Bowei Wang¹, Wenzhong Tang¹, Lukai Song^2,3,*, Guangchen Bai³

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 171-193, 2022, DOI:10.32604/cmes.2022.021123

Abstract With increasing design demands of turbomachinery, stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety. Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability. However, for complex turbomachinery with multiple dynamic influencing factors (i.e., aeroengine compressor with time-variant loads), the stochastic flutter assessment is hard to be achieved effectively, since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain. To improve the assessing efficiency and accuracy of stochastic flutter behavior, a dynamic meta-modeling… More >

Muneerah Al Nuwairan^1,*, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Maryam Alnami¹, Hanan Almuslem¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4441-4454, 2022, DOI:10.32604/cmc.2022.029432

Abstract In this paper, a fractional order model based on the management of waste plastic in the ocean (FO-MWPO) is numerically investigated. The mathematical form of the FO-MWPO model is categorized into three components, waste plastic, Marine debris, and recycling. The stochastic numerical solvers using the Levenberg-Marquardt backpropagation neural networks (LMQBP-NNs) have been applied to present the numerical solutions of the FO-MWPO system. The competency of the method is tested by taking three variants of the FO-MWPO model based on the fractional order derivatives. The data ratio is provided for training, testing and authorization is 77%, 12%, and 11% respectively. The… More >

Sakda Noinang¹, Zulqurnain Sabir², Shumaila Javeed³, Muhammad Asif Zahoor Raja⁴, Dostdar Ali³, Wajaree Weera^5,*, Thongchai Botmart⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3383-3402, 2022, DOI:10.32604/cmc.2022.029166

Abstract This work aims to study the nonlinear ordinary differential equations (ODEs) system of magnetohydrodynamic (MHD) past over an inclined plate using Levenberg-Marquardt backpropagation neural networks (LMBNNs). The stochastic procedures LMBNNs are provided with three categories of sample statistics, testing, training, and verification. The nonlinear MHD system past over an inclined plate is divided into three profiles, dimensionless momentum, species (salinity), and energy (heat) conservations. The data is applied 15%, 10%, and 75% for validation, testing, and training to solve the nonlinear system of MHD past over an inclined plate. A reference data set is designed to compare the obtained and… More >

Mukhsar^1,*, Ansari Saleh Ahmar², M. A. El Safty³, Hamed El-Khawaga^4,5, M. El Sayed⁶

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1175-1186, 2022, DOI:10.32604/iasc.2022.025214

Abstract Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model… More >

Prem Junswang¹, Zulqurnain Sabir², Muhammad Asif Zahoor Raja³, Soheil Salahshour⁴, Thongchai Botmart^5,*, Wajaree Weera⁵

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5823-5843, 2022, DOI:10.32604/cmc.2022.027629

Abstract A novel design of the computational intelligent framework is presented to solve a class of host-vector-predator nonlinear model governed with set of ordinary differential equations. The host-vector-predator nonlinear model depends upon five groups or classes, host plant susceptible and infected populations, vectors population of susceptible and infected individuals and the predator population. An unsupervised artificial neural network is designed using the computational framework of local and global search competencies of interior-point algorithm and genetic algorithms. For solving the host-vector-predator nonlinear model, a merit function is constructed using the differential model and its associated boundary conditions. The optimization of this merit… More >

Marwa M. Eid^1,*, Fawaz Alassery², Abdelhameed Ibrahim³, Bandar Abdullah Aloyaydi⁴, Hesham Arafat Ali^1,3, Shady Y. El-Mashad⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2467-2482, 2022, DOI:10.32604/cmc.2022.025220

Abstract The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved… More >

Fatemeh Ahmadi Zeidabadi¹, Mohammad Dehghani², Pavel Trojovský^2,*, Štěpán Hubálovský³, Victor Leiva⁴, Gaurav Dhiman⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 399-416, 2022, DOI:10.32604/cmc.2022.024736

Abstract Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the… More >

Kazem Nouri^1,*, Milad Fahimi¹, Leila Torkzadeh¹, Dumitru Baleanu^2,3

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1495-1514, 2022, DOI:10.32604/cmc.2022.024406

Abstract The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.… More >

D. Jerusha^*, T. Jaya

Computer Systems Science and Engineering, Vol.42, No.3, pp. 1121-1132, 2022, DOI:10.32604/csse.2022.022997

Abstract Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in… More >

Haijie Pan, Lirong Zheng^*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 493-512, 2022, DOI:10.32604/cmes.2022.019069

Abstract The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD. To this end, we propose a noise reduction method for Stochastic Variance Reduction gradient (SVRG), called N-SVRG, which uses small batches samples instead of all samples for the average gradient calculation, while performing an incremental update of the average gradient. In each round of iteration, a small batch of samples is randomly selected for the average gradient calculation, while the average gradient is updated by rounding of the past model gradients during internal iterations. By suitably reducing the… More >