Home / Journals / CMC / Vol.69, No.3, 2021
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  • Open AccessOpen Access

    ARTICLE

    Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

    Jiyoung Yun, Mainak Basak, Myung-Mook Han*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2827-2843, 2021, DOI:10.32604/cmc.2021.017266
    (This article belongs to the Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract Coronavirus disease 2019 (COVID-19) has been termed a “Pandemic Disease” that has infected many people and caused many deaths on a nearly unprecedented level. As more people are infected each day, it continues to pose a serious threat to humanity worldwide. As a result, healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds. In most cases, healthy people experience tolerable symptoms if they are infected. However, in other cases, patients may suffer severe symptoms and require treatment in an intensive care unit. Thus, hospitals should select patients… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

    Ebrahim Heidary1, Hamïd Parvïn2,3,4,*, Samad Nejatian5,6, Karamollah Bagherifard1,6, Vahideh Rezaie6,7
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2845-2861, 2021, DOI:10.32604/cmc.2021.014361
    Abstract With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the More >

  • Open AccessOpen Access

    ARTICLE

    Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization

    Jagannath Paramguru1, Subrat Kumar Barik1, Ajit Kumar Barisal2, Gaurav Dhiman3, Rutvij H. Jhaveri4, Mohammed Alkahtani5,6, Mustufa Haider Abidi5,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2863-2882, 2021, DOI:10.32604/cmc.2021.016002
    (This article belongs to the Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue. Various non-linearity are added to make the fossil fuel-based power systems more practical. In order to achieve an accurate economical schedule, valve point loading effect, ramp rate constraints, and prohibited operating zones are being considered for realistic scenarios. In this paper, an improved, and modified version of conventional particle swarm optimization (PSO), called Oscillatory PSO (OPSO), is devised to provide a cheaper schedule with optimum cost. The conventional PSO is More >

  • Open AccessOpen Access

    ARTICLE

    Medical Feature Selection Approach Based on Generalized Normal Distribution Algorithm

    Mohamed Abdel-Basset1, Reda Mohamed1, Ripon K. Chakrabortty2, Michael J. Ryan2, Yunyoung Nam3,*, Mohamed Abouhawwash4,5
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2883-2901, 2021, DOI:10.32604/cmc.2021.017854
    Abstract This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance, redundancy, or less information; this pre-processing process is often known as feature selection. This technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization (GNDO) supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary values. Further, a novel… More >

  • Open AccessOpen Access

    ARTICLE

    Towards Privacy-Preserving Cloud Storage: A Blockchain Approach

    Jia-Shun Zhang1, Gang Xu2,*, Xiu-Bo Chen1, Haseeb Ahmad3, Xin Liu4, Wen Liu5,6,7
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2903-2916, 2021, DOI:10.32604/cmc.2021.017227
    Abstract With the rapid development of cloud computing technology, cloud services have now become a new business model for information services. The cloud server provides the IT resources required by customers in a self-service manner through the network, realizing business expansion and rapid innovation. However, due to the insufficient protection of data privacy, the problem of data privacy leakage in cloud storage is threatening cloud computing. To address the problem, we propose BC-PECK, a data protection scheme based on blockchain and public key searchable encryption. Firstly, all the data is protected by the encryption algorithm. The… More >

  • Open AccessOpen Access

    ARTICLE

    Cotton Leaf Diseases Recognition Using Deep Learning and Genetic Algorithm

    Muhammad Rizwan Latif1, Muhamamd Attique Khan1, Muhammad Younus Javed1, Haris Masood2, Usman Tariq3, Yunyoung Nam4,*, Seifedine Kadry5
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2917-2932, 2021, DOI:10.32604/cmc.2021.017364
    (This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Globally, Pakistan ranks 4 in cotton production, 6 as an importer of raw cotton, and 3 in cotton consumption. Nearly 10% of GDP and 55% of the country's foreign exchange earnings depend on cotton products. Approximately 1.5 million people in Pakistan are engaged in the cotton value chain. However, several diseases such as Mildew, Leaf Spot, and Soreshine affect cotton production. Manual diagnosis is not a good solution due to several factors such as high cost and unavailability of an expert. Therefore, it is essential to develop an automated technique that can accurately detect and recognize these… More >

