Home / Journals / CMC / Vol.73, No.3, 2022
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  • Open AccessOpen Access

    ARTICLE

    An Approximation for the Entropy Measuring in the General Structure of SGSP3

    Kamel Jebreen1,2,3,*, Muhammad Haroon Aftab4, Mohammad Issa Sowaity5, Zeeshan Saleem Mufti4, Muhammad Hussain6
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4455-4463, 2022, DOI:10.32604/cmc.2022.030246
    Abstract In this article, we calculate various topological invariants such as symmetric division degree index, redefined Zagreb index, VL index, first and second exponential Zagreb index, first and second multiplicative exponential Zagreb indices, symmetric division degree entropy, redefined Zagreb entropy, VL entropy, first and second exponential Zagreb entropies, multiplicative exponential Zagreb entropy. We take the chemical compound named Proanthocyanidins, which is a very useful polyphenol in human’s diet. They are very beneficial for one’s health. These chemical compounds are extracted from grape seeds. They are tremendously anti-inflammatory. A subdivision form of this compound is presented in this article. The compound named… More >

  • Open AccessOpen Access

    ARTICLE

    Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

    Zia Ullah1, Muhammad Ismail Mohmand1, Sadaqat ur Rehman2,*, Muhammad Zubair3, Maha Driss4, Wadii Boulila5, Rayan Sheikh2, Ibrahim Alwawi6
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4465-4487, 2022, DOI:10.32604/cmc.2022.029101
    Abstract Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we… More >

  • Open AccessOpen Access

    ARTICLE

    Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

    Muhammad Waqas1,3,*, Shehr Bano2, Fatima Hassan2, Shanshan Tu1, Ghulam Abbas2, Ziaul Haq Abbas4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4489-4499, 2022, DOI:10.32604/cmc.2022.029539
    Abstract Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect… More >

  • Open AccessOpen Access

    ARTICLE

    Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

    Muhammad Ali1, Jamal Hussain Shah1, Muhammad Attique Khan2, Majed Alhaisoni3, Usman Tariq4, Tallha Akram5, Ye Jin Kim6, Byoungchol Chang7,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4501-4518, 2022, DOI:10.32604/cmc.2022.030392
    Abstract Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm Optimization (PSO) algorithm, and for… More >

  • Open AccessOpen Access

    ARTICLE

    An Effective Classifier Model for Imbalanced Network Attack Data

    Gürcan Çetin*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4519-4539, 2022, DOI:10.32604/cmc.2022.031734
    Abstract Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these two concerns. The number of… More >

  • Open AccessOpen Access

    ARTICLE

    Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

    K. Shankar1, E. Laxmi Lydia2, Sachin Kumar1,*, Ali S. Abosinne3, Ahmed alkhayyat4, A. H. Abbas5, Sarmad Nozad Mahmood6
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4541-4557, 2022, DOI:10.32604/cmc.2022.031247
    Abstract Oral Squamous Cell Carcinoma (OSCC) is a type of Head and Neck Squamous Cell Carcinoma (HNSCC) and it should be diagnosed at early stages to accomplish efficient treatment, increase the survival rate, and reduce death rate. Histopathological imaging is a wide-spread standard used for OSCC detection. However, it is a cumbersome process and demands expert’s knowledge. So, there is a need exists for automated detection of OSCC using Artificial Intelligence (AI) and Computer Vision (CV) technologies. In this background, the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification (ISMA-AIOCC) model on Histopathological images… More >

  • Open AccessOpen Access

    ARTICLE

    DISTINÏCT: Data poISoning atTacks dectectIon usiNg optÏmized jaCcard disTance

    Maria Sameen1, Seong Oun Hwang2,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4559-4576, 2022, DOI:10.32604/cmc.2022.031091
    Abstract Machine Learning (ML) systems often involve a re-training process to make better predictions and classifications. This re-training process creates a loophole and poses a security threat for ML systems. Adversaries leverage this loophole and design data poisoning attacks against ML systems. Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance. Data poisoning attacks are challenging to detect, and even more difficult to respond to, particularly in the Internet of Things (IoT) environment. To address this problem, we proposed DISTINÏCT, the first proactive data poisoning attack detection framework… More >

  • Open AccessOpen Access

    ARTICLE

    An Improved Transfer-Learning for Image-Based Species Classification of Protected Indonesians Birds

