Journals / CMC / Vol.69, No.3
Table of Content


  • 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 this 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 who have a high risk… More >

  • 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 input text, which improves the… More >

  • 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 this 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 improved by deriving a mechanism… More >

  • 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 restarting strategy (RS) is proposed… More >

  • 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 privacy data is encrypted and… More >

  • 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 this 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 diseases at their… More >

  • 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 require hidden web crawling. Most… More >

  • 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 this 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 and transforming complex networking architectures… More >

  • 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 to minimize large peak power… More >

  • 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 this 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 (GWO), to train neural network-based… More >

  • 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 this 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 antenna and reflector is reduced… More >

  • 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 this 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 extraction ability. In addition, one… More >

  • 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 pixel level to the row/column… More >

  • 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 this 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 effect of weather in considering… More >

  • 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 simulations were performed, which allowed… More >

  • 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 this issue. The proposed methodology… More >

  • 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 rescaled range (R/S) analysis, was… More >

  • 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 proposed OD-DSAE model is to… More >

  • 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 this 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 building a dictionary learning technique… More >

  • 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 this 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 data augmentation is chosen… More >

  • 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 this 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 Markovian queuing model that helps… More >

  • 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 appropriate cloud data center. This… More >

  • 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 still a challenging task. In… More >

  • 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 this 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 similar to that achieved… More >

  • 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 this 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 diagnosis and classification of GI… More >

  • 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 this 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, which were fed to two… More >

  • 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 the optimal time of knowledge… More >

  • 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 tasks. The proposed algorithm aims… More >

  • 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, unlike in supervisory procedures requiring… More >

  • 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 to quickly learn the features… More >

  • 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 this 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 predicted characteristics affect the estimated… More >

  • 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 this 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 negatively affect the performance of… More >

  • 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 commonly interrupted by the software/hardware… More >

  • 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 this 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 learning model utilizes both convolution… More >

  • 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 the challenges associated with connectivity… More >

  • 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 this 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 rules. To support the decision-maker… More >

  • 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 designed by using Partially Homomorphic… More >

  • 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 this 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 implement a cloud platform for… More >

  • 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 chemical composition. Purines include adenine… More >

  • 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 this 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 efficient and robust lightweight authentication… More >

  • 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 that network will no longer… More >

  • 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 to solve complex and high-dimensional… More >

  • 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) is proposed in this paper.… More >

  • 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 this 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 thus led to the emergence… More >

  • 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 this 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 FrCNnet directly learns the features… More >

  • 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 this 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 them. However, neither a benchmark… More >

  • 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 to maximize the market advantage… More >

  • 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 efficiently. Multiobjective problems are… More >

  • 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 of forces which apply to… More >

  • 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 this 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 vulnerable factor in security. This… More >

  • ARTICLE

    Implementation of Legendre Neural Network to Solve Time-Varying Singular Bilinear Systems

    V. Murugesh1, B. Saravana Balaji2,*, Habib Sano Aliy3, J. Bhuvana4, P. Saranya5, Andino Maseleno6, K. Shankar7, A. Sasikala8
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3685-3692, 2021, DOI:10.32604/cmc.2021.017836
    Abstract Bilinear singular systems can be used in the investigation of different types of engineering systems. In the past decade, considerable attention has been paid to analyzing and synthesizing singular bilinear systems. Their importance lies in their real world application such as economic, ecological, and socioeconomic processes. They are also applied in several biological processes, such as population dynamics of biological species, water balance, temperature regulation in the human body, carbon dioxide control in lungs, blood pressure, immune system, cardiac regulation, etc. Bilinear singular systems naturally represent different physical processes such as the fundamental law of mass action, the DC motor,… More >

