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  • Open Access

    ARTICLE

    Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

    Saud S. Alotaibi1, Amal Al-Rasheed2, Sami Althahabi3, Manar Ahmed Hamza4,*, Abdullah Mohamed5, Abu Sarwar Zamani4, Abdelwahed Motwakel4, Mohamed I. Eldesouki6

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3305-3318, 2022, DOI:10.32604/cmc.2022.029385

    Abstract Artificial Intelligence (AI) encompasses various domains such as Machine Learning (ML), Deep Learning (DL), and other cognitive technologies which have been widely applied in healthcare sector. AI models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input data. With this motivation, the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset, named MOKELM-CPED technique. The primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology dataset. In the proposed… More >

  • Open Access

    ARTICLE

    Network Invulnerability Enhancement Algorithm Based on WSN Closeness Centrality

    Qian Sun1,2, Fengbo Yang1,2, Xiaoyi Wang2,3, Jing Li4,*, Jiping Xu1,2, Huiyan Zhang1,2, Li Wang1,2, Jiabin Yu1,2, Xiao Peng1,2, Ruichao Wang5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3021-3038, 2022, DOI:10.32604/cmc.2022.029367

    Abstract Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that… More >

  • Open Access

    ARTICLE

    Optimal Deployment of Heterogeneous Nodes to Enhance Network Invulnerability

    Qian Sun1,2, Fengbo Yang1,2, Xiaoyi Wang2,3,*, Jiping Xu1,2, Huiyan Zhang1,2, Li Wang1,2, Jiabin Yu1,2, Xiao Peng1,2, Ruichao Wang4

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3081-3097, 2022, DOI:10.32604/cmc.2022.029366

    Abstract Wireless sensor networks (WSN) can be used in many fields. In wireless sensor networks, sensor nodes transmit data in multi hop mode. The large number of hops required by data transmission will lead to unbalanced energy consumption and large data transmission delay of the whole network, which greatly affects the invulnerability of the network. Therefore, an optimal deployment of heterogeneous nodes (ODHN) algorithm is proposed to enhance the invulnerability of the wireless sensor networks. The algorithm combines the advantages of DEEC (design of distributed energy efficient clustering) clustering algorithm and BAS (beetle antenna search) optimization algorithm to find the globally… More >

  • Open Access

    ARTICLE

    Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

    Radwa Marzouk1, Eatedal Alabdulkreem2, Sami Dhahbi3, Mohamed K. Nour4, Mesfer Al Duhayyim5, Mahmoud Othman6, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7, Ishfaq Yaseen7, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3905-3920, 2022, DOI:10.32604/cmc.2022.029326

    Abstract Oral cancer is the most commonly occurring ‘head and neck cancers’ across the globe. Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public. Since earlier identification of disease is essential for improved outcomes, Artificial Intelligence (AI) and Machine Learning (ML) models are used in this regard. In this background, the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model (AIDTL-OCCM). The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques. The proposed AIDTL-OCCM model involves… More >

  • Open Access

    ARTICLE

    State of Health Estimation of LiFePO4 Batteries for Battery Management Systems

    Areeb Khalid1,*, Syed Abdul Rahman Kashif1, Noor Ul Ain1, Ali Nasir2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3149-3164, 2022, DOI:10.32604/cmc.2022.029322

    Abstract When considering the mechanism of the batteries, the capacity reduction at storage (when not in use) and cycling (during use) and increase of internal resistance is because of degradation in the chemical composition inside the batteries. To optimize battery usage, a battery management system (BMS) is used to estimate possible aging effects while different load profiles are requested from the grid. This is specifically seen in a case when the vehicle is connected to the net (online through BMS). During this process, the BMS chooses the optimized load profiles based on the least aging effects on the battery pack. The… More >

  • Open Access

    ARTICLE

    Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

    Gulzar Ahmed1, Tahir Alyas2, Muhammad Waseem Iqbal3,*, Muhammad Usman Ashraf4, Ahmed Mohammed Alghamdi5, Adel A. Bahaddad6, Khalid Ali Almarhabi7

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2967-2984, 2022, DOI:10.32604/cmc.2022.029314