  • Open AccessOpen Access

    ARTICLE

    SmartCrawler: A Three-Stage Ranking Based Web Crawler for Harvesting Hidden Web Sources

    Sawroop Kaur1, Aman Singh1,*, G. Geetha2, Mehedi Masud3, Mohammed A. Alzain4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2933-2948, 2021, DOI:10.32604/cmc.2021.019030
    Abstract Web crawlers have evolved from performing a meagre task of collecting statistics, security testing, web indexing and numerous other examples. The size and dynamism of the web are making crawling an interesting and challenging task. Researchers have tackled various issues and challenges related to web crawling. One such issue is efficiently discovering hidden web data. Web crawler’s inability to work with form-based data, lack of benchmarks and standards for both performance measures and datasets for evaluation of the web crawlers make it still an immature research domain. The applications like vertical portals and data integration… More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Forwarding Strategy in SDN-Enabled Named-Data IoV

    Asadullah Tariq1, Irfan ud din1, Rana Asif Rehman2, Byung-Seo Kim3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2949-2966, 2021, DOI:10.32604/cmc.2021.017658
    (This article belongs to the Special Issue: Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks)
    Abstract Internet of Vehicles (IoV), a rapidly growing technology for efficient vehicular communication and it is shifting the trend of traditional Vehicular Ad Hoc Networking (VANET) towards itself. The centralized management of IoV endorses its uniqueness and suitability for the Intelligent Transportation System (ITS) safety applications. Named Data Networking (NDN) is an emerging internet paradigm that fulfills most of the expectations of IoV. Limitations of the current IP internet architecture are the main motivation behind NDN. Software-Defined Networking (SDN) is another emerging networking paradigm of technology that is highly capable of efficient management of overall networks… More >

  • Open AccessOpen Access

    ARTICLE

    An Efficient Hybrid PAPR Reduction for 5G NOMA-FBMC Waveforms

    Arun Kumar1,*, Sivabalan Ambigapathy2, Mehedi Masud3, Emad Sami Jaha4, Sumit Chakravarty5, Kanchan Sengar1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2967-2981, 2021, DOI:10.32604/cmc.2021.019092
    Abstract The article introduces Non-Orthogonal Multiple Access (NOMA) and Filter Bank Multicarrier (FBMC), known as hybrid waveform (NOMA-FBMC), as two of the most deserving contenders for fifth-generation (5G) network. High spectrum access and clampdown of spectrum outflow are unique characteristics of hybrid NOMA-FBMC. We compare the spectral efficiency of Orthogonal Frequency Division Multiplexing (OFDM), FBMC, NOMA, and NOMA-FBMC. It is seen that the hybrid waveform outperforms the existing waveforms. Peak to Average Power Ratio (PAPR) is regarded as a significant issue in multicarrier waveforms. The combination of Selective Mapping-Partial Transmit Sequence (SLM-PTS) is an effective way More >

  • Open AccessOpen Access

    ARTICLE

    Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

    El-Sayed M. El-kenawy1, Hattan F. Abutarboush2, Ali Wagdy Mohamed3,4, Abdelhameed Ibrahim5,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2983-2995, 2021, DOI:10.32604/cmc.2021.019114
    (This article belongs to the Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract Machine learning (ML) has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls. ML is a massive area within artificial intelligence (AI) that focuses on obtaining valuable information out of data, explaining why ML has often been related to stats and data science. An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design. The algorithm is designed, depending on the hybrid between the Sine Cosine Algorithm (SCA) and the Grey Wolf Optimizer More >

