A Novel Radial Basis and Sigmoid Neural Network Combination to Solve the Human Immunodeficiency Virus System in Cancer Patients

Zulqurnain Sabir, Sahar Dirani, Sara Bou Saleh, Mohamad Khaled Mabsout and Adnène Arbi ()
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Zulqurnain Sabir: Department of Computer Science and Mathematics, Lebanese American University, Beirut 1107, Lebanon
Sahar Dirani: Department of Computer Science and Mathematics, Lebanese American University, Beirut 1107, Lebanon
Sara Bou Saleh: Department of Computer Science and Mathematics, Lebanese American University, Beirut 1107, Lebanon
Mohamad Khaled Mabsout: Department of Computer Science and Mathematics, Lebanese American University, Beirut 1107, Lebanon
Adnène Arbi: Department of LIM (LR01ES13), Polytechnic School of Tunisia (EPT), University of Carthage, Tunis 1054, Tunisia

Mathematics, 2024, vol. 12, issue 16, 1-13

Abstract: The purpose of this work is to design a novel process based on the deep neural network (DNN) process to solve the dynamical human immunodeficiency virus (HIV-1) infection system in cancer patients (HIV-1-ISCP). The dual hidden layer neural network structure using the combination of a radial basis and sigmoid function with twenty and forty neurons is presented for the solution of the nonlinear HIV-1-ISCP. The mathematical form of the model is divided into three classes named cancer population cells ( T ), healthy cells ( H ), and infected HIV (I) cells. The validity of the designed novel scheme is proven through the comparison of the results. The optimization is performed using a competent scale conjugate gradient procedure, the correctness of the proposed numerical approach is observed through the reference results, and negligible values of the absolute error are around 10 −3 to 10 −4 . The database numerical solutions are achieved from the Runge–Kutta numerical scheme, and are used further to reduce the mean square error by taking 72% of the data for training, while 14% of the data is taken for testing and substantiations. To authenticate the credibility of this novel procedure, graphical plots using different performances are derived.

Keywords: neural network; human immunodeficiency virus; radial basis and sigmoid function; hidden layers; scale conjugate (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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