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Junling Qiu^*, Huihui Sun, Shuya Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2077-2109, 2023, DOI:10.32604/cmes.2023.027353 - 03 August 2023
Abstract As indispensable components of superconducting circuit-based quantum computers, Josephson junctions determine how well superconducting qubits perform. Reverse Monte Carlo (RMC) can be used to recreate Josephson junction’s atomic structure based on experimental data, and the impact of the structure on junctions’ properties can be investigated by combining different analysis techniques. In order to build a physical model of the atomic structure and then analyze the factors that affect its performance, this paper briefly reviews the development and evolution of the RMC algorithm. It also summarizes the modeling process and structural feature analysis of the Josephson More >

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Cho Mar Aye¹, Kittinan Wansaseub², Sumit Kumar³, Ghanshyam G. Tejani⁴, Sujin Bureerat¹, Ali R. Yildiz⁵, Nantiwat Pholdee^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2111-2128, 2023, DOI:10.32604/cmes.2023.028632 - 03 August 2023
Abstract This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization. The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints. Computational Fluid Dynamic (CFD) and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value. A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed. To validate and further assess the proposed methods, a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied More >

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Qiankun Zuo^1,4, Junhua Hu², Yudong Zhang^3,*, Junren Pan⁴, Changhong Jing⁴, Xuhang Chen⁵, Xiaobo Meng⁶, Jin Hong^7,8,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2129-2147, 2023, DOI:10.32604/cmes.2023.028732 - 03 August 2023
Abstract The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven… More >

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Stepan Konev¹, Victor A. Eremeyev^2,3, Hamid M. Sedighi^4,5,*, Leonid Igumnov², Anatoly Bragov², Aleksandr Konstantinov², Ayaulym Kuanyshova¹, Ivan Sergeichev¹
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2149-2161, 2023, DOI:10.32604/cmes.2023.028179 - 03 August 2023
Abstract This article presents the results of experimental studies concerning the dynamic deformation and failure of a unidirectional carbon fiber reinforced plastic (T700/LY113) under compression. The test samples were manufactured through the filament winding of flat plates. To establish the strain rate dependencies of the strength and elastic modulus of the material, dynamic tests were carried out using a drop tower, the Split Hopkinson Pressure Bar method, and standard static tests. The samples were loaded both along and perpendicular to the direction of the reinforcing fiber. The applicability of the obtained samples for static and dynamic… More >

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Ali S. Alghamdi¹, Mohana Alanazi², Abdulaziz Alanazi³, Yazeed Qasaymeh^1,*, Muhammad Zubair^1,4, Ahmed Bilal Awan⁵, M. G. B. Ashiq⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2163-2192, 2023, DOI:10.32604/cmes.2023.029453 - 03 August 2023
Abstract To maximize energy profit with the participation of electricity, natural gas, and district heating networks in the day-ahead market, stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources, has been carried out. This has been done using a new meta-heuristic algorithm, improved artificial rabbits optimization (IARO). In this study, the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method (TPEM). The IARO algorithm is applied to calculate the best capacity of hub energy equipment, such as solar and wind renewable energy sources, combined heat… More >

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Ola Ragb¹, Mokhtar Mohamed², Mohamed S. Matbuly¹, Omer Civalek^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2193-2217, 2023, DOI:10.32604/cmes.2023.028992 - 03 August 2023
Abstract Four numerical schemes are introduced for the analysis of photocurrent transients in organic photovoltaic devices. The mathematical model for organic polymer solar cells contains a nonlinear diffusion–reaction partial differential equation system with electrostatic convection attached to a kinetic ordinary differential equation. To solve the problem, Polynomial-based differential quadrature, Sinc, and Discrete singular convolution are combined with block marching techniques. These schemes are employed to reduce the problem to a nonlinear algebraic system. The iterative quadrature technique is used to solve the reduced problem. The obtained results agreed with the previous exact one and the finite More >

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Hassan Okasha^1,2, Mazen Nassar^1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2219-2241, 2023, DOI:10.32604/cmes.2023.028783 - 03 August 2023
Abstract In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum More >