  • Open AccessOpen Access

    ARTICLE

    An AMC-Based Circularly Polarized Antenna for 5G sub-6 GHz Communications

    Hussain Askari, Niamat Hussain, Domin Choi, Md. Abu Sufian, Anees Abbas, Nam Kim*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2997-3013, 2021, DOI:10.32604/cmc.2021.018855
    (This article belongs to the Special Issue: Advances in 5G Antenna Designs and Systems)
    Abstract This paper presents an AMC (artificial magnetic conductor)-based wideband circularly polarized printed monopole antenna for unidirectional radiation. The antenna includes an AMC reflector, a coplanar waveguide (CPW) feed structure to excite the antenna, a ground plane with a rectangular slot on the left side of feedline, and an asymmetrical ground plane on its right side. The induced surface currents on CWP feedline, rectangularly slotted, and asymmetrical ground planes cause circularly polarized radiations. The AMC reflector consisting periodic metallic square patches is used instead of the conventional PEC (perfect electric conductor) reflector, the distance between the More >

  • Open AccessOpen Access

    ARTICLE

    YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

    Chintakindi Balaram Murthy1, Mohammad Farukh Hashmi1, Ghulam Muhammad2,3,*, Salman A. AlQahtani2,3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3015-3031, 2021, DOI:10.32604/cmc.2021.018781
    (This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature… More >

  • Open AccessOpen Access

    ARTICLE

    Double Encryption Using Trigonometric Chaotic Map and XOR of an Image

    Orawit Thinnukool1, Thammarat Panityakul2, Mahwish Bano3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3033-3046, 2021, DOI:10.32604/cmc.2021.019153
    Abstract In the most recent decades, a major number of image encryption plans have been proposed. The vast majority of these plans reached a high-security level; however, their moderate speeds because of their complicated processes made them of no use in real-time applications. Inspired by this, we propose another efficient and rapid image encryption plan dependent on the Trigonometric chaotic guide. In contrast to the most of current plans, we utilize this basic map to create just a couple of arbitrary rows and columns. Moreover, to additionally speed up, we raise the processing unit from the More >

  • Open AccessOpen Access

    ARTICLE

    Effect of Weather on the Spread of COVID-19 Using Eigenspace Decomposition

    Manar A. Alqudah1, Thabet Abdeljawad2,3,4,*, Anwar Zeb5, Izaz Ullah Khan5, Fatma Bozkurt6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3047-3063, 2021, DOI:10.32604/cmc.2021.017752
    (This article belongs to the Special Issue: Role of Computer in Modelling & Solving Real-World Problems)
    Abstract Since the end of 2019, the world has suffered from a pandemic of the disease called COVID-19. WHO reports show approximately 113 M confirmed cases of infection and 2.5 M deaths. All nations are affected by this nightmare that continues to spread. Widespread fear of this pandemic arose not only from the speed of its transmission: a rapidly changing “normal life” became a fear for everyone. Studies have mainly focused on the spread of the virus, which showed a relative decrease in high temperature, low humidity, and other environmental conditions. Therefore, this study targets the… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization of the Active Composition of the Wind Farm Using Genetic Algorithms

    Nataliya Shakhovska1,*, Mykola Medykovskyy2, Roman Melnyk2, Nataliya Kryvinska3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3065-3078, 2021, DOI:10.32604/cmc.2021.018761
    Abstract The article presents the results of research on the possibilities of using genetic algorithms for solving the multicriteria optimization problem of determining the active components of a wind farm. Optimization is carried out on two parameters: efficiency factor of wind farm use (integrated parameter calculated on the basis of 6 parameters of each of the wind farm), average power deviation level (average difference between the load power and energy generation capabilities of the active wind farm). That was done an analysis of publications on the use of genetic algorithms to solve multicriteria optimization problems. Computer… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Trust Based Access Control for Multi-Cloud Environment

