Nisar Kottakkaran Sooppy, Naz Iqra, Raja Muhammad Asif Zahoor, Shoaib Muhammad
Department of Mathematics, College of Science and Humanities in Al Kharj, Prince Sattam bin Abdulaziz University, 11942, Saudi Arabia; Saveetha School of Engineering, SIMATS, Chennai, India.
Department of Mathematics, COMSATS University Islamabad, Attock Campus, Pakistan.
Comput Biol Chem. 2024 Dec;113:108234. doi: 10.1016/j.compbiolchem.2024.108234. Epub 2024 Oct 2.
The optimum control methods for the epidemiology of the COVID-19 model are acknowledged using a novel advanced intelligent computing infrastructure that joins artificial neural networks with unsupervised learning-based optimizers i.e., Genetic Algorithms (GA) and sequential quadratic programming (SQP). Unsupervised learning strategy is provided which depends on the wavelet basis's sequential deconstruction of stochastic data. The weights or selection values of neural networks are utilizing cumulative algorithms of Meyer wavelet artificial neural networks (MWANNs) optimized with global search Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP), referred to as MWANNs-GA-SQP and the design technique is utilized to determine the COVID-19 model for five different scenarios employing different step sizes and input intervals. The findings of this research article examined that in order to minimize the total disease transmission at the lowest cost and complexity, safety, focused medical care, and exterior sterilization methods applicability. The provided data is validated through various graphical simulations, which surely authenticate the effectiveness and robustness of the proposed solver. The suggested solver, MWANNs-GA-SQP, is tested in a variety of circumstances to examine that how reliable, safe, and tolerant. Using the proposed MWANNs hubristic intelligent approach, an objective optimization function is created in feed forward neural networking to minimize the mean square error. An investigation of the hybrid GA-SQP is used to confirm the accuracy and dependability of the MWANNs model results. Mean absolute graphs have been constructed to assess the integrity and efficiency of the proposed methodology. The accuracy and reliability of the suggested method are demonstrated by constantly achieving maximum variables of analytical assessment criteria computed for a large appropriate variety of distinct trials.
利用一种新颖的先进智能计算基础设施来确定新冠病毒疾病传播模型的最佳控制方法，该基础设施将人工神经网络与基于无监督学习的优化器（即遗传算法（GA）和序列二次规划（SQP））相结合。提供了一种无监督学习策略，该策略依赖于基于小波基的随机数据序列解构。神经网络的权重或选择值利用了经全局搜索遗传算法（GA）和序列二次规划（SQP）优化的迈耶小波人工神经网络（MWANNs）的累积算法，称为MWANNs - GA - SQP，并且利用该设计技术来确定五种不同场景下采用不同步长和输入间隔的新冠病毒疾病传播模型。本研究文章的结果表明，为了以最低的成本和复杂度、安全性、集中医疗护理以及外部消毒方法的适用性来最小化疾病的总传播。通过各种图形模拟对所提供的数据进行了验证，这肯定地证实了所提出求解器的有效性和稳健性。所建议的求解器MWANNs - GA - SQP在各种情况下进行了测试，以检验其可靠性、安全性和耐受性。使用所提出的MWANNs傲慢智能方法，在前馈神经网络中创建了一个目标优化函数以最小化均方误差。对混合GA - SQP的研究用于确认MWANNs模型结果的准确性和可靠性。已构建平均绝对图来评估所提出方法的完整性和效率。通过不断实现为大量适当的不同试验计算的分析评估标准的最大变量，证明了所建议方法的准确性和可靠性。