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508

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Jiepeng Yao^1,2, Zhanjia Peng^1,2, Jingjing Liu^1,2, Chengxiao Fan^1,2, Zhongyi Wang^1,2,3, Lan Huang^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2243-2265, 2023, DOI:10.32604/cmes.2023.028101 - 03 August 2023
Abstract In the establishment of differential equations, the determination of time-varying parameters is a difficult problem, especially for equations related to life activities. Thus, we propose a new framework named BioE-PINN based on a physical information neural network that successfully obtains the time-varying parameters of differential equations. In the proposed framework, the learnable factors and scale parameters are used to implement adaptive activation functions, and hard constraints and loss function weights are skillfully added to the neural network output to speed up the training convergence and improve the accuracy of physical information neural networks. In this… More >

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Jiaqun Zhu¹, Hongda Chen², Yiqing Fan¹, Tongguang Ni^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2267-2283, 2023, DOI:10.32604/cmes.2023.027709 - 03 August 2023
（This article belongs to the Special Issue: Computer Modeling for Smart Cities Applications)
Abstract To create a green and healthy living environment, people have put forward higher requirements for the refined management of ecological resources. A variety of technologies, including satellite remote sensing, Internet of Things, artificial intelligence, and big data, can build a smart environmental monitoring system. Remote sensing image classification is an important research content in ecological environmental monitoring. Remote sensing images contain rich spatial information and multi-temporal information, but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy. To solve this problem, this study develops a transductive transfer dictionary learning (TTDL)… More >

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Haoyu Fu^1,2,3, Hua Liu^1,2, Xiaohu Hu^1,2, Lei Wang^1,2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2285-2309, 2023, DOI:10.32604/cmes.2023.027996 - 03 August 2023
（This article belongs to the Special Issue: Modeling of Fluids Flow in Unconventional Reservoirs)
Abstract Well interference has become a common phenomenon with the increasing scale of horizontal well fracturing. Recent studies on well interference in horizontal wells do not properly reflect the physical model of the postfracturing well groups and the realistic fracturing process of infill wells. Establishing the correspondence between well interference causative factors and manifestations is of great significance for infill well deployment and secondary oil recovery. In this work, we develop a numerical model that considers low velocity non-Darcy seepage in shale reservoirs to study the inter-well interference phenomenon that occurs in the Santanghu field, and… More >

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Nosheen Fatima¹, Nabeela Kousar¹, Khalil Ur Rehman^2,3,*, Wasfi Shatanawi^2,4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2311-2330, 2023, DOI:10.32604/cmes.2023.026994 - 03 August 2023
（This article belongs to the Special Issue: Advanced Computational Methods in Fluid Mechanics and Heat Transfer)
Abstract A computational analysis of magnetized hybrid Darcy-Forchheimer nanofluid flow across a flat surface is presented in this work. For the study of heat and mass transfer aspects viscous dissipation, activation energy, Joule heating, thermal radiation, and heat generation effects are considered. The suspension of nanoparticles singlewalled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) are created by hybrid nanofluids. However, single-walled carbon nanotubes (SWCNTs) produce nanofluids, with water acting as conventional fluid, respectively. Nonlinear partial differential equations (PDEs) that describe the ultimate flow are converted to nonlinear ordinary differential equations (ODEs) using appropriate similarity transformation.… More >

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Jaimon Dennis Quadros¹, Sher Afghan Khan², Abdul Aabid^3,*, Muneer Baig³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2331-2352, 2023, DOI:10.32604/cmes.2023.028925 - 03 August 2023
（This article belongs to the Special Issue: Advanced Computational Methods in Fluid Mechanics and Heat Transfer)
Abstract The application of abruptly enlarged flows to adjust the drag of aerodynamic vehicles using machine learning models has not been investigated previously. The process variables (Mach number (M), nozzle pressure ratio (η), area ratio (α), and length to diameter ratio (γ )) were numerically explored to address several aspects of this process, namely base pressure (β) and base pressure with cavity (β_cav). In this work, the optimal base pressure is determined using the PCA-BAS-ENN based algorithm to modify the base pressure presetting accuracy, thereby regulating the base drag required for smooth flow of aerodynamic vehicles. Based… More >

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Lei Ling¹, Lijun Huang², Jie Wang², Li Zhang², Yue Wu², Yizhang Jiang¹, Kaijian Xia^2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2353-2379, 2023, DOI:10.32604/cmes.2023.028828 - 03 August 2023
（This article belongs to the Special Issue: Computer Modeling of Artificial Intelligence and Medical Imaging)
Abstract In recent years, the soft subspace clustering algorithm has shown good results for high-dimensional data, which can assign different weights to each cluster class and use weights to measure the contribution of each dimension in various features. The enhanced soft subspace clustering algorithm combines interclass separation and intraclass tightness information, which has strong results for image segmentation, but the clustering algorithm is vulnerable to noisy data and dependence on the initialized clustering center. However, the clustering algorithm is susceptible to the influence of noisy data and reliance on initialized clustering centers and falls into a… More >