    Abstract Handwritten character recognition systems are used in every field of life nowadays, including shopping malls, banks, educational institutes, etc. Urdu is the national language of Pakistan, and it is the fourth spoken language in the world. However, it is still challenging to recognize Urdu handwritten characters owing to their cursive nature. Our paper presents a Convolutional Neural Networks (CNN) model to recognize Urdu handwritten alphabet recognition (UHAR) offline and online characters. Our research contributes an Urdu handwritten dataset (aka UHDS) to empower future works in this field. For offline systems, optical readers are used for extracting the alphabets, while diagonal-based… More >

  • Open Access

    ARTICLE

    An Algorithm for Target Detection of Engineering Vehicles Based on Improved CenterNet

    Pingping Yu1, Hongda Wang1, Xiaodong Zhao1,*, Guangchen Ruan2

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4261-4276, 2022, DOI:10.32604/cmc.2022.029239

    Abstract Aiming at the problems of low target image resolution, insufficient target feature extraction, low detection accuracy and poor real time in remote engineering vehicle detection, an improved CenterNet target detection model is proposed in this paper. Firstly, EfficientNet-B0 with Efficient Channel Attention (ECA) module is used as the basic network, which increases the quality and speed of feature extraction and reduces the number of model parameters. Then, the proposed Adaptive Fusion Bidirectional Feature Pyramid Network (AF-BiFPN) module is applied to fuse the features of different feature layers. Furthermore, the feature information of engineering vehicle targets is added by making full… More >

  • Open Access

    ARTICLE

    A Novel Stochastic Framework for the MHD Generator in Ocean

    Sakda Noinang1, Zulqurnain Sabir2, Shumaila Javeed3, Muhammad Asif Zahoor Raja4, Dostdar Ali3, Wajaree Weera5,*, Thongchai Botmart5

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3383-3402, 2022, DOI:10.32604/cmc.2022.029166

    Abstract This work aims to study the nonlinear ordinary differential equations (ODEs) system of magnetohydrodynamic (MHD) past over an inclined plate using Levenberg-Marquardt backpropagation neural networks (LMBNNs). The stochastic procedures LMBNNs are provided with three categories of sample statistics, testing, training, and verification. The nonlinear MHD system past over an inclined plate is divided into three profiles, dimensionless momentum, species (salinity), and energy (heat) conservations. The data is applied 15%, 10%, and 75% for validation, testing, and training to solve the nonlinear system of MHD past over an inclined plate. A reference data set is designed to compare the obtained and… More >

  • Open Access

    ARTICLE

    Trustworthiness Evaluation for Permissioned Blockchain-Enabled Applications

    Shi-Cho Cha1, Chuang-Ming Shiung1, Wen-Wei Li1, Chun-Neng Peng1, Yi-Hsuan Hung1, Kuo-Hui Yeh2,3,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2731-2755, 2022, DOI:10.32604/cmc.2022.029161

    Abstract As permissioned blockchain becomes a common foundation of blockchain-based circumstances for current organizations, related stakeholders need a means to assess the trustworthiness of the applications involved within. It is extremely important to consider the potential impact brought by the Blockchain technology in terms of security and privacy. Therefore, this study proposes a rigorous security risk management framework for permissioned blockchain-enabled applications. The framework divides itself into different implementation domains, i.e., organization security, application security, consensus mechanism security, node management and network security, host security and perimeter security, and simultaneously provides guidelines to control the security risks of permissioned blockchain applications… More >

  • Open Access

    ARTICLE

    Deep CNN Model for Multimodal Medical Image Denoising

    Walid El-Shafai1,2, Amira A. Mahmoud1, Anas M. Ali1,3, El-Sayed M. El-Rabaie1, Taha E. Taha1, Osama F. Zahran1, Adel S. El-Fishawy1, Naglaa F. Soliman4, Amel A. Alhussan5,*, Fathi E. Abd El-Samie1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3795-3814, 2022, DOI:10.32604/cmc.2022.029134

    Abstract In the literature, numerous techniques have been employed to decrease noise in medical image modalities, including X-Ray (XR), Ultrasonic (Us), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). These techniques are organized into two main classes: the Multiple Image (MI) and the Single Image (SI) techniques. In the MI techniques, images usually obtained for the same area scanned from different points of view are used. A single image is used in the entire procedure in the SI techniques. SI denoising techniques can be carried out both in a transform or spatial domain. This paper is concerned… More >

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