Saad Hassan Kiani1, Ziaul Haq Abbas2, Muhammad Tufail3, Sunghwan Kim4, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1587-1599, 2020, DOI:10.32604/cmc.2020.011057
    Abstract Metamaterial surfaces play a vital role to achieve the surface waves suppression and in-phase reflection, in order to improve the antenna performance. In this paper, the performance comparison of a fifth generation (5G) antenna design is analyzed and compared with a metamaterial-based antenna for 5G communication system applications. Metamaterial surface is utilized as a reflector due to its in-phase reflection characteristic and high-impedance nature to improve the gain of an antenna. As conventional conducting ground plane does not give enough surface waves suppression which affects the antenna performance in terms of efficiency and gain etc. These factors are well considered… More >

  • ARTICLE

    Better Visual Image Super-Resolution with Laplacian Pyramid of Generative Adversarial Networks

    Ming Zhao1, Xinhong Liu1, Xin Yao1, *, Kun He2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1601-1614, 2020, DOI:10.32604/cmc.2020.09754
    Abstract Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to restore finer texture details during image super-resolution reconstruction? This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the Laplacian pyramid to capture the high-frequency details of the image. By combining Laplacian pyramids and generative adversarial networks, progressive reconstruction of super-resolution images can be made, making model applications more flexible. In order to solve the problem of gradient disappearance, we introduce the Residual-in-Residual Dense… More >

  • ARTICLE

    ECG Classification Using Deep CNN Improved by Wavelet Transform

    Yunxiang Zhao1, Jinyong Cheng1, *, Ping Zhang1, Xueping Peng2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1615-1628, 2020, DOI:10.32604/cmc.2020.09938
    Abstract Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies… More >

  • ARTICLE

    Continuous-Variable Quantum Network Coding Based on Quantum Discord

    Tao Shang1, *, Ran Liu1, Jianwei Liu1, Yafei Hou2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1629-1645, 2020, DOI:10.32604/cmc.2020.09820
    Abstract Establishing entanglement is an essential task of quantum communication technology. Beyond entanglement, quantum discord, as a measure of quantum correlation, is a necessary prerequisite to the success of entanglement distribution. To realize efficient quantum communication based on quantum discord, in this paper, we consider the practical advantages of continuous variables and propose a feasible continuous-variable quantum network coding scheme based on quantum discord. By means of entanglement distribution by separable states, it can achieve quantum entanglement distribution from sources to targets in a butterfly network. Compared with the representative discrete-variable quantum network coding schemes, the proposed continuous-variable quantum network coding… More >

  • ARTICLE

    Impact Force Magnitude and Location Recognition of Composite Materials

    Yajie Sun1, 2, *, Yanqing Yuan2, Qi Wang2, Sai Ji1, 2, Lihua Wang3, Shao’en Wu4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1647-1656, 2020, DOI:10.32604/cmc.2020.06331
    Abstract In order to identify the location and magnitude of the impact force accurately, determine the damage range of the structure and accelerate the health monitoring of key components of the composite, this paper studies the location and magnitude of the impact force of composite plates by an inverse method. Firstly, a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force, which is from several impact locations, and establish transfer functions between the impact location and the PZT sensor. Secondly, this paper applies several forces to any location on the material… More >

  • ARTICLE

    A DRL-Based Container Placement Scheme with Auxiliary Tasks

    Ningcheng Yuan1, Chao Jia2, *, Jizhao Lu3, Shaoyong Guo1, Wencui Li3, Xuesong Qiu1, Lei Shi4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1657-1671, 2020, DOI:10.32604/cmc.2020.09840
    Abstract Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose not lead to a satisfying… More >

  • ARTICLE

    A Secure Three-Factor Authenticated Key Agreement Scheme for Multi-Server Environment

    Meichen Xia1, *, Shiliang Li1,, Liu Liu2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1673-1689, 2020, DOI:10.32604/cmc.2020.010177
    Abstract Multi-server authenticated key agreement schemes have attracted great attention to both academia and industry in recent years. However, traditional authenticated key agreement schemes in the single-server environment are not suitable for the multi-server environment because the user has to register on each server when he/she wishes to log in various servers for different service. Moreover, it is unreasonable to consider all servers are trusted since the server in a multi-server environment may be a semi-trusted party. In order to overcome these difficulties, we designed a secure threefactor multi-server authenticated key agreement protocol based on elliptic curve cryptography, which needs the… More >