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Fei Li¹, Haci Mehmet Baskonus^2,*, S. Kumbinarasaiah³, G. Manohara³, Wei Gao⁴, Esin Ilhan⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2381-2408, 2023, DOI:10.32604/cmes.2023.028069 - 03 August 2023
（This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
Abstract This article considers three types of biological systems: the dengue fever disease model, the COVID-19 virus model, and the transmission of Tuberculosis model. The new technique of creating the integration matrix for the Bernoulli wavelets is applied. Also, the novel method proposed in this paper is called the Bernoulli wavelet collocation scheme (BWCM). All three models are in the form system of coupled ordinary differential equations without an exact solution. These systems are converted into a system of algebraic equations using the Bernoulli wavelet collocation scheme. The numerical wave distributions of these governing models are More >

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Ibtehal Alazman¹, Kholoud Saad Albalawi¹, Pranay Goswami^2,*, Kuldeep Malik²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2409-2425, 2023, DOI:10.32604/cmes.2023.028674 - 03 August 2023
（This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
Abstract This paper presents a restricted SIR mathematical model to analyze the evolution of a contagious infectious disease outbreak (COVID-19) using available data. The new model focuses on two main concepts: first, it can present multiple waves of the disease, and second, it analyzes how far an infection can be eradicated with the help of vaccination. The stability analysis of the equilibrium points for the suggested model is initially investigated by identifying the matching equilibrium points and examining their stability. The basic reproduction number is calculated, and the positivity of the solutions is established. Numerical simulations More >

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Yu-Ming Chu¹, Sobia Sultana², Saima Rashid^3,*, Mohammed Shaaf Alharthi⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2427-2464, 2023, DOI:10.32604/cmes.2023.028771 - 03 August 2023
（This article belongs to the Special Issue: Recent Developments on Computational Biology-I)
Abstract Various data sets showing the prevalence of numerous viral diseases have demonstrated that the transmission is not truly homogeneous. Two examples are the spread of Spanish flu and COVID-19. The aim of this research is to develop a comprehensive nonlinear stochastic model having six cohorts relying on ordinary differential equations via piecewise fractional differential operators. Firstly, the strength number of the deterministic case is carried out. Then, for the stochastic model, we show that there is a critical number that can predict virus persistence and infection eradication. Because of the peculiarity of More >

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Muzi Li^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827 - 03 August 2023
（This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

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Wangpeng He^1,*, Yue Zhou¹, Xiaoya Guo², Deshun Hu¹, Junjie Ye³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896 - 03 August 2023
（This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process.… More >

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B. Ramesh, Kuruva Lakshmanna^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2513-2528, 2023, DOI:10.32604/cmes.2023.028944 - 03 August 2023
（This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract Major chronic diseases such as Cardiovascular Disease (CVD), diabetes, and cancer impose a significant burden on people and healthcare systems around the globe. Recently, Deep Learning (DL) has shown great potential for the development of intelligent mobile Health (mHealth) interventions for chronic diseases that could revolutionize the delivery of health care anytime, anywhere. The aim of this study is to present a systematic review of studies that have used DL based on mHealth data for the diagnosis, prognosis, management, and treatment of major chronic diseases and advance our understanding of the progress made in this… More >

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Mahmood Hussain Mir^1,*, Sanjay Jamwal¹, Ummer Iqbal², Abolfazl Mehbodniya³, Julian Webber³, Umar Hafiz Khan⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2529-2565, 2023, DOI:10.32604/cmes.2023.027173 - 03 August 2023
（This article belongs to the Special Issue: Smart and Secure Solutions for Medical Industry)
Abstract The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID- 19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework… More >

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Chong Xu¹, Hao Zhu¹, Haotian Zhu², Jirong Wang¹, Qinghai Zhao^1,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2567-2591, 2023, DOI:10.32604/cmes.2023.029152 - 03 August 2023
（This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
Abstract A new and improved RRT^∗ algorithm has been developed to address the low efficiency of obstacle avoidance planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used to optimize the rough path to create a smoother More >