    N. R. Rejin Paul1,*, D. Paul Raj2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3079-3093, 2021, DOI:10.32604/cmc.2021.018993
    Abstract Security is an essential part of the cloud environment. For ensuring the security of the data being communicated to and from the cloud server, a significant parameter called trust was introduced. Trust-based security played a vital role in ensuring that the communication between cloud users and service providers remained unadulterated and authentic. In most cloud-based data distribution environments, emphasis is placed on accepting trusted client users’ requests, but the cloud servers’ integrity is seldom verified. This paper designs a trust-based access control model based on user and server characteristics in a multi-cloud environment to address… More >

  • Open AccessOpen Access

    ARTICLE

    Using Big Data to Discover Chaos in China’s Futures Market During COVID-19

    Lin Tie1, Bin Huang1, Bin Pan1, Guang Sun1,2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3095-3107, 2021, DOI:10.32604/cmc.2021.019363
    Abstract COVID-19 was first reported in China and quickly spread throughout the world. Weak investor confidence in government efforts to control the pandemic seriously affected global financial markets. This study investigated chaos in China’s futures market during COVID-19, focusing on the degree of chaos at different periods during the pandemic. We constructed a phase diagram to observe the attractor trajectory of index futures (IFs). During the COVID-19 outbreak, overall chaos in China’s futures market was increasing, and there was a clear correlation between market volatility and the macroenvironment (mainly government regulation). The Hurst index, calculated by More >

  • Open AccessOpen Access

    ARTICLE

    An Intelligent Gestational Diabetes Diagnosis Model Using Deep Stacked Autoencoder

    A. Sumathi1,*, S. Meganathan1, B. Vijila Ravisankar2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612
    Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the… More >

  • Open AccessOpen Access

    ARTICLE

    Denoising Medical Images Using Deep Learning in IoT Environment

    Sujeet More1, Jimmy Singla1, Oh-Young Song2,*, Usman Tariq3, Sharaf Malebary4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230
    (This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
    Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for More >

  • Open AccessOpen Access

    ARTICLE

    Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

    Yu-Dong Zhang1, Muhammad Attique Khan2, Ziquan Zhu3, Shui-Hua Wang4,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040
    (This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way… More >

  • Open AccessOpen Access

    ARTICLE

    FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

    M. Iyapparaja1, M. Sathish Kumar1, S. Siva Rama Krishnan1, Chiranji Lal Chowdhary1, Byungun Yoon2, Saurabh Singh2, Gi Hwan Cho3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3163-3178, 2021, DOI:10.32604/cmc.2021.017302
    (This article belongs to the Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)
    Abstract Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the… More >

  • Open AccessOpen Access

    ARTICLE

    Cloud Data Center Selection Using a Modified Differential Evolution

    Yousef Sanjalawe1,2, Mohammed Anbar1,*, Salam Al-E’mari1, Rosni Abdullah1, Iznan Hasbullah1, Mohammed Aladaileh1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3179-3204, 2021, DOI:10.32604/cmc.2021.018546
    Abstract The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector. Cloud data center selection challenges are compounded by ever-increasing users’ requests and the number of data centers required to execute these requests. Cloud service broker policy defines cloud data center’s selection, which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution. Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness, and it is well suited for selecting an More >

  • Open AccessOpen Access

    ARTICLE

    CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

    Ziqing Yan1,2, Pengpeng Yang1,2, Rongrong Ni1,2,*, Yao Zhao1,2, Hairong Qi3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3205-3216, 2021, DOI:10.32604/cmc.2021.020324
    Abstract As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is… More >

  • Open AccessOpen Access

    ARTICLE

    Mental Illness Disorder Diagnosis Using Emotion Variation Detection from Continuous English Speech

