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Search Results (113)
  • Open Access

    ARTICLE

    Numerical Stability and Accuracy of Contact Angle Schemes in Pseudopotential Lattice Boltzmann Model for Simulating Static Wetting and Dynamic Wetting

    Dongmin Wang1,2,*, Gaoshuai Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 299-318, 2023, DOI:10.32604/cmes.2023.027280

    Abstract There are five most widely used contact angle schemes in the pseudopotential lattice Boltzmann (LB) model for simulating the wetting phenomenon: The pseudopotential-based scheme (PB scheme), the improved virtual-density scheme (IVD scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the fluid layer density above the wall (MPB-C scheme), the modified pseudopotential-based scheme with a ghost fluid layer constructed by using the weighted average density of surrounding fluid nodes (MPB-W scheme) and the geometric formulation scheme (GF scheme). But the numerical stability and accuracy of the schemes for wetting simulation remain unclear in the past. In… More >

  • Open Access

    ARTICLE

    Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

    S. Vijayashaarathi1,*, S. NithyaKalyani2

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2881-2897, 2023, DOI:10.32604/csse.2023.036864

    Abstract In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms… More >

  • Open Access

    ARTICLE

    A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

    Zerui Zhen1, Zihao Wu2, Lei Feng1,*, Wenjing Li1, Feng Qi1, Shixuan Guo1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2939-2955, 2023, DOI:10.32604/cmc.2023.036505

    Abstract Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent… More >

  • Open Access

    ARTICLE

    Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

    Salah-ud-din Khokhar1,2,*, QinKe Peng1, Muhammad Yasir Noor3

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2413-2427, 2023, DOI:10.32604/cmc.2023.036148

    Abstract For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative… More >

  • Open Access

    ARTICLE

    Floyd-Warshall Algorithm Based on Picture Fuzzy Information

    Shaista Habib1, Aqsa Majeed1, Muhammad Akram2,*, Mohammed M. Ali Al-Shamiri3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2873-2894, 2023, DOI:10.32604/cmes.2023.026294

    Abstract The Floyd-Warshall algorithm is frequently used to determine the shortest path between any pair of nodes. It works well for crisp weights, but the problem arises when weights are vague and uncertain. Let us take an example of computer networks, where the chosen path might no longer be appropriate due to rapid changes in network conditions. The optimal path from among all possible courses is chosen in computer networks based on a variety of parameters. In this paper, we design a new variant of the Floyd-Warshall algorithm that identifies an All-Pair Shortest Path (APSP) in an uncertain situation of a… More > Graphic Abstract

    Floyd-Warshall Algorithm Based on Picture Fuzzy Information

  • Open Access

    ARTICLE

    Data Analytics on Unpredictable Pregnancy Data Records Using Ensemble Neuro-Fuzzy Techniques

    C. Vairavel1,*, N. S. Nithya2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2159-2175, 2023, DOI:10.32604/csse.2023.036598

    Abstract The immune system goes through a profound transformation during pregnancy, and certain unexpected maternal complications have been correlated to this transition. The ability to correctly examine, diagnoses, and predict pregnancy-hastened diseases via the available big data is a delicate problem since the range of information continuously increases and is scalable. Many approaches for disease diagnosis/classification have been established with the use of data mining concepts. However, such methods do not provide an appropriate classification/diagnosis model. Furthermore, single learning approaches are used to create the bulk of these systems. Classification issues may be made more accurate by combining predictions from many… More >

  • Open Access

    ARTICLE

    A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

    Saurabh Adhikari1, Tushar Kanti Gangopadhayay1, Souvik Pal2,3, D. Akila4, Mamoona Humayun5, Majed Alfayad6, N. Z. Jhanjhi7,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2123-2140, 2023, DOI:10.32604/csse.2023.034431

    Abstract Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to… More >

  • Open Access

    ARTICLE

    Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

    V. P. Nithya*, N. Mohanasundaram, R. Santhosh

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2601-2618, 2023, DOI:10.32604/csse.2023.030503

    Abstract The prediction of mild cognitive impairment or Alzheimer’s disease (AD) has gained the attention of huge researchers as the disease occurrence is increasing, and there is a need for earlier prediction. Regrettably, due to the high-dimensionality nature of neural data and the least available samples, modelling an efficient computer diagnostic system is highly solicited. Learning approaches, specifically deep learning approaches, are essential in disease prediction. Deep Learning (DL) approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging. A novel 3D-Convolutional Neural Network (3D-CNN) architecture is proposed to predict AD with Magnetic resonance imaging (MRI) data.… More >

  • Open Access

    ARTICLE

    Deep Learning Model Ensemble for the Accuracy of Classification Degenerative Arthritis

    Sang-min Lee*, Namgi Kim

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1981-1994, 2023, DOI:10.32604/cmc.2023.035245

    Abstract Artificial intelligence technologies are being studied to provide scientific evidence in the medical field and developed for use as diagnostic tools. This study focused on deep learning models to classify degenerative arthritis into Kellgren–Lawrence grades. Specifically, degenerative arthritis was assessed by X-ray radiographic images and classified into five classes. Subsequently, the use of various deep learning models was investigated for automating the degenerative arthritis classification process. Although research on the classification of osteoarthritis using deep learning has been conducted in previous studies, only local models have been used, and an ensemble of deep learning models has never been applied to… More >

  • Open Access

    ARTICLE

    Efficient Crack Severity Level Classification Using Bilayer Detection for Building Structures

    M. J. Anitha1,*, R. Hemalatha2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 1183-1200, 2023, DOI:10.32604/csse.2023.031888

    Abstract Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures. Moreover, identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings. Hence, this paper proposes an efficient strategy to classify the cracks into fine, medium, and thick using a novel bilayer crack detection algorithm. The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification. The proposed algorithm works well in the dark background and connects the discontinued cracks… More >

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