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Zeyuan Zhou, Ming Yu, Xinfeng Wang^*, Zaixing Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2593-2620, 2023, DOI:10.32604/cmes.2023.027384 - 03 August 2023
（This article belongs to the Special Issue: Peridynamics and its Current Progress)
Abstract How to simulate fracture mode and crack propagation path in a plate with multiple cracks is an attractive but dicult issue in fracture mechanics. Peridynamics is a recently developed nonlocal continuum formulation that can spontaneously predict the crack nucleation, branch and propagation in materials and structures through a meshfree discrete technique. In this paper, the peridynamic motion equation with boundary traction is improved by simplifying the boundary transfer functions. We calculate the critical cracking load and the fracture angles of the plate with multiple cracks under uniaxial tension. The results are consistent with those predicted More >

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Jing Xin^1,*, Kenan Du¹, Jiale Feng¹, Mao Shan²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467 - 03 August 2023
（This article belongs to the Special Issue: Advanced Intelligent Decision and Intelligent Control with Applications in Smart City)
Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud More >

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Lilan Zou¹, Bo Liang¹, Xu Cheng², Shufa Li^1,*, Cong Lin^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2641-2659, 2023, DOI:10.32604/cmes.2023.028037 - 03 August 2023
（This article belongs to the Special Issue: AI-Driven Intelligent Sensor Networks: Key Enabling Theories, Architectures, Modeling, and Techniques)
Abstract Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment. In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment, we proposed a more effective and robust target detection framework based on deep learning, which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection. Firstly, the weighted box fusion method is adopted to generate a fusion box by weighted… More >

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Baofeng Ji^1,2,3,*, Mingkun Zhang^1,2, Weixing Wang¹, Song Chen⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2661-2673, 2023, DOI:10.32604/cmes.2023.028762 - 03 August 2023
（This article belongs to the Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
Abstract The improvement of the quality and efficiency of vehicle wireless network data transmission is always a key concern in the Internet of Vehicles (IoV). Routing transmission solved the limitation of transmission distance to a certain extent. Traditional routing algorithm cannot adapt to complex traffic environment, resulting in low transmission efficiency. In order to improve the transmission success rate and quality of vehicle network routing transmission, make the routing algorithm more suitable for complex traffic environment, and reduce transmission power consumption to improve energy efficiency, a comprehensive optimized routing transmission algorithm is proposed. Based on the… More >

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Yingying Chen¹, Weiqiang Tan², Shidang Li^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2675-2691, 2023, DOI:10.32604/cmes.2023.028533 - 03 August 2023
（This article belongs to the Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)
Abstract In this paper, a novel fairness-aware harvested energy efficiency-based green transmission scheme for wireless information and power transfer (SWIPT) aided sensor networks is developed for active beamforming of multiantenna transmitter and passive beamforming at intelligent reflecting surfaces (IRS). By optimizing the active beamformer assignment at the transmitter in conjunction with the passive beamformer assignment at the IRS, we aim to maximize the minimum harvested energy efficiency among all the energy receivers (ER) where information receivers (IR) are bound to the signal-interference-noise-ratio (SINR) and the maximum transmitted power of the transmitter. To handle the non-convex problem, More >

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Shuilong He^1,2, Yang Liu¹, Shanchao Wang^2,*, Liangying Hu¹, Fei Xiao², Chao Li²
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2693-2714, 2023, DOI:10.32604/cmes.2023.028631 - 03 August 2023
（This article belongs to the Special Issue: Computing Methods for Industrial Artificial Intelligence)
Abstract The reduction of fuel consumption in engines is always considered of vital importance. Along these lines, in this work, this goal was attained by optimizing the heavy-duty commercial vehicle engine control strategy. More specifically, at first, a general first principles model for heavy-duty commercial vehicles and a transient fuel consumption model for heavy-duty commercial vehicles were developed and the parameters were adjusted to fit the empirical data. The accuracy of the proposed model was demonstrated from the stage and the final results. Next, the control optimization problem resulting in low fuel consumption in heavy commercial… More >

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Yaling Xu, Congjun Rao^*, Xinping Xiao, Fuyan Hu^*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2715-2742, 2023, DOI:10.32604/cmes.2023.029023 - 03 August 2023
（This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
Abstract As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to… More >

