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  • Open Access

    ARTICLE

    Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

    Ahmed M. Nassef1,2,*, Hegazy Rezk1,3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 171-180, 2022, DOI:10.32604/cmc.2022.019347

    Abstract This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems. This group of systems had… More >

  • Open Access

    ARTICLE

    Soft -Rough Set and Its Applications in Decision Making of Coronavirus

    M. A. El Safty1,*, Samirah Al Zahrani1, M. K. El-Bably2, M. El Sayed3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 267-285, 2022, DOI:10.32604/cmc.2022.019345

    Abstract In this paper, we present a proposed method for generating a soft rough approximation as a modification and generalization of Zhaowen et al. approach. Comparisons were obtained between our approach and the previous study and also. Eventually, an application on Coronavirus (COVID-19) has been presented, illustrated using our proposed concept, and some influencing results for symptoms of Coronavirus patients have been deduced. Moreover, following these concepts, we construct an algorithm and apply it to a decision-making problem to demonstrate the applicability of our proposed approach. Finally, a proposed approach that competes with others has been obtained, as well as realistic… More >

  • Open Access

    ARTICLE

    Load Balancing Framework for Cross-Region Tasks in Cloud Computing

    Jaleel Nazir1,2, Muhammad Waseem Iqbal1, Tahir Alyas2, Muhammad Hamid3, Muhammad Saleem4, Saadia Malik5, Nadia Tabassum6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1479-1490, 2022, DOI:10.32604/cmc.2022.019344

    Abstract Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and,… More >

  • Open Access

    ARTICLE

    High Throughput Scheduling Algorithms for Input Queued Packet Switches

    R. Chithra Devi1,*, D. Jemi Florinabel2, Narayanan Prasanth3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1527-1540, 2022, DOI:10.32604/cmc.2022.019343

    Abstract The high-performance computing paradigm needs high-speed switching fabrics to meet the heavy traffic generated by their applications. These switching fabrics are efficiently driven by the deployed scheduling algorithms. In this paper, we proposed two scheduling algorithms for input queued switches whose operations are based on ranking procedures. At first, we proposed a Simple 2-Bit (S2B) scheme which uses binary ranking procedure and queue size for scheduling the packets. Here, the Virtual Output Queue (VOQ) set with maximum number of empty queues receives higher rank than other VOQ’s. Through simulation, we showed S2B has better throughput performance than Highest Ranking First… More >

  • Open Access

    ARTICLE

    Hybrid Teaching Learning Approach for Improving Network Lifetime in Wireless Sensor Networks

    P. Baskaran1,*, K. Karuppasamy2

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1975-1992, 2022, DOI:10.32604/cmc.2022.019342

    Abstract In a wireless sensor network (WSN), data gathering is more effectually done with the clustering process. Clustering is a critical strategy for improving energy efficiency and extending the longevity of a network. Hierarchical modeling-based clustering is proposed to enhance energy efficiency where nodes that hold higher residual energy may be clustered to collect data and broadcast it to the base station. Moreover, existing approaches may not consider data redundancy while collecting data from adjacent nodes or overlapping nodes. Here, an improved clustering approach is anticipated to attain energy efficiency by implementing MapReduction for regulating mapping and reducing complexity in routing… More >

  • Open Access

    ARTICLE

    Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

    Iram Mushtaq1, Muhammad Umer1, Muhammad Imran2, Inzamam Mashood Nasir3, Ghulam Muhammad4,*, Mohammad Shorfuzzaman5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 59-72, 2022, DOI:10.32604/cmc.2022.019337

    Abstract During COVID-19, the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand. This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals, pharmacies, and retail stores as its customers. Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers. A questionnaire has been… More >

  • Open Access

    ARTICLE

    Deep Optimal VGG16 Based COVID-19 Diagnosis Model

    M. Buvana1, K. Muthumayil2, S. Senthil kumar3, Jamel Nebhen4, Sultan S. Alshamrani5, Ihsan Ali6,*

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 43-58, 2022, DOI:10.32604/cmc.2022.019331

    Abstract Coronavirus (COVID-19) outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from… More >

  • Open Access

    ARTICLE

    A Position-Aware Transformer for Image Captioning

    Zelin Deng1,*, Bo Zhou1, Pei He2, Jianfeng Huang3, Osama Alfarraj4, Amr Tolba4,5

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 2065-2081, 2022, DOI:10.32604/cmc.2022.019328

    Abstract Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features.… More >

  • Open Access

    ARTICLE

    Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

    R. Sujitha*, B. Paramasivan

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1557-1572, 2022, DOI:10.32604/cmc.2022.019323

    Abstract With the modernization of machine learning techniques in healthcare, different innovations including support vector machine (SVM) have predominantly played a major role in classifying lung cancer, predicting coronavirus disease 2019, and other diseases. In particular, our algorithm focuses on integrated datasets as compared with other existing works. In this study, parallel-based SVM (P-SVM) and multiclass-based multiple submodels (MMSM-SVM) were used to analyze the optimal classification of lung diseases. This analysis aimed to find the optimal classification of lung diseases with id and stages, such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses, respectively. For… More >

  • Open Access

    ARTICLE

    Joint Channel and Multi-User Detection Empowered with Machine Learning

    Mohammad Sh. Daoud1, Areej Fatima2, Waseem Ahmad Khan3, Muhammad Adnan Khan4,5,*, Sagheer Abbas3, Baha Ihnaini6, Munir Ahmad3, Muhammad Sheraz Javeid7, Shabib Aftab3

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 109-121, 2022, DOI:10.32604/cmc.2022.019295

    Abstract The numbers of multimedia applications and their users increase with each passing day. Different multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network systems. In this article, a fuzzy logic empowered adaptive backpropagation neural network (FLeABPNN) algorithm is proposed for joint channel and multi-user detection (CMD). FLeABPNN has two stages. The first stage estimates the channel parameters, and the second performs multi-user detection. The proposed approach capitalizes on a neuro-fuzzy hybrid system that combines the competencies of both fuzzy logic and neural networks. This study analyzes the… More >

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