A reliable deep neural network using the radial basis for the spreading virus in computers with kill signals

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems Pub Date : 2026-01-15 Epub Date: 2025-11-01 DOI:10.1016/j.chemolab.2025.105560

Zulqurnain Sabir , Bahaa Basbous , Basma Souayeh , Muhammad Umar , Soheil Salahshour

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引用次数: 0

Abstract

Purpose

The purpose of this work is to provide a reliable neural network process for the spreading virus in computers with kill signals. The mathematical model shows susceptible, exposed, infected individuals to form the virus inactive, and kill signals classes.

Method

A structure of deep neural network (DNN) is designed by using two different hidden layers having radial basis activation functions in both layers, optimization through the Bayesian regularization, twenty and thirty numbers of neurons in primary and secondary hidden layers for the spreading virus in computers with kill signals. The stochastic DNN framework is presented to solve the spreading virus in computers with kill signals by selecting the data for training as 70 %, and 15 %, 15 % for both validation and testing.

Results

The accuracy of the scheme is observed through the overlapping of the solutions along with negligible absolute error for solving the model. The consistency of the solver is observed through the process of error histogram, regression, and state transition.

Novelty

The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.

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一种基于径向基的可靠深度神经网络，用于具有杀伤信号的计算机中病毒的传播

目的为具有杀伤信号的计算机中病毒的传播提供一种可靠的神经网络过程。该数学模型显示了易感、暴露、感染个体形成的病毒灭活和杀伤信号等级。方法采用两层具有径向基激活函数的不同隐层设计深度神经网络（DNN）结构，通过贝叶斯正则化优化，在主隐层和次隐层分别设置20和30个神经元，用于在具有杀伤信号的计算机中传播病毒。提出了随机深度神经网络框架，通过选择训练数据为70%，验证数据为15%，测试数据为15%，来解决具有杀死信号的计算机中病毒的传播问题。结果通过解的重叠观察到该方案的精度，求解模型的绝对误差可以忽略不计。通过误差直方图、回归和状态转移的过程来观察求解器的一致性。新颖提出的具有径向基激活函数的深度神经网络结构尚未应用于具有杀伤信号的计算机中病毒的传播。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Chemometrics and Intelligent Laboratory Systems 工程技术-分析化学

CiteScore

7.50

自引率

7.70%

发文量

169

审稿时长

3.4 months

期刊介绍： Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.