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  • Open Access


    Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

    Jinxi Guo1, Kai Chen1,2, Jiehui Liu1, Yuhao Ma2, Jie Wu2,*, Yaochun Wu2, Xiaofeng Xue3, Jianshen Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2619-2640, 2024, DOI:10.32604/cmes.2023.031360

    Abstract Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation of equipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasing attention and achieved some results. It might lead to insufficient performance for using transfer learning alone and cause misclassification of target samples for domain bias when building deep models to learn domain-invariant features. To address the above problems, a deep discriminative adversarial domain adaptation neural network for the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstly converted into frequency domain… More >

  • Open Access


    Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

    Heba M. El-Hoseny1,*, Heba F. Elsepae2, Wael A. Mohamed2, Ayman S. Selmy2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107

    Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes of diabetes and verify the… More >

  • Open Access


    Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

    Jiangxia Han1,2, Liang Xue1,2,*, Ying Jia3, Mpoki Sam Mwasamwasa1,2, Felix Nanguka4, Charles Sangweni5, Hailong Liu3, Qian Li3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1323-1340, 2024, DOI:10.32604/cmes.2023.031093

    Abstract Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physics-informed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different physical properties. In this work,… More >

  • Open Access


    Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

    Sarah M. Alhammad1, Doaa Sami Khafaga1,*, Aya Y. Hamed2, Osama El-Koumy3, Ehab R. Mohamed3, Khalid M. Hosny3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2939-2952, 2023, DOI:10.32604/csse.2023.041011

    Abstract Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption… More >

  • Open Access


    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz1, Ki-Chul Kwon1, F. M. Fahmid Hossain1, Md. Biddut Hossain1, Rupali Kiran Shinde1, Sang-Keun Gil2, Nam Kim1,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205

    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field microscopy are aligned to form… More >

  • Open Access


    A Graph Neural Network Recommendation Based on Long- and Short-Term Preference

    Bohuai Xiao1,2, Xiaolan Xie1,2,*, Chengyong Yang3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3067-3082, 2023, DOI:10.32604/csse.2023.034712

    Abstract The recommendation system (RS) on the strength of Graph Neural Networks (GNN) perceives a user-item interaction graph after collecting all items the user has interacted with. Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession. However, user preferences are dynamic. With the passage of time and some trend guidance, users may generate some short-term preferences, which are more likely to lead to user-item interactions. A GNN recommendation based on long- and short-term preference (LSGNN) is proposed to address the above problems. LSGNN consists of four modules, using a GNN… More >

  • Open Access


    Soil NOx Emission Prediction via Recurrent Neural Networks

    Zhaoan Wang1, Shaoping Xiao1,*, Cheryl Reuben2, Qiyu Wang2, Jun Wang2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 285-297, 2023, DOI:10.32604/cmc.2023.044366

    Abstract This paper presents designing sequence-to-sequence recurrent neural network (RNN) architectures for a novel study to predict soil NOx emissions, driven by the imperative of understanding and mitigating environmental impact. The study utilizes data collected by the Environmental Protection Agency (EPA) to develop two distinct RNN predictive models: one built upon the long-short term memory (LSTM) and the other utilizing the gated recurrent unit (GRU). These models are fed with a combination of historical and anticipated air temperature, air moisture, and NOx emissions as inputs to forecast future NOx emissions. Both LSTM and GRU models can capture the intricate pulse patterns… More >

  • Open Access


    Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

    Yong Li1,4, Qiming Liang2,*, Bo Gan3, Xiaolong Cui4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494

    Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the… More >

  • Open Access


    Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm

    P. Kalaiselvi1,*, S. Anusuya2

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1209-1226, 2023, DOI:10.32604/cmc.2023.040264

    Abstract In recent days, Deep Learning (DL) techniques have become an emerging transformation in the field of machine learning, artificial intelligence, computer vision, and so on. Subsequently, researchers and industries have been highly endorsed in the medical field, predicting and controlling diverse diseases at specific intervals. Liver tumor prediction is a vital chore in analyzing and treating liver diseases. This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks (CNN) and a depth-based variant search algorithm with advanced attention mechanisms (CNN-DS-AM). The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases. The… More >

  • Open Access


    A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

    Muhammad Naeem Akram1, Muhammad Usman Yaseen1, Muhammad Waqar1, Muhammad Imran1,*, Aftab Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855

    Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

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