  • ARTICLE

    Monarch Butterfly Optimization for Reliable Scheduling in Cloud

    B. Gomathi1, S. T. Suganthi2,*, Karthikeyan Krishnasamy3, J. Bhuvana4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3693-3710, 2021, DOI:10.32604/cmc.2021.018159
    Abstract Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources. The scheduling of the cloud tasks is a well-recognized NP-hard problem. The Task scheduling problem is convoluted while convincing different objectives, which are dispute in nature. In this paper, Multi-Objective Improved Monarch Butterfly Optimization (MOIMBO) algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions. Three different dispute objectives, such as makespan, reliability, and resource utilization, are deliberated for task scheduling problems.The Epsilon-fuzzy dominance sort method is utilized in the multi-objective domain to elect the foremost… More >

  • ARTICLE

    EDSM-Based Binary Protocol State Machine Reversing

    Shen Wang1,*, Fanghui Sun1, Hongli Zhang1, Dongyang Zhan1,2, Shuang Li3, Jun Wang1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3711-3725, 2021, DOI:10.32604/cmc.2021.016562
    Abstract Internet communication protocols define the behavior rules of network components when they communicate with each other. With the continuous development of network technologies, many private or unknown network protocols are emerging in endlessly various network environments. Herein, relevant protocol specifications become difficult or unavailable to translate in many situations such as network security management and intrusion detection. Although protocol reverse engineering is being investigated in recent years to perform reverse analysis on the specifications of unknown protocols, most existing methods have proven to be time-consuming with limited efficiency, especially when applied on unknown protocol state machines. This paper proposes a… More >

  • ARTICLE

    Centralized QoS Routing Model for Delay/Loss Sensitive Flows at the SDN-IoT Infrastructure

    Mykola Beshley1, Natalia Kryvinska2,*, Halyna Beshley1, Mykhailo Medvetskyi1, Leonard Barolli3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3727-3748, 2021, DOI:10.32604/cmc.2021.018625
    (This article belongs to this Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)
    Abstract The rapidly increasing number of Internet of Things (IoT) devices and Quality of Service (QoS) requirements have made the provisioning of network solutions to meet this demand a major research topic. Providing fast and reliable routing paths based on the QoS requirements of IoT devices is very important task for Industry 4.0. The software-defined network is one of the most current interesting research developments, offering an efficient and effective solution for centralized control and network intelligence. A new SDN-IoT paradigm has been proposed to improve network QoS, taking advantage of SDN architecture in IoT networks. At the present time, most… More >

  • ARTICLE

    Dynamic Voting Classifier for Risk Identification in Supply Chain 4.0

    Abdullah Ali Salamai1, El-Sayed M. El-kenawy2, Ibrahim Abdelhameed3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3749-3766, 2021, DOI:10.32604/cmc.2021.018179
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Supply chain 4.0 refers to the fourth industrial revolution’s supply chain management systems, which integrate the supply chain’s manufacturing operations, information technology, and telecommunication processes. Although supply chain 4.0 aims to improve supply chains’ production systems and profitability, it is subject to different operational and disruptive risks. Operational risks are a big challenge in the cycle of supply chain 4.0 for controlling the demand and supply operations to produce and deliver products across IT systems. This paper proposes a voting classifier to identify the operational risks in the supply chain 4.0 based on a Sine Cosine Dynamic Group (SCDG) algorithm.… More >

  • ARTICLE

    Modeling CO2 Emission of Middle Eastern Countries Using Intelligent Methods

    Mamdouh El Haj Assad1, Ibrahim Mahariq2,*, Zaher Al Barakeh2, Mahmoud Khasawneh2, Mohammad Ali Amooie3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3767-3781, 2021, DOI:10.32604/cmc.2021.018872
    (This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)
    Abstract CO2 emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities. Consequently, these factors must be considered for CO2 emission prediction for seven middle eastern countries including Iran, Kuwait, United Arab Emirates, Turkey, Saudi Arabia, Iraq and Qatar. In order to propose a predictive model, a Multilayer Perceptron Artificial Neural Network (MLP ANN) is applied. Three transfer functions including logsig, tansig and radial basis functions are utilized in the hidden layer of the network. Moreover, various numbers of neurons are applied in the structure of the models. It… More >