    Chao-Lung Yang1, Yulius Harjoseputro2,3, Yu-Chen Hu4, Yung-Yao Chen2,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4577-4593, 2022, DOI:10.32604/cmc.2022.031305
    Abstract This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed sequence of BNDFC layers outperform… More >

  • Open AccessOpen Access

    ARTICLE

    New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

    Deepak Sukheja1, Javaid Ahmad Shah2, G. Madhu3, K. Sandeep Kautish4, Fahad A. Alghamdi5, Ibrahim. S. Yahia6,7,8, El-Sayed M. El-Kenawy9,10, Ali Wagdy Mohamed11,12,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4595-4609, 2022, DOI:10.32604/cmc.2022.029122
    Abstract Efficient decision-making remains an open challenge in the research community, and many researchers are working to improve accuracy through the use of various computational techniques. In this case, the fuzzification and defuzzification processes can be very useful. Defuzzification is an effective process to get a single number from the output of a fuzzy set. Considering defuzzification as a center point of this research paper, to analyze and understand the effect of different types of vehicles according to their performance. In this paper, the multi-criteria decision-making (MCDM) process under uncertainty and defuzzification is discussed by using the center of the area… More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

    Pramote Charongrattanasakul1, Wimonmas Bamrungsetthapong2,*, Poom Kumam3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4611-4626, 2022, DOI:10.32604/cmc.2022.030856
    Abstract The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. A comparison between the proposed… More >

  • Open AccessOpen Access

    ARTICLE

    Thermal Loss Analysis of a Flat Plate Solar Collector Using Numerical Simulation

    Timur Merembayev1,2,*, Yedilkhan Amirgaliyev1,3, Murat Kunelbayev1, Didar Yedilkhan1,4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4627-4640, 2022, DOI:10.32604/cmc.2022.027180
    Abstract In this paper, we studied theoretically and numerically heated losses of a flat solar collector to model the solar water heating system for the Kazakhstan climate condition. For different climatic zones with a growing cost for energy or lack of central heating systems, promising is to find ways to improve the energy efficiency of the solar system. The mathematical model (based on ordinary differential equation) simulated the solar system work process under different conditions. To bridge the modeling and real values results, we studied the important physical parameters such as loss coefficient, Nu, Ra, and Pr values. They impacted the… More >

  • Open AccessOpen Access

    ARTICLE

    Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

    Mubashar Hussain1, Toqir A. Rana2,3, Aksam Iftikhar4, M. Usman Ashraf5,*, Muhammad Waseem Iqbal6, Ahmed Alshaflut7, Abdullah Alourani8
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4641-4656, 2022, DOI:10.32604/cmc.2022.024759
    Abstract In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the sequential rule-based approaches have poor… More >

  • Open AccessOpen Access

    ARTICLE

    CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

    Adnan Ahmed Rafique1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Samia Allaoua Chelloug4,*, Ahmad Jalal1, Jeongmin Park5
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4657-4675, 2022, DOI:10.32604/cmc.2022.027720
    Abstract Latest advancements in vision technology offer an evident impact on multi-object recognition and scene understanding. Such scene-understanding task is a demanding part of several technologies, like augmented reality-based scene integration, robotic navigation, autonomous driving, and tourist guide. Incorporating visual information in contextually unified segments, convolution neural networks-based approaches will significantly mitigate the clutter, which is usual in classical frameworks during scene understanding. In this paper, we propose a convolutional neural network (CNN) based segmentation method for the recognition of multiple objects in an image. Initially, after acquisition and preprocessing, the image is segmented by using CNN. Then, CNN features are… More >

  • Open AccessOpen Access

    ARTICLE

    Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

    Vicky Mudeng1,2, Jin-woo Jeong3, Se-woon Choe1,4,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4677-4693, 2022, DOI:10.32604/cmc.2022.031046
    Abstract A lump growing in the breast may be referred to as a breast mass related to the tumor. However, not all tumors are cancerous or malignant. Breast masses can cause discomfort and pain, depending on the size and texture of the breast. With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant. With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images. This work accomplished breast masses classification… More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

    Mohammed Altaf Ahmed1, Sara A Althubiti2, Dronamraju Nageswara Rao3, E. Laxmi Lydia4, Woong Cho5, Gyanendra Prasad Joshi6, Sung Won Kim7,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4695-4711, 2022, DOI:10.32604/cmc.2022.032083
    Abstract Cyberattacks are developing gradually sophisticated, requiring effective intrusion detection systems (IDSs) for monitoring computer resources and creating reports on anomalous or suspicious actions. With the popularity of Internet of Things (IoT) technology, the security of IoT networks is developing a vital problem. Because of the huge number and varied kinds of IoT devices, it can be challenging task for protecting the IoT framework utilizing a typical IDS. The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity. Therefore, this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Blockchain Security Using Ripple Consensus Algorithm