  • ARTICLE

    Embedding Implicit User Importance for Group Recommendation

    Qing Yang1, Shengjie Zhou1, Heyong Li1, Jingwei Zhang2, 3, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1691-1704, 2020, DOI:10.32604/cmc.2020.010256
    Abstract Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality, which creates real scenarios and promotes the development of group recommendation systems. Different from traditional personalized recommendation methods, which are concerned only with the accuracy of recommendations for individuals, group recommendation is expected to balance the needs of multiple users. Building a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation applications. Existing studies often focus… More >

  • ARTICLE

    A Cross Layer Protocol for Fast Identification of Blocked Tags in Large-Scale RFID Systems

    Chu Chu1, Zhong Huang1, Rui Xu1, Guangjun Wen1, *, Lilan Liu2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1705-1724, 2020, DOI:10.32604/cmc.2020.010190
    Abstract Blocker tag attack is one of the denial-of-service (DoS) attacks that threatens the privacy and security of RFID systems. The attacker interferes with the blocked tag by simulating a fake tag with the same ID, thus causing a collision of message replies. In many practical scenarios, the number of blocked tags may vary, or even be small. For example, the attacker may only block the important customers or high-value items. To avoid the disclosure of privacy and economic losses, it is of great importance to fast pinpoint these blocked ones. However, existing works do not take into account the impact… More >

  • ARTICLE

    A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm

    Xinliang Tang1, Man Zhang1, Pingping Yu1, Wei Liu2, Ning Cao3, *, Yunfeng Xu4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1725-1739, 2020, DOI:10.32604/cmc.2020.010272
    Abstract In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection… More >

  • ARTICLE

    Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding

    Chuqing Zhang1, Jiangyuan Yao2, *, Guangwu Hu3, Thomas Schøtt4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1741-1753, 2020, DOI:10.32604/cmc.2020.010306
    Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones.… More >

  • ARTICLE

    Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

    Zhaoquan Gu1, Yinyin Cai1, Sheng Wang1, Mohan Li1, *, Jing Qiu1, Shen Su1, Xiaojiang Du1, Zhihong Tian1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1755-1770, 2020, DOI:10.32604/cmc.2020.010739
    Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks against content-based filtering JRS. We… More >

  • ARTICLE

    Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model

    Yuwei Huang1, 2, Xiubo Chen1, 3, *, Kaiguo Yuan1, 3, Jianyi Zhang4, Biao Liu2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1771-1783, 2020, DOI:10.32604/cmc.2020.09905
    Abstract Directional modulation is one of the hot topics in data security researches. To fulfill the requirements of communication security in wireless environment with multiple paths, this study takes into account the factors of reflections and antenna radiation pattern for directional modulation. Unlike other previous works, a novel multiple-reflection model, which is more realistic and complex than simplified two-ray reflection models, is proposed based on two reflectors. Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array. The quantum approach has strengths in convergence speed and the globe searching ability for the… More >

  • ARTICLE

    Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets

    Chunlai Du1, Shenghui Liu1, Lei Si2, Yanhui Guo2, *, Tong Jin1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1785-1796, 2020, DOI:10.32604/cmc.2020.010091
    Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More >

  • ARTICLE

    Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography

    Yun Tan1, Jiaohua Qin1, *, Hao Tang2, Xuyu Xiang1, Ling Tan2, Neal N. Xiong3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1797-1817, 2020, DOI:10.32604/cmc.2020.010802
    Abstract With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and… More >

  • ARTICLE

    High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

    Jie Xu1, *, Qun Wang1, Yifan Wang1, Khan Asif2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1819-1844, 2020, DOI:10.32604/cmc.2020.010727
    Abstract Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors,… More >

  • ARTICLE

    Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration

    Jingjian Chen1, Gang Xu1, *, Fengqi Wei1, Liqiang He2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1845-1858, 2020, DOI:10.32604/cmc.2020.010010
    Abstract Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical throughput of the node on… More >

  • ARTICLE

    Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model

    Xiaokan Wang1, 2, *, Qiong Wang2, Shuang Liang3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1859-1867, 2020, DOI:10.32604/cmc.2020.011032
    Abstract Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a time-varying, time-delaying and nonlinear control… More >

  • ARTICLE

    Chinese Spirits Identification Model Based on Mid-Infrared Spectrum

    Wu Zeng1, Zhanxiong Huo1, *, Yuxuan Xie2, Yingxiang Jiang1, Kun Hu1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1869-1883, 2020, DOI:10.32604/cmc.2020.010139
    Abstract Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features but only achieved an 83%… More >