  • ARTICLE

    Container Introspection: Using External Management Containers to Monitor Containers in Cloud Computing

    Dongyang Zhan1,*, Kai Tan1, Lin Ye1,2, Haining Yu1,3, Hao Liu4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3783-3794, 2021, DOI:10.32604/cmc.2021.019432
    Abstract Cloud computing plays an important role in today's Internet environment, which meets the requirements of scalability, security and reliability by using virtualization technologies. Container technology is one of the two mainstream virtualization solutions. Its lightweight, high deployment efficiency make container technology widely used in large-scale cloud computing. While container technology has created huge benefits for cloud service providers and tenants, it cannot meet the requirements of security monitoring and management from a tenant perspective. Currently, tenants can only run their security monitors in the target container, but it is not secure because the attacker is able to detect and compromise… More >

  • ARTICLE

    Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS

    Amer I. Al-Omari1, Amal S. Hassan2, Heba F. Nagy2, Ayed R. A. Al-Anzi3,*, Loai Alzoubi1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3795-3811, 2021, DOI:10.32604/cmc.2021.019061
    Abstract This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution. Assuming that the observed samples are taken from the upper record ranked set sampling (URRSS) and upper record values (URV) schemes. Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error, linear exponential and precautionary loss functions, in addition, we obtain Bayesian credible intervals. The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution. Then, the behavior of the estimates is examined at various record values. The output of the study… More >

  • ARTICLE

    An Optimized Convolutional Neural Network Architecture Based on Evolutionary Ensemble Learning

    Qasim M. Zainel1, Murad B. Khorsheed2, Saad Darwish3,*, Amr A. Ahmed4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3813-3828, 2021, DOI:10.32604/cmc.2021.014759
    (This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)
    Abstract Convolutional Neural Networks (CNNs) models succeed in vast domains. CNNs are available in a variety of topologies and sizes. The challenge in this area is to develop the optimal CNN architecture for a particular issue in order to achieve high results by using minimal computational resources to train the architecture. Our proposed framework to automated design is aimed at resolving this problem. The proposed framework is focused on a genetic algorithm that develops a population of CNN models in order to find the architecture that is the best fit. In comparison to the co-authored work, our proposed framework is concerned… More >

  • ARTICLE

    Classification of Retroviruses Based on Genomic Data Using RVGC

    Khalid Mahmood Aamir1, Muhammad Bilal2, Muhammad Ramzan1,3, Muhammad Attique Khan4, Yunyoung Nam5,*, Seifedine Kadry6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3829-3844, 2021, DOI:10.32604/cmc.2021.017835
    (This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)
    Abstract Retroviruses are a large group of infectious agents with similar virion structures and replication mechanisms. AIDS, cancer, neurologic disorders, and other clinical conditions can all be fatal due to retrovirus infections. Detection of retroviruses by genome sequence is a biological problem that benefits from computational methods. The National Center for Biotechnology Information (NCBI) promotes science and health by making biomedical and genomic data available to the public. This research aims to classify the different types of rotavirus genome sequences available at the NCBI. First, nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing stage. Based on… More >

  • ARTICLE

    FastAFLGo: Toward a Directed Greybox Fuzzing

    Chunlai Du1, Tong Jin1, Yanhui Guo2,*, Binghao Jia1, Bin Li3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3845-3855, 2021, DOI:10.32604/cmc.2021.017697
    Abstract While the size and complexity of software are rapidly increasing, not only is the number of vulnerabilities increasing, but their forms are diversifying. Vulnerability has become an important factor in network attack and defense. Therefore, automatic vulnerability discovery has become critical to ensure software security. Fuzzing is one of the most important methods of vulnerability discovery. It is based on the initial input, i.e., a seed, to generate mutated test cases as new inputs of a tested program in the next execution loop. By monitoring the path coverage, fuzzing can choose high-value test cases for inclusion in the new seed… More >