    A. Baseera1, Abeer Abdullah Alsadhan2,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4713-4726, 2022, DOI:10.32604/cmc.2022.029538
    Abstract In the development of technology in various fields like big data analysis, data mining, big data, cloud computing, and blockchain technology, security become more constrained. Blockchain is used in providing security by encrypting the sharing of information. Blockchain is applied in the peer-to-peer (P2P) network and it has a decentralized ledger. Providing security against unauthorized breaches in the distributed network is required. To detect unauthorized breaches, there are numerous techniques were developed and those techniques are inefficient and have poor data integrity. Hence, a novel technique needs to be implemented to tackle the new breaches in the distributed network. This… More >

  • Open AccessOpen Access

    ARTICLE

    A Fast Tongue Detection and Location Algorithm in Natural Environment

    Lei Zhu1, Guojiang Xin1,2,*, Xin Wang1, Changsong Ding1,2, Hao Liang1,2, Qilei Chen3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4727-4742, 2022, DOI:10.32604/cmc.2022.028187
    Abstract The collection and extraction of tongue images has always been an important part of intelligent tongue diagnosis. At present, the collection of tongue images generally needs to be completed in a sealed, stable light environment, which is not conducive to the promotion of extensive tongue image and intelligent tongue diagnosis. In response to the problem, a new algorithm named GCYTD (GELU-CA-YOLO Tongue Detection) is proposed to quickly detect and locate the tongue in a natural environment, which can greatly reduce the restriction of the tongue image collection environment. The algorithm is based on the YOLO (You Only Look Once) V4-tiny… More >

  • Open AccessOpen Access

    ARTICLE

    High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble

    Yong-Woon Kim1, Yung-Cheol Byun2,*, Dong Seog Han3, Dalia Dominic1, Sibu Cyriac1
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4743-4762, 2022, DOI:10.32604/cmc.2022.028632
    Abstract A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes… More >

  • Open AccessOpen Access

    ARTICLE

    Body Worn Sensors for Health Gaming and e-Learning in Virtual Reality

    Mir Mushhood Afsar1, Shizza Saqib1, Yazeed Yasin Ghadi2, Suliman A. Alsuhibany3, Ahmad Jalal1, Jeongmin Park4,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4763-4777, 2022, DOI:10.32604/cmc.2022.028618
    Abstract Virtual reality is an emerging field in the whole world. The problem faced by people today is that they are more indulged in indoor technology rather than outdoor activities. Hence, the proposed system introduces a fitness solution connecting virtual reality with a gaming interface so that an individual can play first-person games. The system proposed in this paper is an efficient and cost-effective solution that can entertain people along with playing outdoor games such as badminton and cricket while sitting in the room. To track the human movement, sensors Micro Processor Unit (MPU6050) are used that are connected with Bluetooth… More >

  • Open AccessOpen Access

    ARTICLE

    Automotive Service Quality Investigation Using a Grey-DEMATEL Model

    Phi-Hung Nguyen*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4779-4800, 2022, DOI:10.32604/cmc.2022.030745
    Abstract In today’s fast-challenging business environment, automobile manufacturers are required to supply customers with high-quality vehicles at competitive prices. However, existing research on factors influencing service quality lacks a detailed and systematic understanding, and there is no consensus study on causal relationship and measuring the weights of service quality factors in the automotive manufacturing industry. This study provides an integrated technique for evaluating the automotive service quality in the context of VinFast-the Vietnamese leading brand. First, the Grey Theory System (GTS) is utilized to estimate the subjective views of the decision maker (DM) and overcome incomplete and vague decision information. Then,… More >

  • Open AccessOpen Access

    ARTICLE

    MCBC-SMOTE: A Majority Clustering Model for Classification of Imbalanced Data

    Jyoti Arora1, Meena Tushir2, Keshav Sharma1, Lalit Mohan1, Aman Singh3,*, Abdullah Alharbi4, Wael Alosaimi4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4801-4817, 2022, DOI:10.32604/cmc.2022.025960
    Abstract Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known… More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Band Bandpass Filter Using Novel Topology for Next-Generation IoT Wireless Systems

