Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (1,164)
  • Open Access

    ARTICLE

    A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

    Ghulam Gilanie1,*, Sana Cheema1, Akkasha Latif1, Anum Saher1, Muhammad Ahsan1, Hafeez Ullah2, Diya Oommen3

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 57-71, 2024, DOI:10.32604/iasc.2024.041535

    Abstract Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient. It affects a large percentage of people globally, who fluctuate between depression and mania, or vice versa. A pleasant or unpleasant mood is more than a reflection of a state of mind. Normally, it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio, so automated procedures are the best options to diagnose and verify the severity of bipolar. In this research work, facial micro-expressions have been used for bipolar detection using… More >

  • Open Access

    ARTICLE

    Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

    Yongcan Zhu1,2, Jiawen Wang1, Ye Zhang1,2, Long Zhao1, Botao Jiang1, Xinbo Huang1,*

    Energy Engineering, Vol.121, No.4, pp. 895-911, 2024, DOI:10.32604/ee.2023.041002

    Abstract The accumulation of snow and ice on PV modules can have a detrimental impact on power generation, leading to reduced efficiency for prolonged periods. Thus, it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules. To address this issue, the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images. Furthermore, the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules, allowing for the establishment of a residual ice and… More >

  • Open Access

    ARTICLE

    DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

    Anas Bilal1, Azhar Imran2, Talha Imtiaz Baig3,4, Xiaowen Liu1,*, Haixia Long1, Abdulkareem Alzahrani5, Muhammad Shafiq6

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

    Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

  • Open Access

    ARTICLE

    Transparent and Accurate COVID-19 Diagnosis: Integrating Explainable AI with Advanced Deep Learning in CT Imaging

    Mohammad Mehedi Hassan1,*, Salman A. AlQahtani2, Mabrook S. AlRakhami1, Ahmed Zohier Elhendi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3101-3123, 2024, DOI:10.32604/cmes.2024.047940

    Abstract In the current landscape of the COVID-19 pandemic, the utilization of deep learning in medical imaging, especially in chest computed tomography (CT) scan analysis for virus detection, has become increasingly significant. Despite its potential, deep learning’s “black box” nature has been a major impediment to its broader acceptance in clinical environments, where transparency in decision-making is imperative. To bridge this gap, our research integrates Explainable AI (XAI) techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) method, with advanced deep learning models. This integration forms a sophisticated and transparent framework for COVID-19 identification, enhancing the capability of standard Convolutional Neural Network… More >

  • Open Access

    ARTICLE

    IR-YOLO: Real-Time Infrared Vehicle and Pedestrian Detection

    Xiao Luo1,3, Hao Zhu1,2,*, Zhenli Zhang1,2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2667-2687, 2024, DOI:10.32604/cmc.2024.047988

    Abstract Road traffic safety can decrease when drivers drive in a low-visibility environment. The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents. To tackle the challenges posed by the low recognition accuracy and the substantial computational burden associated with current infrared pedestrian-vehicle detection methods, an infrared pedestrian-vehicle detection method A proposal is presented, based on an enhanced version of You Only Look Once version 5 (YOLOv5). First, A head specifically designed for detecting small targets has been integrated into the model to make full… More >

  • Open Access

    ARTICLE

    Enhancing Image Description Generation through Deep Reinforcement Learning: Fusing Multiple Visual Features and Reward Mechanisms

    Yan Li, Qiyuan Wang*, Kaidi Jia

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2469-2489, 2024, DOI:10.32604/cmc.2024.047822

    Abstract Image description task is the intersection of computer vision and natural language processing, and it has important prospects, including helping computers understand images and obtaining information for the visually impaired. This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images. Our method focuses on refining the reward function in deep reinforcement learning, facilitating the generation of precise descriptions by aligning visual and textual features more closely. Our approach comprises three key architectures. Firstly, it utilizes Residual Network 101 (ResNet-101) and Faster Region-based Convolutional Neural Network (Faster R-CNN) to extract average… More >

  • Open Access

    REVIEW

    A Review of the Application of Artificial Intelligence in Orthopedic Diseases

    Xinlong Diao, Xiao Wang*, Junkang Qin, Qinmu Wu, Zhiqin He, Xinghong Fan

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2617-2665, 2024, DOI:10.32604/cmc.2024.047377

    Abstract In recent years, Artificial Intelligence (AI) has revolutionized people’s lives. AI has long made breakthrough progress in the field of surgery. However, the research on the application of AI in orthopedics is still in the exploratory stage. The paper first introduces the background of AI and orthopedic diseases, addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases, draws out the advantages of deep learning and machine learning in image detection, and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years, describing the contributions, strengths and weaknesses,… More >

  • Open Access

    ARTICLE

    Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

    Zhiqing Dong1, Zhao Zhang1,*, Hongyan Zhou2, Xuebo Chen2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1977-1993, 2024, DOI:10.32604/cmc.2024.047233

    Abstract With the advent of the information security era, it is necessary to guarantee the privacy, accuracy, and dependable transfer of pictures. This study presents a new approach to the encryption and compression of color images. It is predicated on 2D compressed sensing (CS) and the hyperchaotic system. First, an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security. Then, the processed images are concurrently encrypted and compressed using 2D CS. Among them, chaotic sequences replace traditional random measurement matrices to increase the system’s security. Third, the processed images are re-encrypted using a combination of… More >

  • Open Access

    ARTICLE

    Learning Epipolar Line Window Attention for Stereo Image Super-Resolution Reconstruction

    Xue Li, Hongying Zhang*, Zixun Ye, Xiaoru Huang

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2847-2864, 2024, DOI:10.32604/cmc.2024.047093

    Abstract Transformer-based stereo image super-resolution reconstruction (Stereo SR) methods have significantly improved image quality. However, existing methods have deficiencies in paying attention to detailed features and do not consider the offset of pixels along the epipolar lines in complementary views when integrating stereo information. To address these challenges, this paper introduces a novel epipolar line window attention stereo image super-resolution network (EWASSR). For detail feature restoration, we design a feature extractor based on Transformer and convolutional neural network (CNN), which consists of (shifted) window-based self-attention ((S)W-MSA) and feature distillation and enhancement blocks (FDEB). This combination effectively solves the problem of global… More >

  • Open Access

    ARTICLE

    DGConv: A Novel Convolutional Neural Network Approach for Weld Seam Depth Image Detection

    Pengchao Li1,2,3,*, Fang Xu1,2,3,4, Jintao Wang1,2, Haibing Guo4, Mingmin Liu4, Zhenjun Du4

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1755-1771, 2024, DOI:10.32604/cmc.2023.047057

    Abstract We propose a novel image segmentation algorithm to tackle the challenge of limited recognition and segmentation performance in identifying welding seam images during robotic intelligent operations. Initially, to enhance the capability of deep neural networks in extracting geometric attributes from depth images, we developed a novel deep geometric convolution operator (DGConv). DGConv is utilized to construct a deep local geometric feature extraction module, facilitating a more comprehensive exploration of the intrinsic geometric information within depth images. Secondly, we integrate the newly proposed deep geometric feature module with the Fully Convolutional Network (FCN8) to establish a high-performance deep neural network algorithm… More >

Displaying 11-20 on page 2 of 1164. Per Page