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  • Open Access

    ARTICLE

    Enhancing Skin Cancer Diagnosis with Deep Learning: A Hybrid CNN-RNN Approach

    Syeda Shamaila Zareen1,*, Guangmin Sun1,*, Mahwish Kundi2, Syed Furqan Qadri3, Salman Qadri4

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418

    Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model achieves a high average recognition… More >

  • Open Access

    ARTICLE

    Coal/Gangue Volume Estimation with Convolutional Neural Network and Separation Based on Predicted Volume and Weight

    Zenglun Guan1,2, Murad S. Alfarzaeai1,3,*, Eryi Hu1,3,*, Taqiaden Alshmeri4, Wang Peng3

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 279-306, 2024, DOI:10.32604/cmc.2024.047159

    Abstract In the coal mining industry, the gangue separation phase imposes a key challenge due to the high visual similarity between coal and gangue. Recently, separation methods have become more intelligent and efficient, using new technologies and applying different features for recognition. One such method exploits the difference in substance density, leading to excellent coal/gangue recognition. Therefore, this study uses density differences to distinguish coal from gangue by performing volume prediction on the samples. Our training samples maintain a record of 3-side images as input, volume, and weight as the ground truth for the classification. The prediction process relies on a… More >

  • Open Access

    ARTICLE

    Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

    Yuejiao Wang, Zhong Ma*, Chaojie Yang, Yu Yang, Lu Wei

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 819-836, 2024, DOI:10.32604/cmc.2024.047108

    Abstract The quantization algorithm compresses the original network by reducing the numerical bit width of the model, which improves the computation speed. Because different layers have different redundancy and sensitivity to data bit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determine the optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantization can effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In this paper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bit width is proposed,… More >

  • Open Access

    ARTICLE

    Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

    Haojie Lian1, Jiaqi Wang1, Leilei Chen2,*, Shengze Li3, Ruochen Cao4, Qingyuan Hu5, Peiyun Zhao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549

    Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations… More >

  • Open Access

    ARTICLE

    DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

    Chengzhang Zhu1,2, Renmao Zhang1, Yalong Xiao1,2,*, Beiji Zou1, Xian Chai1, Zhangzheng Yang1, Rong Hu3, Xuanchu Duan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453

    Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer. To address… More >

  • Open Access

    ARTICLE

    Deep Learning Social Network Access Control Model Based on User Preferences

    Fangfang Shan1,2,*, Fuyang Li1, Zhenyu Wang1, Peiyu Ji1, Mengyi Wang1, Huifang Sun1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1029-1044, 2024, DOI:10.32604/cmes.2024.047665

    Abstract A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes… More >

  • Open Access

    REVIEW

    A Survey on Chinese Sign Language Recognition: From Traditional Methods to Artificial Intelligence

    Xianwei Jiang1, Yanqiong Zhang1,*, Juan Lei1, Yudong Zhang2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1-40, 2024, DOI:10.32604/cmes.2024.047649

    Abstract Research on Chinese Sign Language (CSL) provides convenience and support for individuals with hearing impairments to communicate and integrate into society. This article reviews the relevant literature on Chinese Sign Language Recognition (CSLR) in the past 20 years. Hidden Markov Models (HMM), Support Vector Machines (SVM), and Dynamic Time Warping (DTW) were found to be the most commonly employed technologies among traditional identification methods. Benefiting from the rapid development of computer vision and artificial intelligence technology, Convolutional Neural Networks (CNN), 3D-CNN, YOLO, Capsule Network (CapsNet) and various deep neural networks have sprung up. Deep Neural Networks (DNNs) and their derived… More >

  • Open Access

    ARTICLE

    Identifying Brand Consistency by Product Differentiation Using CNN

    Hung-Hsiang Wang1, Chih-Ping Chen2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 685-709, 2024, DOI:10.32604/cmes.2024.047630

    Abstract This paper presents a new method of using a convolutional neural network (CNN) in machine learning to identify brand consistency by product appearance variation. In Experiment 1, we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions. Results show that it is a challenge to distinguish periods for the subtle evolution of the mouse devices with such traditional methods as time series analysis and principal component analysis (PCA). In Experiment 2, we applied deep learning to predict the extent… More >

  • Open Access

    ARTICLE

    Tensile Failure Characterization of Glass/Epoxy Composites using Acoustic Emission RMS Data

    K. KRISHNAMOORTHYa,*, N. PRABHUb

    Journal of Polymer Materials, Vol.40, No.3-4, pp. 215-226, 2023, DOI:10.32381/JPM.2023.40.3-4.7

    Abstract The acoustic emission monitoring with artificial neural networks predicts the ultimate strength of glass/epoxy composite laminates using Acoustic Emission Data. The ultimate loads of all the specimens were used to characterise the emission of hits during failure modes. The six layered glass fiber laminates were prepared (in woven mat form) with epoxy as the binding medium by hand lay-up technique. At room temperature, with a pressure of 30 kg/cm2, the laminates were cured. The laminates of standard dimensions as per ASTM D3039 for the tensile test were cut from the lamina. The Acoustic Emission (AE) test was conducted on these… More >

  • Open Access

    ARTICLE

    Aspect-Level Sentiment Analysis Based on Deep Learning

    Mengqi Zhang1, Jiazhao Chai2, Jianxiang Cao3, Jialing Ji3, Tong Yi4,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486

    Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them into representative user feature vectors.… More >

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