    Muhammad Faisal*, Sohail Khalid
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4819-4832, 2022, DOI:10.32604/cmc.2022.029049
    Abstract The design of single- and quad-band Bandpass Filter (BPF) topology has been presented in this paper for next-generation Internet of Things (IoT) devices. The main topology is constructed using the Split Ring Resonator (SRR), separated by the Anti-Parallel Coupled Line Structure (APCLS). A detailed analysis of APCLS has been presented, which is further used to construct the single- and quad-band BPF. The single-band BPF design consists of SRR loaded with APCLS. The developed single-band BPF displays a dual-mode response with a center frequency of 2.65 GHz and a measured fractional bandwidth of 17.17%. Moreover, a quad-band bandpass filter has been… More >

  • Open AccessOpen Access

    ARTICLE

    Swarming Computational Efficiency to Solve a Novel Third-Order Delay Differential Emden-Fowler System

    Wajaree Weera1, Zulqurnain Sabir2, Muhammad Asif Zahoor Raja3, Sakda Noinang4, Thongchai Botmart1,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4833-4849, 2022, DOI:10.32604/cmc.2022.030888
    Abstract The purpose of this research is to construct an integrated neuro swarming scheme using the procedures of the artificial neural networks (ANNs) with the use of global search particle swarm optimization (PSO) along with the competent local search interior-point programming (IPP) called as ANN-PSOIPP. The proposed computational scheme is implemented for the numerical simulations of the third order nonlinear delay differential Emden-Fowler model (TON-DD-EFM). The TON-DD-EFM is based on two types along with the particulars of shape factor, delayed terms, and singular points. A merit function is performed using the optimization of PSOIPP to find the solutions to the TON-DD-EFM.… More >

  • Open AccessOpen Access

    ARTICLE

    Swarming Computational Techniques for the Influenza Disease System

    Sakda Noinang1, Zulqurnain Sabir2, Gilder Cieza Altamirano3, Muhammad Asif Zahoor Raja4, Manuel Jesús Sànchez-Chero5, María-Verónica Seminario-Morales5, Wajaree Weera6,*, Thongchai Botmart6
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4851-4868, 2022, DOI:10.32604/cmc.2022.029437
    Abstract The current study relates to designing a swarming computational paradigm to solve the influenza disease system (IDS). The nonlinear system’s mathematical form depends upon four classes: susceptible individuals, infected people, recovered individuals and cross-immune people. The solutions of the IDS are provided by using the artificial neural networks (ANNs) together with the swarming computational paradigm-based particle swarm optimization (PSO) and interior-point scheme (IPA) that are the global and local search approaches. The ANNs-PSO-IPA has never been applied to solve the IDS. Instead a merit function in the sense of mean square error is constructed using the differential form of each… More >

  • Open AccessOpen Access

    ARTICLE

    Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network

    Tahir Alyas1,*, Khalid Alissa2, Abdul Salam Mohammad3, Shazia Asif4, Tauqeer Faiz5, Gulzar Ahmed6
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4869-4883, 2022, DOI:10.32604/cmc.2022.031376
    Abstract Fungal disease affects more than a billion people worldwide, resulting in different types of fungus diseases facing life-threatening infections. The outer layer of your body is called the integumentary system. Your skin, hair, nails, and glands are all part of it. These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun. The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect. Heat, light, damage, and illness are all protected by it. Fungi-caused infections are found in almost every… More >

  • Open AccessOpen Access

    ARTICLE

    Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

    Muhammad Irfan1, Farman Ali2, Fazal Muhammad3,*, Saifur Rahman1, Ammar Armghan4, Yousaf Khan5, Faisal Althobiani6, Rehan Shafiq7, Mohammed Alshareef8, Mohammad E. Gommosani9
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4885-4895, 2022, DOI:10.32604/cmc.2022.030157
    Abstract The fiber nonlinearity and phase noise (PN) are the focused impairments in the optical communication system, induced by high-capacity transmission and high laser input power. The channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN impairments. Thus, addressing of these distortions is the basic objective for the 5G mobile network. In this paper, the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber (mmWave-RoF). The analytical model is designed in terms of outage probability for the proposed mmWave-RoF system. The performance of… More >

