Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (525)
  • Open Access

    ARTICLE

    Unsteady MHD Casson Nanofluid Flow Past an Exponentially Accelerated Vertical Plate: An Analytical Strategy

    T. Aghalya, R. Tamizharasi*

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.046635

    Abstract In this study, the characteristics of heat transfer on an unsteady magnetohydrodynamic (MHD) Casson nanofluid over an exponentially accelerated vertical porous plate with rotating effects were investigated. The flow was driven by the combined effects of the magnetic field, heat radiation, heat source/sink and chemical reaction. Copper oxide () and titanium oxide () are acknowledged as nanoparticle materials. The nondimensional governing equations were subjected to the Laplace transformation technique to derive closed-form solutions. Graphical representations are provided to analyze how changes in physical parameters, such as the magnetic field, heat radiation, heat source/sink and chemical reaction, affect the velocity, temperature… More >

  • Open Access

    ARTICLE

    Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

    Murugan Palanikumar1, Nasreen Kausar2,*, Dragan Pamucar3,4, Seifedine Kadry5,6,7,*, Chomyong Kim8, Yunyoung Nam9

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.031439

    Abstract In this article, multiple attribute decision-making problems are solved using the vague normal set (VNS). It is possible to generalize the vague set (VS) and q-rung fuzzy set (FS) into the q-rung vague set (VS). A log q-rung normal vague weighted averaging (log q-rung NVWA), a log q-rung normal vague weighted geometric (log q-rung NVWG), a log generalized q-rung normal vague weighted averaging (log Gq-rung NVWA), and a log generalized q-rung normal vague weighted geometric (log Gq-rung NVWG) operator are discussed in this article. A description is provided of the scoring function, accuracy function and operational laws of the log… More >

  • Open Access

    ARTICLE

    Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

    Haosong Gou1, Gaoyi Zhang1, Renê Ripardo Calixto2, Senthil Kumar Jagatheesaperumal3, Victor Hugo C. de Albuquerque2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.047806

    Abstract Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally,… More >

  • Open Access

    ARTICLE

    Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

    Chengkan Xu1,2,4, Xiaofei Wang3, Yixuan Li2, Guannan Wang2,*, He Zhang2,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.047327

    Abstract Structural damage in heterogeneous materials typically originates from microstructures where stress concentration occurs. Therefore, evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial. Repeating unit cells (RUCs) are commonly used to represent microstructural details and homogenize the effective response of composites. This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs. The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters, including volume fraction, fiber/matrix property ratio, fiber shapes, and loading direction. Subsequently, the conditional generative adversarial network… More > Graphic Abstract

    Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

  • Open Access

    ARTICLE

    A Hybrid SIR-Fuzzy Model for Epidemic Dynamics: A Numerical Study

    Muhammad Shoaib Arif1,2,*, Kamaleldin Abodayeh1, Yasir Nawaz2

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.046944

    Abstract This study focuses on the urgent requirement for improved accuracy in disease modeling by introducing a new computational framework called the Hybrid SIR-Fuzzy Model. By integrating the traditional Susceptible-Infectious-Recovered (SIR) model with fuzzy logic, our method effectively addresses the complex nature of epidemic dynamics by accurately accounting for uncertainties and imprecisions in both data and model parameters. The main aim of this research is to provide a model for disease transmission using fuzzy theory, which can successfully address uncertainty in mathematical modeling. Our main emphasis is on the imprecise transmission rate parameter, utilizing a three-part description of its membership level.… More >

  • Open Access

    ARTICLE

    Study of the Ballistic Impact Behavior of Protective Multi-Layer Composite Armor

    Dongsheng Jia, Yingjie Xu*, Liangdi Wang, Jihong Zhu, Weihong Zhang

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.046703

    Abstract The abalone shell, a composite material whose cross-section is composed of inorganic and organic layers, has high strength and toughness. Inspired by the abalone shell, several multi-layer composite plates with different layer sequences and thicknesses are studied as bullet-proof material in this paper. To investigate the ballistic performance of this multi-layer structure, the complete characterization model and related material parameters of large deformation, failure and fracture of Al2O3 ceramics and Carbon Fiber Reinforced Polymer (CFRP) are studied. Then, 3D finite element models of the proposed composite plates with different layer sequences and thicknesses impacted by a 12.7 mm armor-piercing incendiary… More >

  • Open Access

    ARTICLE

    A Study on the Transmission Dynamics of the Omicron Variant of COVID-19 Using Nonlinear Mathematical Models

    S. Dickson1, S. Padmasekaran1, Pushpendra Kumar2,*, Kottakkaran Sooppy Nisar3, Hamidreza Marasi4

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.030286

    Abstract This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQIcRVW and SQIRV models, considering the delay in converting susceptible individuals into infected ones. The significant delays eventually resulted in the pandemic’s containment. To ensure the safety of the host population, this concept integrates quarantine and the COVID-19 vaccine. We investigate the stability of the proposed models. The fundamental reproduction number influences stability conditions. According to our findings, asymptomatic cases considerably impact the prevalence of Omicron infection in the community. The real data of the Omicron variant from Chennai, Tamil Nadu, India, is used to validate the… More >

  • Open Access

    ARTICLE

    Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

    Nazik Alturki1, Raed Alharthi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Reemah M. Alhebshi4, Shtwai Alsubai5, Ali Kashif Bashir6,7,8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.044700

    Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as “OFF” or “ON” based… More >

  • Open Access

    ARTICLE

    Task Offloading in Edge Computing Using GNNs and DQN

    Asier Garmendia-Orbegozo1, Jose David Nunez-Gonzalez1,*, Miguel Angel Anton2

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2024.045912

    Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and appropriate to download various tasks… More > Graphic Abstract

    Task Offloading in Edge Computing Using GNNs and DQN

  • Open Access

    ARTICLE

    Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

    Nazik Alturki1, Abdulaziz Altamimi2, Muhammad Umer3,*, Oumaima Saidani1, Amal Alshardan1, Shtwai Alsubai4, Marwan Omar5, Imran Ashraf6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol., , DOI:10.32604/cmes.2023.045868

    Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) imputer is utilized to deal… More >

Displaying 11-20 on page 2 of 525. Per Page