  • ARTICLE

    Local Features-Based Watermarking for Image Security in Social Media

    Shady Y. El-mashad1, Amani M. Yassen1, Abdulwahab K. Alsammak1, Basem M. Elhalawany2,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3857-3870, 2021, DOI:10.32604/cmc.2021.018660
    Abstract The last decade shows an explosion of using social media, which raises several challenges related to the security of personal files including images. These challenges include modifying, illegal copying, identity fraud, copyright protection and ownership of images. Traditional digital watermarking techniques embed digital information inside another digital information without affecting the visual quality for security purposes. In this paper, we propose a hybrid digital watermarking and image processing approach to improve the image security level. Specifically, variants of the widely used Least-Significant Bit (LSB) watermarking technique are merged with a blob detection algorithm to embed information into the boundary pixels… More >

  • ARTICLE

    Prediction of the Slope Solute Loss Based on BP Neural Network

    Xiaona Zhang1,*, Jie Feng2, Zhiguo Yu1, Zhen Hong3, Xinge Yun1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3871-3888, 2021, DOI:10.32604/cmc.2021.020057
    Abstract The existence of soil macropores is a common phenomenon. Due to the existence of soil macropores, the amount of solute loss carried by water is deeply modified, which affects watershed hydrologic response. In this study, a new improved BP (Back Propagation) neural network method, using Levenberg–Marquand training algorithm, was used to analyze the solute loss on slopes taking into account the soil macropores. The rainfall intensity, duration, the slope, the characteristic scale of macropores and the adsorption coefficient of ions, are used as the variables of network input layer. The network middle layer is used as hidden layer, the number… More >

  • ARTICLE

    An Efficient Scheme for Interference Mitigation in 6G-IoT Wireless Networks

    Fahd N. Al-Wesabi1,*, Imran Khan2, Nadhem Nemri3, Mohammed A. Al-Hagery4, Huda G. Iskander5, Quang Ngoc Nguyen6, Babar Shah7, Ki-Il Kim8
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3889-3902, 2021, DOI:10.32604/cmc.2021.016218
    Abstract The Internet of Things (IoT) is the fourth technological revolution in the global information industry after computers, the Internet, and mobile communication networks. It combines radio-frequency identification devices, infrared sensors, global positioning systems, and various other technologies. Information sensing equipment is connected via the Internet, thus forming a vast network. When these physical devices are connected to the Internet, the user terminal can be extended and expanded to exchange information, communicate with anything, and carry out identification, positioning, tracking, monitoring, and triggering of corresponding events on each device in the network. In real life, the IoT has a wide range… More >

  • ARTICLE

    Image Splicing Detection Based on Texture Features with Fractal Entropy

    Razi J. Al-Azawi1, Nadia M. G. Al-Saidi2, Hamid A. Jalab3,*, Rabha W. Ibrahim4, Dumitru Baleanu5,6,7
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3903-3915, 2021, DOI:10.32604/cmc.2021.020368
    (This article belongs to this Special Issue: Recent Advances in Fractional Calculus Applied to Complex Engineering Phenomena)
    Abstract Over the past years, image manipulation tools have become widely accessible and easier to use, which made the issue of image tampering far more severe. As a direct result to the development of sophisticated image-editing applications, it has become near impossible to recognize tampered images with naked eyes. Thus, to overcome this issue, computer techniques and algorithms have been developed to help with the identification of tampered images. Research on detection of tampered images still carries great challenges. In the present study, we particularly focus on image splicing forgery, a type of manipulation where a region of an image is… More >