  • Open AccessOpen Access

    ARTICLE

    Research on Tibetan Speech Recognition Based on the Am-do Dialect

    Kuntharrgyal Khysru1,*, Jianguo Wei1,2, Jianwu Dang3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4897-4907, 2022, DOI:10.32604/cmc.2022.027591
    Abstract In China, Tibetan is usually divided into three major dialects: the Am-do, Khams and Lhasa dialects. The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan. Although this dialect has its own specific historical and social conditions and development, there have been different degrees of communication with other ethnic groups, but all the abovementioned dialects developed from the same language: Tibetan. This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language, which optimizes the problems existing in previous research. Audio data of the Am-do dialect are expanded… More >

  • Open AccessOpen Access

    ARTICLE

    A Constant Gain and Miniaturized Antipodal Vivaldi Antenna for 5G Communication Applications

    Amruta S. Dixit1, Sumit Kumar1,*, Shabana Urooj2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4909-4921, 2022, DOI:10.32604/cmc.2022.027862
    Abstract This paper proposes a stable gain and a compact Antipodal Vivaldi Antenna (AVA) for a 38 GHz band of 5G communication. A novel compact AVA is designed to provide constant gain, high front to back ratio (FBR), and very high efficiency. The performance of the proposed AVA is enhanced with the help of a dielectric lens (DL) and corrugations. A rectangular-shaped DL is incorporated in conventional AVA (CAVA) to enhance its gain up to 1 dBi and the bandwidth by 1.8 GHz. Next, the rectangular corrugations are implemented in CAVA with lens (CAVA-L) to further improve the gain and bandwidth. The proposed… More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Simulations of One-Directional Fractional Pharmacokinetics Model

    Nursyazwani Mohamad Noor1, Siti Ainor Mohd Yatim1,*, Nur Intan Raihana Ruhaiyem2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4923-4934, 2022, DOI:10.32604/cmc.2022.030414
    Abstract In this paper, we present a three-compartment of pharmacokinetics model with irreversible rate constants. The compartment consists of arterial blood, tissues and venous blood. Fick’s principle and the law of mass action were used to develop the model based on the diffusion process. The model is modified into a fractional pharmacokinetics model with the sense of Caputo derivative. The existence and uniqueness of the model are investigated and the positivity of the model is established. The behaviour of the model is investigated by implementing numerical algorithms for the numerical solution of the system of fractional differential equations. MATLAB software is… More >

  • Open AccessOpen Access

    ARTICLE

    Sensors-Based Ambient Assistant Living via E-Monitoring Technology

    Sadaf Hafeez1, Yazeed Yasin Ghadi2, Mohammed Alarfaj3, Tamara al Shloul4, Ahmad Jalal1, Shaharyar Kamal1, Dong-Seong Kim5,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4935-4952, 2022, DOI:10.32604/cmc.2022.023841
    Abstract Independent human living systems require smart, intelligent, and sustainable online monitoring so that an individual can be assisted timely. Apart from ambient assisted living, the task of monitoring human activities plays an important role in different fields including virtual reality, surveillance security, and human interaction with robots. Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions. In this paper, we propose multiple methods such as random occupancy pattern, spatio temporal cloud, way-point trajectory, Hilbert transform, Walsh Hadamard transform and bone pair descriptors to extract optimal… More >

  • Open AccessOpen Access

    ARTICLE

    Sika Deer Behavior Recognition Based on Machine Vision

    He Gong1,3,4, Mingwang Deng1, Shijun Li1,2,6,*, Tianli Hu1,3,4, Yu Sun1,3,4, Ye Mu1,3,4, Zilian Wang1, Chang Zhang1, Thobela Louis Tyasi5
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4953-4969, 2022, DOI:10.32604/cmc.2022.027457
    Abstract With the increasing intensive and large-scale development of the sika deer breeding industry, it is crucial to assess the health status of the sika deer by monitoring their behaviours. A machine vision–based method for the behaviour recognition of sika deer is proposed in this paper. Google Inception Net (GoogLeNet) is used to optimise the model in this paper. First, the number of layers and size of the model were reduced. Then, the 5 × 5 convolution was changed to two 3 × 3 convolutions, which reduced the parameters and increased the nonlinearity of the model. A 5 × 5 convolution… More >

