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  • Open Access

    ARTICLE

    Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

    Yumin Jo1, Jongho Paik2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4153-4176, 2024, DOI:10.32604/cmc.2024.047046

    Abstract Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as… More >

  • Open Access

    ARTICLE

    DOUBLE DIFFUSION EFFECTS ON CONVECTION IN FLOW ON VERTICAL PLATE IMBEDDED IN POROUS MEDIA

    Z. Aouachriaa,*, F. Rouichia, D. Haddadb

    Frontiers in Heat and Mass Transfer, Vol.3, No.2, pp. 1-6, 2012, DOI:10.5098/hmt.v3.2.3004

    Abstract Natural convection flow past a vertical porous plate in a porous medium is studied numerically, by taking into account the Dufour and Soret effects. The similarity equations of the problem considered are obtained by using usual similarity technique. This system of ordinary differential equations, which are solved numerically by using the Nachtsheim -Swigerst hooting iteration technique together with a sixth order Runge-Kutta integrations scheme. The results show that Soret and Dufour effects do not appreciably influence the velocity, temperature and concentration fields, but rather only tend to increase the mass and energy flux due to the added contributions. More >

  • Open Access

    ARTICLE

    Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

    Xin Fan1,2, Shuqing Zhang1,2,*, Kaisheng Wu1,2, Wei Zheng1,2, Yu Ge1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

    Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

  • Open Access

    ARTICLE

    Maximizing Influence in Temporal Social Networks: A Node Feature-Aware Voting Algorithm

    Wenlong Zhu1,2,*, Yu Miao1, Shuangshuang Yang3, Zuozheng Lian1,2, Lianhe Cui1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646

    Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting… More >

  • Open Access

    ARTICLE

    MHD CONVECTIVE BOUNDARY LAYER FLOW TOWARDS A VERTICAL SURFACE IN A POROUS MEDIUM WITH RADIATION, CHEMICAL REACTION AND INTERNAL HEAT GENERATION

    Emmanuel Maurice Arthur*, Timothy Ayando, Yakubu Ibrahim Seini

    Frontiers in Heat and Mass Transfer, Vol.6, pp. 1-10, 2015, DOI:10.5098/hmt.6.21

    Abstract The combined effects of chemical reaction and viscous dissipation on hydromagnetic mixed convective flow towards a vertical plate embedded in a highly porous medium with radiation and internal heat generation has been examined. The governing boundary layer equations have been transformed to a two-point boundary value problem using a local similarity approach and solved numerically using the Newton Raphson shooting method alongside the Fourth-order Runge - Kutta algorithm. The effects of various embedded parameters on fluid velocity, temperature and concentration have been presented graphically whilst the skin friction coefficient and the rates of heat and mass transfers have been tabulated… More >

  • Open Access

    ARTICLE

    MHD MIXED CONVECTION STAGNATION POINT FLOW TOWARDS A STRETCHING SHEET IN THE PRESENCE OF DUFOUR EFFECT, RADIATION EFFECT AND WITH VARIABLE FLUID VISCOSITY

    Vandana Bisht*

    Frontiers in Heat and Mass Transfer, Vol.7, pp. 1-7, 2016, DOI:10.5098/hmt.7.19

    Abstract In this paper the steady laminar magneto hydrodynamic (MHD) mixed convection boundary layer flow towards a vertical stretching sheet with variable fluid viscosity, radiation and in the presence of Dufour’s effect have been investigated. The governing partial differential equations are transformed into set of ordinary differential equations using similarity transformation, and then these equations have been solved numerically using Runge- Kutta method with shooting technique. Results shows that magnitude of skin friction coefficient decreases, while magnitude of heat transfer coefficient and mass transfer coefficient increases with decreasing values of viscosity variation parameter for the case of opposing flow. But in… More >

  • Open Access

    ARTICLE

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

    Xinzheng Wang1,2,*, Bing Guo1, Yan Shen3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1183-1206, 2024, DOI:10.32604/cmes.2023.029552

    Abstract Predicting students’ academic achievements is an essential issue in education, which can benefit many stakeholders, for instance, students, teachers, managers, etc. Compared with online courses such as MOOCs, students’ academic-related data in the face-to-face physical teaching environment is usually sparsity, and the sample size is relatively small. It makes building models to predict students’ performance accurately in such an environment even more challenging. This paper proposes a Two-Way Neural Network (TWNN) model based on the bidirectional recurrent neural network and graph neural network to predict students’ next semester’s course performance using only their previous course achievements. Extensive experiments on a… More > Graphic Abstract

    Two-Way Neural Network Performance Prediction Model Based on Knowledge Evolution and Individual Similarity

  • Open Access

    ARTICLE

    An Efficient Character-Level Adversarial Attack Inspired by Textual Variations in Online Social Media Platforms

    Jebran Khan1, Kashif Ahmad2, Kyung-Ah Sohn1,3,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2869-2894, 2023, DOI:10.32604/csse.2023.040159

    Abstract In recent years, the growing popularity of social media platforms has led to several interesting natural language processing (NLP) applications. However, these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning (ML) and NLP techniques. This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication. These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form. The intuition of the proposed scheme is to generate adversarial examples… More >

  • Open Access

    ARTICLE

    An Enhanced Automatic Arabic Essay Scoring System Based on Machine Learning Algorithms

    Nourmeen Lotfy1, Abdulaziz Shehab1,2,*, Mohammed Elhoseny1,3, Ahmed Abu-Elfetouh1

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1227-1249, 2023, DOI:10.32604/cmc.2023.039185

    Abstract Despite the extensive effort to improve intelligent educational tools for smart learning environments, automatic Arabic essay scoring remains a big research challenge. The nature of the writing style of the Arabic language makes the problem even more complicated. This study designs, implements, and evaluates an automatic Arabic essay scoring system. The proposed system starts with pre-processing the student answer and model answer dataset using data cleaning and natural language processing tasks. Then, it comprises two main components: the grading engine and the adaptive fusion engine. The grading engine employs string-based and corpus-based similarity algorithms separately. After that, the adaptive fusion… More >

  • Open Access

    ARTICLE

    Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

    Yang Yang1, Yuhan Long1, Yijing Lin2, Zhipeng Gao1, Lanlan Rui1, Peng Yu1,3,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3623-3651, 2023, DOI:10.32604/cmc.2023.040250

    Abstract With the rapid development of the Internet of Things (IoT), the automation of edge-side equipment has emerged as a significant trend. The existing fault diagnosis methods have the characteristics of heavy computing and storage load, and most of them have computational redundancy, which is not suitable for deployment on edge devices with limited resources and capabilities. This paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge distillation. First, we offer a clustering-based self-knowledge distillation approach (Cluster KD), which takes the mean value of the sample diagnosis results, clusters them, and takes the clustering results as the… More >

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