  • ARTICLE

    RSS-Based Selective Clustering Technique Using Master Node for WSN

    Vikram Rajpoot1, Vivek Tiwari2, Akash Saxena3, Prashant Chaturvedi4, Dharmendra Singh Rajput5, Mohammed Alkahtani6,7, Mustufa Haider Abidi7,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3917-3930, 2021, DOI:10.32604/cmc.2021.015826
    (This article belongs to this Special Issue: Green IoT Networks using Machine Learning, Deep Learning Models)
    Abstract Wireless sensor networks (WSN) are designed to monitor the physical properties of the target area. The received signal strength (RSS) plays a significant role in reducing sensor node power consumption during data transmission. Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span. This paper introduces the RSS-based energy-efficient selective clustering technique using a master node (RESCM) to improve energy utilization using a master node. The master node positioned at the center of the network area and base station (BS) is placed outside the network area. During… More >

  • ARTICLE

    Improving Stock Price Forecasting Using a Large Volume of News Headline Text

    Daxing Zhang1,*, Erguan Cai2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3931-3943, 2021, DOI:10.32604/cmc.2021.012302
    Abstract Previous research in the area of using deep learning algorithms to forecast stock prices was focused on news headlines, company reports, and a mix of daily stock fundamentals, but few studies achieved excellent results. This study uses a convolutional neural network (CNN) to predict stock prices by considering a great amount of data, consisting of financial news headlines. We call our model N-CNN to distinguish it from a CNN. The main concept is to narrow the diversity of specific stock prices as they are impacted by news headlines, then horizontally expand the news headline data to a higher level for… More >

  • ARTICLE

    DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

    Ziadoon K. Maseer1, Robiah Yusof1, Salama A. Mostafa2,*, Nazrulazhar Bahaman1, Omar Musa3, Bander Ali Saleh Al-rimy4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3945-3966, 2021, DOI:10.32604/cmc.2021.016074
    (This article belongs to this Special Issue: AI, IoT, Blockchain Assisted Intelligent Solutions to Medical and Healthcare Systems)
    Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design… More >

  • ARTICLE

    Controlled Quantum Network Coding Without Loss of Information

    Xing-Bo Pan1, Xiu-Bo Chen1,*, Gang Xu2, Haseeb Ahmad3, Tao Shang4, Zong-Peng Li5,6, Yi-Xian Yang1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3967-3979, 2021, DOI:10.32604/cmc.2021.017087
    Abstract Quantum network coding is used to solve the congestion problem in quantum communication, which will promote the transmission efficiency of quantum information and the total throughput of quantum network. We propose a novel controlled quantum network coding without information loss. The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie. Firstly, two pairs of three-particle non-maximum entangled states are pre-shared between senders and controller. By adding auxiliary particles and local operations, the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the basis.… More >

  • ARTICLE

    Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

    Saurabh Pal1, Pijush Kanti Dutta Pramanik1, Musleh Alsulami2, Anand Nayyar3,*, Mohammad Zarour4, Prasenjit Choudhury1
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3981-4001, 2021, DOI:10.32604/cmc.2021.017966
    Abstract With the popularity of e-learning, personalization and ubiquity have become important aspects of online learning. To make learning more personalized and ubiquitous, we propose a learner model for a query-based personalized learning recommendation system. Several contextual attributes characterize a learner, but considering all of them is costly for a ubiquitous learning system. In this paper, a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling. A total of 208 students are surveyed. DEMATEL (Decision Making Trial and Evaluation Laboratory) technique is used to establish the validity and importance of the identified contexts and find… More >

  • ARTICLE

    Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

    Wan Hyun Cho1, Sang Kyoon Kim2, Myung Hwan Na1, In Seop Na3,*
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758
    Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination of deep neural networks (DNNs)… More >