  • Open AccessOpen Access

    ARTICLE

    Flexible Strain Sensor Based on 3D Electrospun Carbonized Sponge

    He Gong1,2,3, Zilian Wang1,3, Zhiqiang Cheng4, Lin Chen1,3, Haohong Pan1,3, Daming Zhang2, Tianli Hu1,3,*, Thobela Louis Tyasi5
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4971-4980, 2022, DOI:10.32604/cmc.2022.029433
    Abstract Flexible strain sensor has attracted much attention because of its potential application in human motion detection. In this work, the prepared strain sensor was obtained by encapsulating electrospun carbonized sponge (CS) with room temperature vulcanized silicone rubber (RTVS). In this paper, the formation mechanism of conductive sponge was studied. Based on the combination of carbonized sponge and RTVS, the strain sensing mechanism and piezoresistive properties are discussed. After research and testing, the CS/RTVS flexible strain sensor has excellent fast response speed and stability, and the maximum strain coefficient of the sensor is 136.27. In this study, the self-developed CS/RTVS sensor… More >

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    ARTICLE

    Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

    Haykel Marouani1,*, Fahad Awjah Almehmadi1, Rihem Farkh2, Habib Dhahri3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4981-4994, 2022, DOI:10.32604/cmc.2022.029315
    Abstract

    In this work, the concepts of particle swarm optimization-based method, named non-Gaussian improved particle swarm optimization for minimizing the cost of energy (COE) of wind turbines (WTs) on high-altitude sites are introduced. Since the COE depends on site specification constants and initialized parameters of wind turbine, the focus was on the design optimization of rotor radius, hub height and rated power. Based on literature, the COE is converted to the Saudi Arabia context. Thus, the constrained wind turbine optimization problem is developed. Then, non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving… More >

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    ARTICLE

    An Image Edge Detection Algorithm Based on Multi-Feature Fusion

    Zhenzhou Wang1, Kangyang Li1, Xiang Wang1,*, Antonio Lee2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4995-5009, 2022, DOI:10.32604/cmc.2022.029650
    Abstract Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On… More >

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    ARTICLE

    Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

    Mesfer Al Duhayyim1,*, Heba G. Mohamed2, Saud S. Alotaibi3, Hany Mahgoub4,5, Abdullah Mohamed6, Abdelwahed Motwakel7, Abu Sarwar Zamani7, Mohamed I. Eldesouki8
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5011-5024, 2022, DOI:10.32604/cmc.2022.031096
    Abstract Cyberbullying (CB) is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB. The recently developed deep learning (DL) models pave the way to design CB classifier models with maximum performance. At the same time, optimal hyperparameter tuning process plays a vital role to enhance overall results. This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification (TLGODL-CBC) model in Social Media. The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context. Initially, the input data is cleaned and pre-processed to make… More >

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    ARTICLE

    Novel Approach to Energy Management via Performance Shaping Factors in Power Plants

    Ahmed Ali Ajmi1,2, Noor Shakir Mahmood1,2, Khairur Rijal Jamaludin1,*, Hayati Habibah Abdul Talib1, Shamsul Sarip1, Hazilah Mad Kaidi1
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5025-5039, 2022, DOI:10.32604/cmc.2022.031239
    Abstract The literature that a lack of integration between the performance shaping factors (PSFs) and the energy management performance (EMP) is one of the critical problems that prevent performance improvement and reduces the power plant’s efficiency. To solve this problem, this article aims to achieve two main objectives: (1) Systematically investigate and identify the critical success factors (CSFs) for integration with PSFs and EMP; (2) Develop a novel modelling approach to predict the performance of power plants based on innovative integrated strategies. The research methodology is grounded on the theoretical and practical approach to improving performance. The Newcastle Ottawa Scale (NOS)… More >

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    ARTICLE

    Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

    Mehrdad Ahmadi Kamarposhti1,*, Hassan Shokouhandeh2, Ilhami Colak3, Kei Eguchi4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5041-5061, 2022, DOI:10.32604/cmc.2022.031583
    Abstract The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector. The capability of online fuzzy tracking systems is maximum power, resistance to radiation and temperature changes, and no need for external sensors to measure radiation intensity and temperature. However, the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing. The controller used in the maximum power point tracking (MPPT) circuit must be… More >

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    ARTICLE

    Unconstrained Hand Dorsal Veins Image Database and Recognition System

    Mustafa M. Al Rifaee1,*, Mohammad M. Abdallah1, Mosa I. Salah2, Ayman M. Abdalla1
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5063-5073, 2022, DOI:10.32604/cmc.2022.030033
    Abstract Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric… More >