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Ning Hu¹, Ling Liu¹, Xin Liu³, Kaijun Wu², Yue Zhao^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2743-2759, 2023, DOI:10.32604/cmes.2023.026939 - 03 August 2023
（This article belongs to the Special Issue: Emerging Trends on Blockchain: Architecture and Dapp Ecosystem)
Abstract With the widespread application of cloud computing and network virtualization technologies, more and more enterprise applications are directly deployed in the cloud. However, the traditional TCP/IP network transmission model does not fully consider the information security issues caused by the uncontrollable internet environment. Network security communication solutions represented by encrypted virtual private networks (VPN) are facing multiple security threats. In fact, during the communication process, the user application needs to protect not only the content of the communication but also the behavior of the communication, such as the communication relationship, the communication protocol, and so… More >

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Heba S. Mohammed¹, Mazen Nassar^2,3, Refah Alotaibi¹, Ahmed Elshahhat^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2761-2793, 2023, DOI:10.32604/cmes.2023.028826 - 03 August 2023
（This article belongs to the Special Issue: Application of Computer Tools in the Study of Mathematical Problems)
Abstract A new extended exponential lifetime model called Harris extended-exponential (HEE) distribution for data modelling with increasing and decreasing hazard rate shapes has been considered. In the reliability context, researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample size. So, this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring strategy. Under the premise of independent gamma priors, the Bayesian estimation is created using the squared-error and… More >

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Shahzad Noor Abbasi¹, Shahzaib Ashraf^1,*, M. Shazib Hameed¹, Sayed M. Eldin^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2795-2844, 2023, DOI:10.32604/cmes.2023.028963 - 03 August 2023
（This article belongs to the Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)
Abstract The primary goal of this research is to determine the optimal agricultural field selection that would most effectively support manufacturing producers in manufacturing production while accounting for unpredictability and reliability in their decision-making. The PFS is known to address the levels of participation and non-participation. To begin, we introduce the novel concept of a PFZN, which is a hybrid structure of Pythagorean fuzzy sets and the ZN. The PFZN is graded in terms of membership and non-membership, as well as reliability, which provides a strong advice in real-world decision support concerns. The PFZN is a… More >

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Abdourahamane Salifou Adam¹, Hatem Mrad¹, Haykel Marouani^2,*, Yasser Fouad³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2845-2865, 2023, DOI:10.32604/cmes.2023.030777 - 03 August 2023
（This article belongs to the Special Issue: Structural Design and Optimization)
Abstract A high percentage of failure in pump elements originates from fatigue. This study focuses on the discharge section behavior, made of ductile iron, under dynamic load. An experimental protocol is established to collect the strain under pressurization and depressurization tests at specific locations. These experimental results are used to formulate the ultimate pressure expression function of the strain and the lateral surface of the discharge section and to validate finite element modeling. Fe-Safe is then used to assess the fatigue life cycle using different types of fatigue criteria (Coffin-Manson, Morrow, Goodman, and Soderberg). When the… More >

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Amit V. Patel¹, Arpan Desai¹, Issa Elfergani^2,3,*, Hiren Mewada⁴, Chemseddine Zebiri⁵, Keyur Mahant¹, Jonathan Rodriguez², Raed Abd-Alhameed³
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2867-2879, 2023, DOI:10.32604/cmes.2023.026200 - 03 August 2023
（This article belongs to the Special Issue: Green IoE for Smart 5G and beyond (5GB) Applications)
Abstract A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed for millimeter-wave communication systems in this paper. The multiple-input-multiple-output (MIMO) antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back. The overall size of the four elements MIMO antenna is 2.24λ × 2.24λ (at 27.12 GHz). The prototype of four-element MIMO resonator is designed and printed using Rogers RT Duroid 5880 with ε_r = 2.2 and loss tangent = 0.0009 and having a thickness of More >

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ARTICLE

Zekai Li¹, Wenfeng Wang^2,3,4,5,6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2881-2899, 2023, DOI:10.32604/cmes.2023.028670 - 03 August 2023
（This article belongs to the Special Issue: Federated Learning Algorithms, Approaches, and Systems for Internet of Things)
Abstract We advanced an emerging federated learning technology in city intelligentization for tackling a real challenge— to learn damaged objects in aerial videos. A meta-learning system was integrated with the fuzzy broad learning system to further develop the theory of federated learning. Both the mixed picture set of aerial video segmentation and the 3D-reconstructed mixed-reality data were employed in the performance of the broad federated meta-learning system. The study results indicated that the object classification accuracy is up to 90% and the average time cost in damage detection is only 0.277 s. Consequently, the broad federated More >

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