    S. Lalitha1, Deepa Gupta2,*, Mohammed Zakariah3, Yousef Ajami Alotaibi3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3217-3238, 2021, DOI:10.32604/cmc.2021.018406
    (This article belongs to the Special Issue: Recent Trends in Machine Intelligence respected to Medical Field Applications)
    Abstract Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single emotion. Limited work with standalone speech emotion recognition (SER) systems proposed for continuous speech only has been reported. In the recent decade, various effective SER systems have been proposed for discrete speech, i.e., short speech phrases. It would be more helpful if these systems could also recognize emotions from continuous speech. However, if these systems are applied directly to test emotions from continuous speech, emotion recognition performance would not be… More >

  • Open AccessOpen Access

    ARTICLE

    Gastrointestinal Tract Infections Classification Using Deep Learning

    Muhammad Ramzan1, Mudassar Raza1, Muhammad Sharif1, Muhammad Attique Khan2, Yunyoung Nam3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920
    (This article belongs to the Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)
    Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the… More >

  • Open AccessOpen Access

    ARTICLE

    Automatic Detection of COVID-19 Using a Stacked Denoising Convolutional Autoencoder

    Habib Dhahri1,2,*, Besma Rabhi3, Slaheddine Chelbi4, Omar Almutiry1, Awais Mahmood1, Adel M. Alimi3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3259-3274, 2021, DOI:10.32604/cmc.2021.018449
    (This article belongs to the Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract The exponential increase in new coronavirus disease 2019 ({COVID-19}) cases and deaths has made COVID-19 the leading cause of death in many countries. Thus, in this study, we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images. A stacked denoising convolutional autoencoder (SDCA) model was proposed to classify X-ray images into three classes: normal, pneumonia, and {COVID-19}. The SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy images. The proposed model’s architecture mainly composed of eight autoencoders, More >

  • Open AccessOpen Access

    ARTICLE

    Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms

    Chuanrong Wu1,*, Haotian Cui1, Zhi Lu2, Xiaoming Yang3, Mark E. McMurtrey4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3275-3287, 2021, DOI:10.32604/cmc.2021.019969
    Abstract Big data knowledge, such as customer demands and consumer preferences, is among the crucial external knowledge that firms need for new product development in the big data environment. Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients. This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients: subscription pricing and pay-per-use pricing. We find that: (1) the subscription price of big data knowledge has no effect on… More >

  • Open AccessOpen Access

    ARTICLE

    Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

    Ahmed Y. Hamed1,*, Monagi H. Alkinani2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3289-3301, 2021, DOI:10.32604/cmc.2021.018658
    Abstract Task scheduling is the main problem in cloud computing that reduces system performance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different… More >

  • Open AccessOpen Access

    ARTICLE

    An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

    P. Naga Srinivasu1, Shakeel Ahmed2,*, Abdulaziz Alhumam2, Akash Bhoi Kumar3, Muhammad Fazal Ijaz4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3303-3319, 2021, DOI:10.32604/cmc.2021.018472
    Abstract In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training,… More >

  • Open AccessOpen Access

    ARTICLE

    An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

    Shabana Habib1, Noreen Fayyaz Khan2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3321-3335, 2021, DOI:10.32604/cmc.2021.015504
    Abstract Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

    Fatmah Abdulrahman Baothman*, Osama Ahmed Abulnaja, Fatima Jafar Muhdher
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3337-3363, 2021, DOI:10.32604/cmc.2021.017637
    (This article belongs to the Special Issue: Intelligent Big Data Management and Machine Learning Techniques for IoT-Enabled Pervasive Computing)
    Abstract Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements; time, capacity, speed, and culture. Cross-cultural variations are increasing the complexity level because each mass and event have different characteristics and challenges. However, no prior study has employed the six Hofstede Cultural Dimensions (HCD) for predicting crowd behaviors. This study aims to develop the Cultural Crowd-Artificial Neural Network (CC-ANN) learning model that considers crowd’s HCD to predict their physical (distance and speed) and social (collectivity and cohesion) characteristics. The model was developed towards a cognitive intelligent decision support tool where the… More >

