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  • Open Access

    ARTICLE

    Numerical Treatments for Crossover Cancer Model of Hybrid Variable-Order Fractional Derivatives

    Nasser Sweilam1, Seham Al-Mekhlafi2,*, Aya Ahmed3, Ahoud Alsheri4, Emad Abo-Eldahab3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1619-1645, 2024, DOI:10.32604/cmes.2024.047896

    Abstract In this paper, two crossover hybrid variable-order derivatives of the cancer model are developed. Grünwald-Letnikov approximation is used to approximate the hybrid fractional and variable-order fractional operators. The existence, uniqueness, and stability of the proposed model are discussed. Adams Bashfourth’s fifth-step method with a hybrid variable-order fractional operator is developed to study the proposed models. Comparative studies with generalized fifth-order Runge-Kutta method are given. Numerical examples and comparative studies to verify the applicability of the used methods and to demonstrate the simplicity of these approximations are presented. We have showcased the efficiency of the proposed More >

  • Open Access

    ARTICLE

    Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

    P. Saranya*, P. Asha

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1137-1149, 2023, DOI:10.32604/iasc.2023.031470

    Abstract In today’s growing modern world environment, as human food activities are changing, it is affecting human health, thus leading to diseases like cancer. Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death. So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observation, which has become necessary to classify the type in cancer research. The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature… More >

  • Open Access

    ARTICLE

    Computational Algorithms for the Analysis of Cancer Virotherapy Model

    Ali Raza1,2,*, Dumitru Baleanu3,4, Muhammad Rafiq5, Syed Zaheer Abbas6, Abubakar Siddique6, Umer Javed8, Mehvish Naz7, Arooj Fatima6, Tayyba Munawar6, Hira Batool6, Zaighum Nazir6

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3621-3634, 2022, DOI:10.32604/cmc.2022.023286

    Abstract Cancer is a common term for many diseases that can affect any part of the body. In 2020, ten million people will die due to cancer. A worldwide leading cause of death is cancer by the World Health Organization (WHO) report. Interaction of cancer cells, viral therapy, and immune response are identified in this model. Mathematical and computational modeling is an effective tool to predict the dynamics of cancer virotherapy. The cell population is categorized into three parts like uninfected cells (x), infected cells (y), virus-free cells (v), and immune cells (z). The modeling of More >

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