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  • Open Access

    ARTICLE

    Deep Learning-Based Hookworm Detection in Wireless Capsule Endoscopic Image Using AdaBoost Classifier

    K. Lakshminarayanan1, N. Muthukumaran1, Y. Harold Robinson2, Vimal Shanmuganathan3, Seifedine Kadry4, Yunyoung Nam5,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3045-3055, 2021, DOI:10.32604/cmc.2021.014370

    Abstract Hookworm is an illness caused by an internal sponger called a roundworm. Inferable from deprived cleanliness in the developing nations, hookworm infection is a primary source of concern for both motherly and baby grimness. The current framework for hookworm detection is composed of hybrid convolutional neural networks; explicitly an edge extraction framework alongside a hookworm classification framework is developed. To consolidate the cylindrical zones obtained from the edge extraction framework and the trait map acquired into the hookworm scientific categorization framework, pooling layers are proposed. The hookworms display different profiles, widths, and bend directions. These challenges make it difficult for… More >

  • Open Access

    ARTICLE

    Enhanced KOCED Routing Protocol with K-means Algorithm

    SeaYoung Park1, Jong-Yong Lee2, Daesung Lee3,*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4019-4037, 2021, DOI:10.32604/cmc.2021.014353

    Abstract Replacing or recharging batteries in the sensor nodes of a wireless sensor network (WSN) is a significant challenge. Therefore, efficient power utilization by sensors is a critical requirement, and it is closely related to the life span of the network. Once a sensor node consumes all its energy, it will no longer function properly. Therefore, various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation. Recently, clustering algorithms combined with artificial intelligence have been proposed for this purpose. In particular, various protocols employ the K-means clustering algorithm, which is a machine learning… More >

  • Open Access

    ARTICLE

    State-Based Offloading Model for Improving Response Rate of IoT Services

    K. Sakthidasan1, Bhekisipho Twala2, S. Yuvaraj3, K. Vijayan3,*, S. Praveenkumar3, Prashant Mani4, C. Bharatiraja3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3721-3735, 2021, DOI:10.32604/cmc.2021.014321

    Abstract The Internet of Things (IoT) is a heterogeneous information sharing and access platform that provides services in a pervasive manner. Task and computation offloading in the IoT helps to improve the response rate and the availability of resources. Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing. In this paper, the state-based task offloading method (STOM) is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities. The proposed method is designed using the Markov decision-making model to improve the rate of requests… More >

  • Open Access

    ARTICLE

    Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

    Ishu Anand1, Himani Negi1, Deepika Kumar1, Mamta Mittal2, Tai-hoon Kim3,*, Sudipta Roy4

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

    Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR images.… More >

  • Open Access

    ARTICLE

    Quality of Service Aware Cluster Routing in Vehicular Ad Hoc Networks

    Ishtiaq Wahid1, Fasee Ullah2,*, Masood Ahmad1, Atif Khan3, M. Irfan Uddin4, Abdullah Alharbi5, Wael Alosaimi5

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3949-3965, 2021, DOI:10.32604/cmc.2021.014190

    Abstract In vehicular ad hoc networks (VANETs), the topology information (TI) is updated frequently due to vehicle mobility. These frequent changes in topology increase the topology maintenance overhead. To reduce the control message overhead, cluster-based routing schemes are proposed. In cluster-based routing schemes, the nodes are divided into different virtual groups, and each group (logical node) is considered a cluster. The topology changes are accommodated within each cluster, and broadcasting TI to the whole VANET is not required. The cluster head (CH) is responsible for managing the communication of a node with other nodes outside the cluster. However, transmitting real-time data… More >

  • Open Access

    ARTICLE

    Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

    Neelum Noreen1,*, Sellapan Palaniappan1, Abdul Qayyum2, Iftikhar Ahmad3, Madini O. Alassafi3

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3967-3982, 2021, DOI:10.32604/cmc.2021.014158

    Abstract Brain tumors are life-threatening for adults and children. However, accurate and timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, and pituitary tumors. Many studies describe the analysis and classification of brain tumors, but few have looked at the problem of feature engineering. Methods are needed to overcome the drawbacks of manual diagnosis and conventional feature-engineering techniques. An automatic diagnostic system is thus necessary to extract features and classify brain tumors accurately. While progress continues to be made, the automatic diagnoses of brain tumors still face challenges of low accuracy and high… More >

  • Open Access

    ARTICLE

    Epidemiological Analysis of the Coronavirus Disease Outbreak with Random Effects

    Muhammad Farman1, Aqeel Ahmad1, Ali Akgül2,*, Muhammad Umer Saleem3, Muhammad Naeem4, Dumitru Baleanu5,6,7

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3215-3227, 2021, DOI:10.32604/cmc.2021.014006

    Abstract Today, coronavirus appears as a serious challenge to the whole world. Epidemiological data of coronavirus is collected through media and web sources for the purpose of analysis. New data on COVID-19 are available daily, yet information about the biological aspects of SARS-CoV-2 and epidemiological characteristics of COVID-19 remains limited, and uncertainty remains around nearly all its parameters’ values. This research provides the scientific and public health communities better resources, knowledge, and tools to improve their ability to control the infectious diseases. Using the publicly available data on the ongoing pandemic, the present study investigates the incubation period and other time… More >

  • Open Access

    ARTICLE

    Analysis of Silver Nanoparticles in Engine Oil: Atangana–Baleanu Fractional Model

    Saqib Murtaza1, Farhad Ali2,3,*, Nadeem Ahmad Sheikh1, Ilyas Khan4, Kottakkaran Sooppy Nisar5

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2915-2932, 2021, DOI:10.32604/cmc.2021.013757

    Abstract The present article aims to examine the heat and mass distribution in a free convection flow of electrically conducted, generalized Jeffrey nanofluid in a heated rotatory system. The flow analysis is considered in the presence of thermal radiation and the transverse magnetic field of strength B0. The medium is porous accepting generalized Darcy’s law. The motion of the fluid is due to the cosine oscillations of the plate. Nanofluid has been formed by the uniform dispersing of the Silver nanoparticles in regular engine oil. The problem has been modeled in the form of classical partial differential equations and then generalized… More >

  • Open Access

    ARTICLE

    Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

    Emad-ul-Haq Qazi, Muhammad Hussain*, Hatim A. AboAlsamh

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3329-3348, 2021, DOI:10.32604/cmc.2021.013589

    Abstract The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain… More >

  • Open Access

    ARTICLE

    A Cell-Based Smoothed Finite Element Method for Modal Analysis of Non-Woven Fabrics

    Nguyễn T. Quyền1,*, N. Dourado2, A. J. P. Gomes3,4, F. B. N. Ferreira1

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2765-2795, 2021, DOI:10.32604/cmc.2021.013164

    Abstract The combination of a 4-node quadrilateral mixed interpolation of tensorial components element (MITC4) and the cell-based smoothed finite element method (CSFEM) was formulated and implemented in this work for the analysis of free vibration and unidirectional buckling of shell structures. This formulation was applied to numerous numerical examples of non-woven fabrics. As CSFEM schemes do not require coordinate transformation, spurious modes and numerical instabilities are prevented using bilinear quadrilateral element subdivided into two, three and four smoothing cells. An improvement of the original CSFEM formulation was made regarding the calculation of outward unit normal vectors, which allowed to remove the… More >

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