A reliable stochastic computational procedure to solve the mathematical robotic model

Zulqurnain Sabir; Salem Ben Said; Qasem Al-Mdallal; Shahid Ahmad Bhat

doi:10.1016/j.eswa.2023.122224

A reliable stochastic computational procedure to solve the mathematical robotic model

Zulqurnain Sabir, Salem Ben Said, Qasem Al-Mdallal, Shahid Ahmad Bhat

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

The current work presents the numerical solutions of the robotic system in the process of coronavirus. The stochastic performances using the modeling of Gudermannian neural networks (GDMNNs) are provided along with the global search genetic algorithm (GA) and rapid interior-point scheme (IPS), i.e., GDMNNs-GAIPS. An error function using the differential form of the model is created and then optimized by applying the hybridization of GA-IPS. The correctness and accuracy of the stochastic procedure GDMNNs-GAIPS is examined by using the comparison of the proposed and reference results. The reliability and substantiation of the proposed GDMNNs-GAIPS is authenticated by using the statistical operators based on the mean square error, Theil inequality coefficient and variance account for. Forty numbers of independent trials along with ten numbers of hidden neurons have been used to solve the mathematical model of robotic system to detect the positive cases of COVID-19.

Original language	English
Article number	122224
Journal	Expert Systems with Applications
Volume	238
DOIs	https://doi.org/10.1016/j.eswa.2023.122224
Publication status	Published - Mar 15 2024

Keywords

Coronavirus
Genetic algorithm
Gudermannian neural networks
Interior-point scheme
Robot mathematical model
Statistical analysis

ASJC Scopus subject areas

General Engineering
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2023.122224

Cite this

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title = "A reliable stochastic computational procedure to solve the mathematical robotic model",

abstract = "The current work presents the numerical solutions of the robotic system in the process of coronavirus. The stochastic performances using the modeling of Gudermannian neural networks (GDMNNs) are provided along with the global search genetic algorithm (GA) and rapid interior-point scheme (IPS), i.e., GDMNNs-GAIPS. An error function using the differential form of the model is created and then optimized by applying the hybridization of GA-IPS. The correctness and accuracy of the stochastic procedure GDMNNs-GAIPS is examined by using the comparison of the proposed and reference results. The reliability and substantiation of the proposed GDMNNs-GAIPS is authenticated by using the statistical operators based on the mean square error, Theil inequality coefficient and variance account for. Forty numbers of independent trials along with ten numbers of hidden neurons have been used to solve the mathematical model of robotic system to detect the positive cases of COVID-19.",

keywords = "Coronavirus, Genetic algorithm, Gudermannian neural networks, Interior-point scheme, Robot mathematical model, Statistical analysis",

author = "Zulqurnain Sabir and Said, {Salem Ben} and Qasem Al-Mdallal and Bhat, {Shahid Ahmad}",

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AU - Sabir, Zulqurnain

AU - Said, Salem Ben

AU - Al-Mdallal, Qasem

AU - Bhat, Shahid Ahmad

PY - 2024/3/15

Y1 - 2024/3/15

N2 - The current work presents the numerical solutions of the robotic system in the process of coronavirus. The stochastic performances using the modeling of Gudermannian neural networks (GDMNNs) are provided along with the global search genetic algorithm (GA) and rapid interior-point scheme (IPS), i.e., GDMNNs-GAIPS. An error function using the differential form of the model is created and then optimized by applying the hybridization of GA-IPS. The correctness and accuracy of the stochastic procedure GDMNNs-GAIPS is examined by using the comparison of the proposed and reference results. The reliability and substantiation of the proposed GDMNNs-GAIPS is authenticated by using the statistical operators based on the mean square error, Theil inequality coefficient and variance account for. Forty numbers of independent trials along with ten numbers of hidden neurons have been used to solve the mathematical model of robotic system to detect the positive cases of COVID-19.

AB - The current work presents the numerical solutions of the robotic system in the process of coronavirus. The stochastic performances using the modeling of Gudermannian neural networks (GDMNNs) are provided along with the global search genetic algorithm (GA) and rapid interior-point scheme (IPS), i.e., GDMNNs-GAIPS. An error function using the differential form of the model is created and then optimized by applying the hybridization of GA-IPS. The correctness and accuracy of the stochastic procedure GDMNNs-GAIPS is examined by using the comparison of the proposed and reference results. The reliability and substantiation of the proposed GDMNNs-GAIPS is authenticated by using the statistical operators based on the mean square error, Theil inequality coefficient and variance account for. Forty numbers of independent trials along with ten numbers of hidden neurons have been used to solve the mathematical model of robotic system to detect the positive cases of COVID-19.

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KW - Genetic algorithm

KW - Gudermannian neural networks

KW - Interior-point scheme

KW - Robot mathematical model

KW - Statistical analysis

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A reliable stochastic computational procedure to solve the mathematical robotic model

Abstract

Keywords

ASJC Scopus subject areas

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