  • ARTICLE

    A Compact Size 5G Hairpin Bandpass Filter with Multilayer Coupled Line

    Qazwan Abdullah1,2,*, Ömer Aydoĝdu2, Adeeb Salh3, Nabil Farah4, Md Hairul Nizam Talib4, Taha Sadeq5, Mohammed A. A. Al-Mekhalfi3, Abdu Saif6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4025-4042, 2021, DOI:10.32604/cmc.2021.018798
    Abstract The multilayer structure is a promising technique used to minimize the size of planar microstrip filters. In the flexible design and incorporation of other microwave components, multilayer band-pass filter results in better and enhanced dimensions. This paper introduces a microstrip fifth-generation (5G) low-frequency band of 2.52–2.65 GHz using a parallel-coupled line (PCL) Bandpass filter and multilayer (ML) hairpin Bandpass filter. The targeted four-pole resonator has a center frequency of 2.585 GHz with a bandwidth of 130 MHz. The filters are designed with a 0.1 dB passband ripple with a Chebyshev response. The hairpin-line offers compact filter design structures. Theoretically, they… More >

  • ARTICLE

    Application of Grey Model and Neural Network in Financial Revenue Forecast

    Yifu Sheng1, Jianjun Zhang1,*, Wenwu Tan1, Jiang Wu1, Haijun Lin1, Guang Sun2, Peng Guo3
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4043-4059, 2021, DOI:10.32604/cmc.2021.019900
    Abstract There are many influencing factors of fiscal revenue, and traditional forecasting methods cannot handle the feature dimensions well, which leads to serious over-fitting of the forecast results and unable to make a good estimate of the true future trend. The grey neural network model fused with Lasso regression is a comprehensive prediction model that combines the grey prediction model and the BP neural network model after dimensionality reduction using Lasso. It can reduce the dimensionality of the original data, make separate predictions for each explanatory variable, and then use neural networks to make multivariate predictions, thereby making up for the… More >

  • ARTICLE

    Multi-Layered Deep Learning Features Fusion for Human Action Recognition

    Sadia Kiran1, Muhammad Attique Khan1, Muhammad Younus Javed1, Majed Alhaisoni2, Usman Tariq3, Yunyoung Nam4,*, Robertas Damaševičius5, Muhammad Sharif6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4061-4075, 2021, DOI:10.32604/cmc.2021.017800
    (This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)
    Abstract Human Action Recognition (HAR) is an active research topic in machine learning for the last few decades. Visual surveillance, robotics, and pedestrian detection are the main applications for action recognition. Computer vision researchers have introduced many HAR techniques, but they still face challenges such as redundant features and the cost of computing. In this article, we proposed a new method for the use of deep learning for HAR. In the proposed method, video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning. The Resnet-50 Pre-Trained Model is… More >

  • ARTICLE

    Emergency Decision-Making Based on q-Rung Orthopair Fuzzy Rough Aggregation Information

    Ahmed B. Khoshaim1, Saleem Abdullah2, Shahzaib Ashraf3,*, Muhammad Naeem4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4077-4094, 2021, DOI:10.32604/cmc.2021.016973
    (This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
    Abstract With the frequent occurrences of emergency events, emergency decision making (EDM) plays an increasingly significant role in coping with such situations and has become an important and challenging research area in recent times. It is essential for decision makers to make reliable and reasonable emergency decisions within a short span of time, since inappropriate decisions may result in enormous economic losses and social disorder. To handle emergency effectively and quickly, this paper proposes a new EDM method based on the novel concept of q-rung orthopair fuzzy rough (q-ROPR) set. A novel list of q-ROFR aggregation information, detailed description of the… More >

  • ARTICLE

    Intelligent IoT-Aided Early Sound Detection of Red Palm Weevils

    Mohamed Esmail Karar1,2, Omar Reyad1,3,*, Abdel-Haleem Abdel-Aty4, Saud Owyed5, Mohd F. Hassan6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4095-4111, 2021, DOI:10.32604/cmc.2021.019059
    (This article belongs to this Special Issue: Artificial Intelligence based Smart precision agriculture with analytic pattern in sustainable environments using IoT)
    Abstract Smart precision agriculture utilizes modern information and wireless communication technologies to achieve challenging agricultural processes. Therefore, Internet of Things (IoT) technology can be applied to monitor and detect harmful insect pests such as red palm weevils (RPWs) in the farms of date palm trees. In this paper, we propose a new IoT-based framework for early sound detection of RPWs using fine-tuned transfer learning classifier, namely InceptionResNet-V2. The sound sensors, namely TreeVibes devices are carefully mounted on each palm trunk to setup wireless sensor networks in the farm. Palm trees are labeled based on the sensor node number to identify the… More >