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    ARTICLE

    Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

    Abdullah A Asiri1, Akhtar Badshah2, Fazal Muhammad3,*, Hassan A Alshamrani1, Khalil Ullah4, Khalaf A Alshamrani1, Samar Alqhtani5, Muhammad Irfan6, Hanan Talal Halawani7, Khlood M Mehdar8
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5075-5089, 2022, DOI:10.32604/cmc.2022.031156
    Abstract Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the… More >

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    ARTICLE

    Aging Analysis Framework of Windows-Based Systems through Differential-Analysis of System Snapshots

    Eun-Tae Jang1, Sung Hoon Baek2, Ki-Woong Park1,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5091-5102, 2022, DOI:10.32604/cmc.2022.026663
    Abstract When a Windows-based system is used for an exceedingly long time, its performance degrades, and the error occurrence rate tends to increase. This is generally called system aging. To investigate the reasons for system aging, various studies have been conducted within the range of the operating system kernel to the user application. However, finding an accurate reason for system performance degradation remains challenging research topic. In this study, system monitoring was conducted by dividing a system into ‘before software installation,’ ‘after software installation,’ and ‘after software removal.’ We confirmed that when a software installed in a system is removed, various… More >

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    ARTICLE

    Deep Reinforcement Learning-Based Job Shop Scheduling of Smart Manufacturing

    Eman K. Elsayed1, Asmaa K. Elsayed2,*, Kamal A. Eldahshan3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5103-5120, 2022, DOI:10.32604/cmc.2022.030803
    Abstract Industry 4.0 production environments and smart manufacturing systems integrate both the physical and decision-making aspects of manufacturing operations into autonomous and decentralized systems. One of the key aspects of these systems is a production planning, specifically, Scheduling operations on the machines. To cope with this problem, this paper proposed a Deep Reinforcement Learning with an Actor-Critic algorithm (DRLAC). We model the Job-Shop Scheduling Problem (JSSP) as a Markov Decision Process (MDP), represent the state of a JSSP as simple Graph Isomorphism Networks (GIN) to extract nodes features during scheduling, and derive the policy of optimal scheduling which guides the included… More >

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    ARTICLE

    A Novel Optimized Language-Independent Text Summarization Technique

    Hanan A. Hosni Mahmoud1,*, Alaaeldin M. Hafez2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5121-5136, 2022, DOI:10.32604/cmc.2022.031485
    Abstract A substantial amount of textual data is present electronically in several languages. These texts directed the gear to information redundancy. It is essential to remove this redundancy and decrease the reading time of these data. Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects. This paper proposes a language-independent extractive summarization technique. The proposed technique presents a clustering-based optimization technique. The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance. Experiments are devised and evaluated using BillSum dataset for… More >

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    ARTICLE

    CLEC: Combination Locality Based Erasure Code for Permissioned Blockchain Storage

    Jiabin Wu1,3, Boai Yang2, Yang Liu1, Fang Liu3,*, Nong Xiao1, Shuo Li4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5137-5150, 2022, DOI:10.32604/cmc.2022.028305
    Abstract Building a new decentralized domain name system based on blockchain technology is helping to solve problems, such as load imbalance and over-dependence on the trust of the central node. However, in the existing blockchain storage system, the storage overhead is very high due to its full-replication data storage mechanism. The total storage consumption for each block is up to O(n) with n nodes. Erasure code applied to blockchains can significantly reduce the storage overhead, but also greatly lower the read performance. In this study, we propose a novel coding scheme for blockchain storage, Combination Locality based Erasure Code for Permissioned… More >

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    ARTICLE

    A Decision-Based Hybrid Proxy Mobile IPv6 Scheme for Better Resources Utilization

    Habib Ullah Khan1,*, Anwar Hussain2, Shah Nazir2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5151-5167, 2022, DOI:10.32604/cmc.2022.030837
    Abstract Seamless mobility is always one of the major requirements of modern-day communication. In a heterogeneous and massive IoT environment, efficient network-based mobility protocol such as proxy mobile IPv6 (PMIPv6), is potentially a good candidate for efficient mobility as well as resource utilization efficiency. Several extensions are devised for performance in the research domain. However, a multi-criterion decision-based resource-efficient PMIPv6 extension is required to achieve efficiency when network resources are overloaded. In this research, a multi-criterion decision-based PMIPv6 scheme is devised that provides better performance when the Local Mobility Anchor (LMA) or Mobile Access Gateway (MAG) is overloaded. The objective is… More >