  • Open AccessOpen Access

    ARTICLE

    Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

    Imran Ahmed1,*, Humaira Sardar1, Hanan Aljuaid2, Fakhri Alam Khan1, Muhammad Nawaz1, Adnan Awais1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3365-3381, 2021, DOI:10.32604/cmc.2021.018486
    (This article belongs to the Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
    Abstract Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can… More >

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    ARTICLE

    A Material Identification Approach Based on Wi-Fi Signal

    Chao Li1, Fan Li1,2, Wei Du3, Lihua Yin1,*, Bin Wang4, Chonghua Wang5, Tianjie Luo1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3383-3397, 2021, DOI:10.32604/cmc.2021.020765
    Abstract Material identification is a technology that can help to identify the type of target material. Existing approaches depend on expensive instruments, complicated pre-treatments and professional users. It is difficult to find a substantial yet effective material identification method to meet the daily use demands. In this paper, we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier, which can significantly reduce the cost and guarantee a high level accuracy. In practical measurement of Wi-Fi based material identification, these two features are… More >

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    ARTICLE

    Recurrent Convolutional Neural Network MSER-Based Approach for Payable Document Processing

    Suliman Aladhadh1, Hidayat Ur Rehman2, Ali Mustafa Qamar3,4,*, Rehan Ullah Khan1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3399-3411, 2021, DOI:10.32604/cmc.2021.018724
    (This article belongs to the Special Issue: Emerging Trends in Artificial Intelligence and Machine Learning)
    Abstract A tremendous amount of vendor invoices is generated in the corporate sector. To automate the manual data entry in payable documents, highly accurate Optical Character Recognition (OCR) is required. This paper proposes an end-to-end OCR system that does both localization and recognition and serves as a single unit to automate payable document processing such as cheques and cash disbursement. For text localization, the maximally stable extremal region is used, which extracts a word or digit chunk from an invoice. This chunk is later passed to the deep learning model, which performs text recognition. The deep… More >

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    ARTICLE

    CNR: A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

    Mahmood ul Hassan1,*, Amin Al-Awady1, Khalid Mahmood2, Shahzad Ali3, Ibrahim Algamdi1, Muhammad Kashif Saeed4, Safdar Zaman5
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3413-3427, 2021, DOI:10.32604/cmc.2021.018544
    Abstract Wireless Sensor Networks (WSNs) are an integral part of the Internet of Things (IoT) and are widely used in a plethora of applications. Typically, sensor networks operate in harsh environments where human intervention is often restricted, which makes battery replacement for sensor nodes impractical. Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network. Without a connectivity restoration mechanism, node failures ultimately lead to a network partition, which affects the basic function of the sensor network. Therefore, the research community actively concentrates on addressing and solving… More >

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    ARTICLE

    A Compromise Programming to Task Assignment Problem in Software Development Project

    Ngo Tung Son1,2,*, Jafreezal Jaafar1, Izzatdin Abdul Aziz1, Bui Ngoc Anh2, Hoang Duc Binh2, Muhammad Umar Aftab3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3429-3444, 2021, DOI:10.32604/cmc.2021.017710
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional… More >

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    ARTICLE

    Road Distance Computation Using Homomorphic Encryption in Road Networks

    Haining Yu1, Lailai Yin1,*, Hongli Zhang1, Dongyang Zhan1,2, Jiaxing Qu3, Guangyao Zhang4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3445-3458, 2021, DOI:10.32604/cmc.2021.019462
    Abstract Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is More >

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    ARTICLE

    Screening of COVID-19 Patients Using Deep Learning and IoT Framework

    Harshit Kaushik1, Dilbag Singh2, Shailendra Tiwari3, Manjit Kaur2, Chang-Won Jeong4, Yunyoung Nam5,*, Muhammad Attique Khan6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3459-3475, 2021, DOI:10.32604/cmc.2021.017337
    (This article belongs to the Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we… More >