  • ARTICLE

    Hybrid Neural Network for Automatic Recovery of Elliptical Chinese Quantity Noun Phrases

    Hanyu Shi1, Weiguang Qu1,2,*, Tingxin Wei2,3, Junsheng Zhou1, Yunfei Long4, Yanhui Gu1, Bin Li2
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4113-4127, 2021, DOI:10.32604/cmc.2021.019518
    Abstract In Mandarin Chinese, when the noun head appears in the context, a quantity noun phrase can be reduced to a quantity phrase with the noun head omitted. This phrase structure is called elliptical quantity noun phrase. The automatic recovery of elliptical quantity noun phrase is crucial in syntactic parsing, semantic representation and other downstream tasks. In this paper, we propose a hybrid neural network model to identify the semantic category for elliptical quantity noun phrases and realize the recovery of omitted semantics by supplementing concept categories. Firstly, we use BERT to generate character-level vectors. Secondly, Bi-LSTM is applied to capture… More >

  • ARTICLE

    Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

    Diaa Salam Abd Elminaam1,2,*, Nabil Neggaz3, Ibrahim Abdulatief Ahmed4,5, Ahmed El Sawy Abouelyazed4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4129-4149, 2021, DOI:10.32604/cmc.2021.019047
    Abstract At present, the immense development of social networks allows generating a significant amount of textual data, which has facilitated researchers to explore the field of opinion mining. In addition, the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem. This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence (SI)-based algorithm, Harris hawks algorithm, to select the most relevant terms. The experimental study has been tested on two datasets: Arabic Jordanian General Tweets and Opinion Corpus for Arabic. In terms of accuracy and number… More >

  • ARTICLE

    Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

    Vinay Arora1, Karun Verma1, Rohan Singh Leekha2, Kyungroul Lee3, Chang Choi4,*, Takshi Gupta5, Kashish Bhatia6
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4151-4168, 2021, DOI:10.32604/cmc.2021.019178
    (This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)
    Abstract The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension, irregular cardiac functioning, and heart failure. Machine-based learning of heart sound is an {efficient} technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac sounds. Phonocardiogram (PCG) and electrocardiogram (ECG) waveforms provide the much-needed information for the diagnosis of these diseases. In this work, the researchers have converted the heart sound signal into its corresponding repeating pattern-based spectrogram. PhysioNet 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform experimentation. The existing models, viz. MobileNet, Xception, Visual… More >

  • ARTICLE

    An Improved Machine Learning Technique with Effective Heart Disease Prediction System

    Mohammad Tabrez Quasim1, Saad Alhuwaimel2,*, Asadullah Shaikh3, Yousef Asiri3, Khairan Rajab3, Rihem Farkh4,5, Khaled Al Jaloud4
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4169-4181, 2021, DOI:10.32604/cmc.2021.015984
    (This article belongs to this Special Issue: AI 2.0-Enabled Next Generation Intelligence of Things for Smart Enterprise Systems)
    Abstract Heart disease is the leading cause of death worldwide. Predicting heart disease is challenging because it requires substantial experience and knowledge. Several research studies have found that the diagnostic accuracy of heart disease is low. The coronary heart disorder determines the state that influences the heart valves, causing heart disease. Two indications of coronary heart disorder are strep throat with a red persistent skin rash, and a sore throat covered by tonsils or strep throat. This work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial stiffness. At first, we achieved the component perception measured… More >

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