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  • Open Access

    ARTICLE

    Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

    Mahmood A. Mahmood1,2,*, Khalaf Alsalem1

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3431-3448, 2024, DOI:10.32604/cmc.2024.047604

    Abstract Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses. Early detection of these diseases is essential for effective management. We propose a novel transformed wavelet, feature-fused, pre-trained deep learning model for detecting olive leaf diseases. The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images. The model has four main phases: preprocessing using data augmentation, three-level wavelet transformation, learning using pre-trained deep learning models, and a fused deep learning model. In the preprocessing phase, the image dataset is augmented using techniques such as… More >

  • Open Access

    ARTICLE

    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access

    ARTICLE

    Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

    Lifang Fu1, Huanxin Peng2,*, Changjin Ma2, Yuhan Liu2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4399-4416, 2024, DOI:10.32604/cmc.2024.047053

    Abstract In recent years, how to efficiently and accurately identify multi-model fake news has become more challenging. First, multi-model data provides more evidence but not all are equally important. Secondly, social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical. Unfortunately, existing approaches fail to handle these problems. This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues (TD-MMC), which utilizes three valuable multi-model clues: text-model importance, text-image complementary, and text-image inconsistency. TD-MMC is dominated by textural content and… More >

  • Open Access

    ARTICLE

    Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

    Zihao Song, Yan Zhou*, Wei Cheng, Futai Liang, Chenhao Zhang

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3349-3376, 2024, DOI:10.32604/cmc.2024.047034

    Abstract The frequent missing values in radar-derived time-series tracks of aerial targets (RTT-AT) lead to significant challenges in subsequent data-driven tasks. However, the majority of imputation research focuses on random missing (RM) that differs significantly from common missing patterns of RTT-AT. The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation. Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss. In this paper, a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed. Our model consists of two… More >

  • Open Access

    ARTICLE

    Cross-Diffusion Effects on an MHD Williamson Nanofluid Flow Past a Nonlinear Stretching Sheet Immersed in a Permeable Medium

    R. Madan Kumar1, R. Srinivasa Raju2, F. Mebarek-Oudina3,*, M. Anil Kumar4, V. K. Narla2

    Frontiers in Heat and Mass Transfer, Vol.22, No.1, pp. 15-34, 2024, DOI:10.32604/fhmt.2024.048045

    Abstract The primary aim of this research endeavor is to examine the characteristics of magnetohydrodynamic Williamson nanofluid flow past a nonlinear stretching surface that is immersed in a permeable medium. In the current analysis, the impacts of Soret and Dufour (cross-diffusion effects) have been attentively taken into consideration. Using appropriate similarity variable transformations, the governing nonlinear partial differential equations were altered into nonlinear ordinary differential equations and then solved numerically using the Runge Kutta Fehlberg-45 method along with the shooting technique. Numerical simulations were then perceived to show the consequence of various physical parameters on the plots of velocity, temperature, and… More > Graphic Abstract

    Cross-Diffusion Effects on an MHD Williamson Nanofluid Flow Past a Nonlinear Stretching Sheet Immersed in a Permeable Medium

  • Open Access

    ARTICLE

    ADVANCES IN THERMODIFFUSION AND THERMOPHORESIS (SORET EFFECT) IN LIQUID MIXTURES

    Morteza Eslamian*

    Frontiers in Heat and Mass Transfer, Vol.2, No.4, pp. 1-20, 2011, DOI:10.5098/hmt.v2.4.3001

    Abstract Recent advances in thermodiffusion (Soret effect) in binary and higher multicomponent liquid mixtures are reviewed. The mixtures studied include the hydrocarbon, associating, molten metal and semiconductor, polymer, and DNA mixtures. The emphasis is placed on the theoretical works, particularly models based on the nonequilibrium thermodynamics, although other approaches such as the statistical, kinetic and hydrodynamic approaches are discussed as well. For each mixture, the major theoretical and experimental works are discussed and the research trends and challenges are addressed. Some of the challenges include a need for combining various methods to develop a comprehensive theoretical model or at least to… More >

  • Open Access

    ARTICLE

    NUMERICAL INVESTIGATION OF HEAT TRANSPORT IN A DIRECT METHANOL FUEL CELL WITH ANISOTROPIC GAS DIFFUSION LAYERS

    Zheng Miaoa, Ya-Ling Hea,*, Tian-Shou Zhaob, Wen-Quan Taoa

    Frontiers in Heat and Mass Transfer, Vol.2, No.1, pp. 1-10, 2011, DOI:10.5098/hmt.v2.1.3001

    Abstract A non-isothermal two-phase mass transport model is developed in this paper to investigate the heat generation and transport phenomena in a direct methanol fuel cell with anisotropic gas diffusion layers (GDLs). Thermal contact resistances at the GDL/CL (catalyst layer) and GDL/Rib interfaces, and the deformation of GDLs are considered together with the inherent anisotropy of the GDL. Latent heat effects due to condensation/evaporation of water and methanol between liquid and gas phases are also taken into account. Formulation of the two-phase mass transport across the membrane electrode assembly (MEA) is mainly based on the classical multiphase flow theory in the… More >

  • Open Access

    ARTICLE

    An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

    Irfan Haider1, Muhammad Attique Khan1,*, Muhammad Nazir1, Taerang Kim2, Jae-Hyuk Cha2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

    Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been employed in this work for… 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-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

    Rui Wang1, Yao Zhou3,*, Guangchun Luo1, Peng Chen2, Dezhong Peng3,4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3011-3027, 2024, DOI:10.32604/cmes.2023.047065

    Abstract Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data. Due to the challenges associated with annotating anomaly events, time series reconstruction has become a prevalent approach for unsupervised anomaly detection. However, effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series. In this paper, we propose a cross-dimension attentive feature fusion network for time series anomaly detection, referred to as CAFFN. Specifically, a series and feature mixing block is introduced to learn representations in 1D space. Additionally, a… More >

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