Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (471)
  • Open Access

    ARTICLE

    Improved Convolutional Neural Network for Traffic Scene Segmentation

    Fuliang Xu, Yong Luo, Chuanlong Sun, Hong Zhao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2691-2708, 2024, DOI:10.32604/cmes.2023.030940

    Abstract In actual traffic scenarios, precise recognition of traffic participants, such as vehicles and pedestrians, is crucial for intelligent transportation. This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants. The algorithm incorporates long and short-term memory networks and the fused attention module (GSAM, GCT, and Spatial Attention Module) to enhance the algorithm’s capability to process both global and local information. Additionally, to increase the network’s initial operation stability, the original network activation function was replaced with Gaussian error linear unit. Experiments were conducted using the publicly available Cityscapes dataset.… More >

  • Open Access

    ARTICLE

    A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

    Nasser Alalwan1,*, Ahmed I. Taloba2, Amr Abozeid3, Ahmed Ibrahim Alzahrani1, Ali H. Al-Bayatti4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2497-2517, 2024, DOI:10.32604/cmes.2023.029910

    Abstract An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid method for the automatic classification… More >

  • Open Access

    ARTICLE

    Visualization for Explanation of Deep Learning-Based Fault Diagnosis Model Using Class Activation Map

    Youming Guo, Qinmu Wu*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1489-1514, 2023, DOI:10.32604/cmc.2023.042313

    Abstract Permanent magnet synchronous motor (PMSM) is widely used in various production processes because of its high efficiency, fast reaction time, and high power density. With the continuous promotion of new energy vehicles, timely detection of PMSM faults can significantly reduce the accident rate of new energy vehicles, further enhance consumers’ trust in their safety, and thus promote their popularity. Existing fault diagnosis methods based on deep learning can only distinguish different PMSM faults and cannot interpret and analyze them. Convolutional neural networks (CNN) show remarkable accuracy in image data analysis. However, due to the “black box” problem in deep learning… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

    Anwar Ullah1, Xinguo Yu1,*, Muhammad Numan2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2359-2383, 2023, DOI:10.32604/cmc.2023.041219

    Abstract Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The proposed research involves several steps.… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435

    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

    Jialun Lin1, Qiong Chen1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631

    Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    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 >

Displaying 21-30 on page 3 of 471. Per Page