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    ARTICLE

    A Vicenary Analysis of SARS-CoV-2 Genomes

    Sk Sarif Hassan1, Ranjeet Kumar Rout2, Kshira Sagar Sahoo3, Nz Jhanjhi4, Saiyed Umer5, Thamer A. Tabbakh6,*, Zahrah A. Almusaylim7
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3477-3493, 2021, DOI:10.32604/cmc.2021.017206
    Abstract Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their… More >

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    ARTICLE

    An Efficient Lightweight Authentication and Key Agreement Protocol for Patient Privacy

    Seyed Amin Hosseini Seno1, Mahdi Nikooghadam1, Rahmat Budiarto2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3495-3512, 2021, DOI:10.32604/cmc.2021.019051
    (This article belongs to the Special Issue: Advances of AI and Blockchain technologies for Future Smart City)
    Abstract Tele-medical information system provides an efficient and convenient way to connect patients at home with medical personnel in clinical centers. In this system, service providers consider user authentication as a critical requirement. To address this crucial requirement, various types of validation and key agreement protocols have been employed. The main problem with the two-way authentication of patients and medical servers is not built with thorough and comprehensive analysis that makes the protocol design yet has flaws. This paper analyzes carefully all aspects of security requirements including the perfect forward secrecy in order to develop an… More >

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    ARTICLE

    TBDDoSA-MD: Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network (SDVN)

    Rajendra Prasad Nayak1, Srinivas Sethi2, Sourav Kumar Bhoi3, Kshira Sagar Sahoo4, Nz Jhanjhi5, Thamer A. Tabbakh6, Zahrah A. Almusaylim7,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3513-3529, 2021, DOI:10.32604/cmc.2021.018930
    Abstract Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in… More >

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    ARTICLE

    Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

    Nora Shoaip1, Amira Rezk1, Shaker EL-Sappagh2,3, Tamer Abuhmed4,*, Sherif Barakat1, Mohammed Elmogy5
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3531-3548, 2021, DOI:10.32604/cmc.2021.019069
    Abstract Alzheimer’s disease (AD) is a very complex disease that causes brain failure, then eventually, dementia ensues. It is a global health problem. 99% of clinical trials have failed to limit the progression of this disease. The risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical symptoms. Therapies for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological dysfunction. In this regard, the need becomes more urgent for biomarker-based detection. A key issue in understanding AD is the need… More >

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    ARTICLE

    A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

    Wei Sun1,2,*, Xuan Chen3, Xiaorui Zhang1,3, Guangzhao Dai2, Pengshuai Chang2, Xiaozheng He4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3549-3561, 2021, DOI:10.32604/cmc.2021.021627
    Abstract Vehicle re-identification (ReID) aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera scenario. It has gradually become a core technology of intelligent transportation system. Most existing vehicle re-identification models adopt the joint learning of global and local features. However, they directly use the extracted global features, resulting in insufficient feature expression. Moreover, local features are primarily obtained through advanced annotation and complex attention mechanisms, which require additional costs. To solve this issue, a multi-feature learning model with enhanced local attention for vehicle re-identification (MFELA)… More >

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    ARTICLE

    Fake News Detection on Social Media: A Temporal-Based Approach

    Yonghun Jang, Chang-Hyeon Park, Dong-Gun Lee, Yeong-Seok Seo*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3563-3579, 2021, DOI:10.32604/cmc.2021.018901
    (This article belongs to the Special Issue: Advances of AI and Blockchain technologies for Future Smart City)
    Abstract Following the development of communication techniques and smart devices, the era of Artificial Intelligence (AI) and big data has arrived. The increased connectivity, referred to as hyper-connectivity, has led to the development of smart cities. People in these smart cities can access numerous online contents and are always connected. These developments, however, also lead to a lack of standardization and consistency in the propagation of information throughout communities due to the consumption of information through social media channels. Information cannot often be verified, which can confuse the users. The increasing influence of social media has… More >