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    ARTICLE

    Truncation and Rounding-Based Scalable Approximate Multiplier Design for Computer Imaging Applications

    S. Rooban1,*, A. Yamini Naga Ratnam1, M. V. S. Ramprasad2, N. Subbulakshmi3, R. Uma Mageswari4
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5169-5184, 2022, DOI:10.32604/cmc.2022.027974
    Abstract Advanced technology used for arithmetic computing application, comprises greater number of approximate multipliers and approximate adders. Truncation and Rounding-based Scalable Approximate Multiplier (TRSAM) distinguish a variety of modes based on height (h) and truncation (t) as TRSAM (h, t) in the architecture. This TRSAM operation produces higher absolute error in Least Significant Bit (LSB) data shift unit. A new scalable approximate multiplier approach that uses truncation and rounding TRSAM (3, 7) is proposed to increase the multiplier accuracy. With the help of foremost one bit architecture, the proposed scalable approximate multiplier approach reduces the partial products. The proposed approximate TRSAM… More >

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    ARTICLE

    Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

    Guanghao Jin1, Chunmei Pei1, Na Zhao1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5185-5196, 2022, DOI:10.32604/cmc.2022.027307
    Abstract When utilizing the deep learning models in some real applications, the distribution of the labels in the environment can be used to increase the accuracy. Generally, to compute this distribution, there should be the validation set that is labeled by the ground truths. On the other side, the dependency of ground truths limits the utilization of the distribution in various environments. In this paper, we carried out a novel system for the deep learning-based classification to solve this problem. Firstly, our system only uses one validation set with ground truths to compute some hyper parameters, which is named as one-shot… More >

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    ARTICLE

    A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine

    Hai Long1, Zhe Wang1, Yidi Cui2,3, Junhui Wang4, Bo Gao5, Chao Chen5, Yan Zhu5,*, Heinrich Herre1
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5197-5217, 2022, DOI:10.32604/cmc.2022.029365
    Abstract Psoriasis is a chronic, non-communicable, painful, disfiguring and disabling disease for which there is no cure, with great negative impact on patients’ quality of life (QoL). Diagnosis and treatment with traditional Chinese medical technique based on syndrome differentiation has been used in practice for a long time and proven effective, though, up to now, there are only a few available studies about the use of semantic technologies and the knowledge systems that use Traditional Chinese Medicine (TCM)-syndrome differentiation for information retrieval and automated reasoning. In this paper we use semantic techniques based on ontologies to develop a prototypical system for… More >

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    ARTICLE

    Code-based Sequential Aggregate Signature Scheme

    Bennian Dou1,*, Lei Xu1, Xiaoling Yu2, Lin Mei1, Cong Zuo3
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5219-5231, 2022, DOI:10.32604/cmc.2022.030270
    Abstract This paper proposes the first code-based quantum immune sequential aggregate signature (SAS) scheme and proves the security of the proposed scheme in the random oracle model. Aggregate signature (AS) schemes and sequential aggregate signature schemes allow a group of potential signers to sign different messages respectively, and all the signatures of those users on those messages can be aggregated into a single signature such that the size of the aggregate signature is much smaller than the total size of all individual signatures. Because of the aggregation of many signatures into a single short signature, AS and SAS schemes can reduce… More >

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    ARTICLE

    Throughput Enhancement for NOMA Systems Using Intelligent Reflecting Surfaces

    Raed Alhamad1,*, Hatem Boujemaa2
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5233-5244, 2022, DOI:10.32604/cmc.2022.030793
    Abstract In this article, we optimize the powers associated to Non Orthogonal Multiple Access (NOMA) users, sensing and harvesting duration for Cognitive Radio Networks (CRN). The secondary source harvests energy from node A signal. Then, it senses the channel to detect primary source. Then, the secondary source transmits a signal that is reflected by Intelligent Reflecting Surfaces (IRS) so that all reflections have a zero phase at any user. A set Ii of reflectors are associated to user Ui. The use of M = Mi = 512, 256, 128, 64, 32, 16, 8 reflectors per user offers 45, 42, 39, 36, 33, 30, 27 dB gain… More >

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    ARTICLE

    Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation Problem

    Nashwa Nageh1, Ahmed Elshamy1, Abdel Wahab Said Hassan1, Mostafa Sami2, Mustafa Abdul Salam3,4,*
    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5245-5268, 2022, DOI:10.32604/cmc.2022.030906
    Abstract Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The… More >

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