  • ARTICLE

    An Efficient Bar Code Image Recognition Algorithm for Sorting System

    Desheng Zheng1, *, Ziyong Ran1, Zhifeng Liu1, Liang Li2, Lulu Tian3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1885-1895, 2020, DOI:10.32604/cmc.2020.010070
    Abstract In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify the source image directly. Finally,… More >

  • ARTICLE

    Improving Chinese Word Representation with Conceptual Semantics

    Tingxin Wei1, 2, Weiguang Qu2, 3, *, Junsheng Zhou3, Yunfei Long4, Yanhui Gu3, Zhentao Xia3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1897-1913, 2020, DOI:10.32604/cmc.2020.010813
    Abstract The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning encoded in the representation of… More >

  • ARTICLE

    Privacy-Preserving Decision Protocols Based on Quantum Oblivious Key Distribution

    Kejia Zhang1, 2, 3, 4, Chunguang Ma5, Zhiwei Sun4, 6, *, Xue Zhang2, 3, Baomin Zhou2, Yukun Wang7
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1915-1928, 2020, DOI:10.32604/cmc.2020.09836
    Abstract Oblivious key transfer (OKT) is a fundamental problem in the field of secure multi-party computation. It makes the provider send a secret key sequence to the user obliviously, i.e., the user may only get almost one bit key in the sequence which is unknown to the provider. Recently, a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution (QOKD) from the well-known expression of quantum key distribution (QKD). In this paper, a new QOKD model is firstly proposed for the provider and user with limited quantum capabilities,… More >

  • ARTICLE

    A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

    Yongmei Zhang1, *, Jianzhe Ma2, Lei Hu3, Keming Yu4, Lihua Song1, 5, Huini Chen1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556
    Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract… More >

  • ARTICLE

    Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm

    Jinhui Ge1, Xiaoliang Liu2, *, Guo Liang3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1945-1958, 2020, DOI:10.32604/cmc.2020.010977
    Abstract With the expansion of the application scope of social computing problems, many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes, cultures, and the emotional needs of customers. The actual soft time window vehicle routing problem, speeding up the response of customer needs, improving distribution efficiency, and reducing operating costs is the focus of current social computing problems. Therefore, designing fast and effective algorithms to solve this problem has certain theoretical and practical significance. In this paper, considering the time delay problem of customer demand, the… More >

  • ARTICLE

    A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines

    Dunhong Yao1, 2, 3, Shijun Li4, *, Ang Li5, Yu Chen6
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1959-1975, 2020, DOI:10.32604/cmc.2020.010186
    Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as… More >

  • ARTICLE

    Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

    Kui Fu1, Jiansheng Peng1, 2, *, Hanxiao Zhang2, Xiaoliang Wang3, Frank Jiang4
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882
    Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

  • ARTICLE

    A Recommendation Approach Based on Bayesian Networks for Clone Refactor

    Ye Zhai1, *, Dongsheng Liu1, Celimuge Wu2, Rongrong She1
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1999-2012, 2020, DOI:10.32604/cmc.2020.09950
    Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are… More >

  • ARTICLE

    A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression

    Lejun Zhang1, Tianwen Huang1, Xiaoyan Hu1, Zhijie Zhang1, Weizheng Wang2, Donghai Guan3, *, Chunhui Zhao1, 4, Seokhoon Kim5
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2013-2030, 2020, DOI:10.32604/cmc.2020.011219
    Abstract Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel… More >

  • ARTICLE

    Bilateral Collaborative Optimization for Cloud Manufacturing Service

    Bin Xu1, 2, Yong Tang1, Yi Zhu1, Wenqing Yan1, Cheng He3, Jin Qi1, *
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2031-2042, 2020, DOI:10.32604/cmc.2020.011149
    Abstract Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing, which directly affect the quality of Cloud Manufacturing services. However, the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints. Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time, a Bilateral Collaborative Optimization Model of Cloud Manufacturing (BCOM-CMfg) is constructed in this paper. In BCOM-CMfg, to solve the manufacturing service scheduling problem on the supply side, a new… More >

  • ARTICLE

    Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

    Congcong Wang1, 2, 3, Pengyu Liu1, 2, 3, *, Kebin Jia1, 2, 3, Xiaowei Jia4, Yaoyao Li1, 2, 3
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505
    Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often… More >

  • ARTICLE

    Information Classification and Extraction on Official Web Pages of Organizations

    Jinlin Wang1, Xing Wang1, *, Hongli Zhang1, Binxing Fang1, Yuchen Yang1, Jianan Liu2
    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2057-2073, 2020, DOI:10.32604/cmc.2020.011158
    Abstract As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model.… More >

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