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    ARTICLE

    Mango Leaf Disease Identification Using Fully Resolution Convolutional Network

    Rabia Saleem1, Jamal Hussain Shah1,*, Muhammad Sharif1, Ghulam Jillani Ansari2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3581-3601, 2021, DOI:10.32604/cmc.2021.017700
    (This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)
    Abstract Due to the high demand for mango and being the king of all fruits, it is the need of the hour to curb its diseases to fetch high returns. Automatic leaf disease segmentation and identification are still a challenge due to variations in symptoms. Accurate segmentation of the disease is the key prerequisite for any computer-aided system to recognize the diseases, i.e., Anthracnose, apical-necrosis, etc., of a mango plant leaf. To solve this issue, we proposed a CNN based Fully-convolutional-network (FrCNnet) model for the segmentation of the diseased part of the mango leaf. The proposed… More >

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    ARTICLE

    An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

    Abid Sohail1, Ammar Haseeb1, Mobashar Rehman2,*, Dhanapal Durai Dominic3, Muhammad Arif Butt4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3603-3618, 2021, DOI:10.32604/cmc.2021.017795
    (This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between More >

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    ARTICLE

    Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

    Yuan Li1,7, Jiaxuan Xie1, Mu Xia2, Qianqian Li3, Meng Li4, Lei Guo5,*, Zhen Zhang6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3619-3628, 2021, DOI:10.32604/cmc.2021.016187
    Abstract The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed More >

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    ARTICLE

    Augmented Node Placement Model in -WSN Through Multiobjective Approach

    Kalaipriyan Thirugnansambandam1, Debnath Bhattacharyya2, Jaroslav Frnda3, Dinesh Kumar Anguraj2, Jan Nedoma4,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3629-3644, 2021, DOI:10.32604/cmc.2021.018939
    Abstract In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in the target-based region. The linear objective is to find the positions to cover the complete target nodes and connectivity between each sensor for data forwarding towards the base station given a grid with target points and a potential sensor placement position. In this paper, a multiobjective problem on target-based WSN (t-WSN) is derived, which minimizes the number of deployed nodes, and maximizes the cost of coverage and sensing range. An Evolutionary-based Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is incorporated to tackle this multiobjective problem… More >

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    ARTICLE

    Simulation of Lumbar Spinal Stenosis Using the Finite Element Method

    Din Prathumwan1, Inthira Chaiya2, Kamonchat Trachoo2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3645-3657, 2021, DOI:10.32604/cmc.2021.018241
    Abstract Lumbar spine stenosis (LSS) is a narrowing of the spinal canal that results in pressure on the spinal nerves. This orthopedic disorder can cause severe pain and dysfunction. LSS is a common disabling problem amongst elderly people. In this paper, we developed a finite element model (FEM) to study the forces and the von Mises stress acting on the spine when people bend down. An artificial lumbar spine (L3) was generated from CT data by using the FEM, which is a powerful tool to study biomechanics. The proposed model is able to predict the effect… More >

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    Management of Schemes and Threat Prevention in ICS Partner Companies Security

    Sangdo Lee1, Jun-Ho Huh2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3659-3684, 2021, DOI:10.32604/cmc.2021.015632
    (This article belongs to the Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
    Abstract An analysis of the recent major security incidents related to industrial control systems, revealed that most had been caused by company employees. Therefore, enterprise security management systems have been developed to focus on companies’ personnel. Nonetheless, several hacking incidents, involving major companies and public/financial institutions, were actually attempted by the cooperative firms or the outsourced manpower undertaking maintenance work. Specifically, institutions that operate industrial control systems (ICSs) associated with critical national infrastructures, such as traffic or energy, have contracted several cooperative firms. Nonetheless, ICT's importance is gradually increasing, due to outsourcing, and is the most… More >

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