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  • Open Access

    ARTICLE

    Residual Feature Attentional Fusion Network for Lightweight Chest CT Image Super-Resolution

    Kun Yang1,2, Lei Zhao1, Xianghui Wang1, Mingyang Zhang1, Linyan Xue1,2, Shuang Liu1,2, Kun Liu1,2,3,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401

    Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion… More >

  • Open Access

    ARTICLE

    SA-Model: Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

    Lingli Zhang1, Yadong Wu1,*, Qikai Chu2, Pan Li2, Guijuan Wang3,4, Weihan Zhang1, Yu Qiu1, Yi Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 631-645, 2023, DOI:10.32604/cmes.2023.027179

    Abstract Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing, ancient literature research, etc. However, the existing research on sentiment analysis is relatively small. It does not effectively solve the problems such as the weak feature extraction ability of poetry text, which leads to the low performance of the model on sentiment analysis for Chinese classical poetry. In this research, we offer the SA-Model, a poetic sentiment analysis model. SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension (BERT-wwm-ext) and Enhanced representation through knowledge integration (ERNIE)… More >

  • Open Access

    ARTICLE

    An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

    Arfat Ahmad Khan1, Rashid Jahangir2,*, Roobaea Alroobaea3, Saleh Yahya Alyahyan4, Ahmed H. Almulhi3, Majed Alsafyani3, Chitapong Wechtaisong5

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4085-4100, 2023, DOI:10.32604/cmc.2023.036797

    Abstract Automatic Speaker Identification (ASI) involves the process of distinguishing an audio stream associated with numerous speakers’ utterances. Some common aspects, such as the framework difference, overlapping of different sound events, and the presence of various sound sources during recording, make the ASI task much more complicated and complex. This research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation resources. In this research, the performance of the transformer model is investigated. Seven audio features, chromagram, Mel-spectrogram, tonnetz, Mel-Frequency Cepstral Coefficients (MFCCs), delta MFCCs, delta-delta MFCCs and spectral… More >

  • Open Access

    ARTICLE

    SepFE: Separable Fusion Enhanced Network for Retinal Vessel Segmentation

    Yun Wu1, Ge Jiao1,2,*, Jiahao Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2465-2485, 2023, DOI:10.32604/cmes.2023.026189

    Abstract The accurate and automatic segmentation of retinal vessels from fundus images is critical for the early diagnosis and prevention of many eye diseases, such as diabetic retinopathy (DR). Existing retinal vessel segmentation approaches based on convolutional neural networks (CNNs) have achieved remarkable effectiveness. Here, we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net, which is one of the most popular architectures. In view of the excellent work of depth-wise separable convolution, we introduce it to replace the standard convolutional layer. The complexity of the proposed model is reduced by decreasing the number of… More >

  • Open Access

    ARTICLE

    RT-YOLO: A Residual Feature Fusion Triple Attention Network for Aerial Image Target Detection

    Pan Zhang, Hongwei Deng*, Zhong Chen

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1411-1430, 2023, DOI:10.32604/cmc.2023.034876

    Abstract In recent years, target detection of aerial images of unmanned aerial vehicle (UAV) has become one of the hottest topics. However, target detection of UAV aerial images often presents false detection and missed detection. We proposed a modified you only look once (YOLO) model to improve the problems arising in object detection in UAV aerial images: (1) A new residual structure is designed to improve the ability to extract features by enhancing the fusion of the inner features of the single layer. At the same time, triplet attention module is added to strengthen the connection between space and channel and… More >

  • Open Access

    ARTICLE

    One-Class Arabic Signature Verification: A Progressive Fusion of Optimal Features

    Ansam A. Abdulhussien1,2,*, Mohammad F. Nasrudin1, Saad M. Darwish3, Zaid A. Alyasseri1

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 219-242, 2023, DOI:10.32604/cmc.2023.033331

    Abstract Signature verification is regarded as the most beneficial behavioral characteristic-based biometric feature in security and fraud protection. It is also a popular biometric authentication technology in forensic and commercial transactions due to its various advantages, including noninvasiveness, user-friendliness, and social and legal acceptability. According to the literature, extensive research has been conducted on signature verification systems in a variety of languages, including English, Hindi, Bangla, and Chinese. However, the Arabic Offline Signature Verification (OSV) system is still a challenging issue that has not been investigated as much by researchers due to the Arabic script being distinguished by changing letter shapes,… More >

  • Open Access

    ARTICLE

    DuFNet: Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

    Tao Duan1, Yue Liu1, Jingze Li1, Zhichao Lian2,*, Qianmu Li2

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 223-239, 2023, DOI:10.32604/cmes.2023.024742

    Abstract The application of unmanned driving in the Internet of Things is one of the concrete manifestations of the application of artificial intelligence technology. Image semantic segmentation can help the unmanned driving system by achieving road accessibility analysis. Semantic segmentation is also a challenging technology for image understanding and scene parsing. We focused on the challenging task of real-time semantic segmentation in this paper. In this paper, we proposed a novel fast architecture for real-time semantic segmentation named DuFNet. Starting from the existing work of Bilateral Segmentation Network (BiSeNet), DuFNet proposes a novel Semantic Information Flow (SIF) structure for context information… More > Graphic Abstract

    DuFNet: Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things

  • Open Access

    ARTICLE

    Monocular Depth Estimation with Sharp Boundary

    Xin Yang1,2, Qingling Chang1,2, Shiting Xu3, Xinlin Liu1,2, Yan Cui1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 573-592, 2023, DOI:10.32604/cmes.2023.023424

    Abstract Monocular depth estimation is the basic task in computer vision. Its accuracy has tremendous improvement in the decade with the development of deep learning. However, the blurry boundary in the depth map is a serious problem. Researchers find that the blurry boundary is mainly caused by two factors. First, the low-level features, containing boundary and structure information, may be lost in deep networks during the convolution process. Second, the model ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area, during the backpropagation. Focusing on the factors mentioned above.… More >

  • Open Access

    ARTICLE

    EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network

    S. Kavi Priya*, K. Pon Karthika

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1745-1766, 2023, DOI:10.32604/iasc.2023.032160

    Abstract Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory… More >

  • Open Access

    ARTICLE

    Chi-Square and PCA Based Feature Selection for Diabetes Detection with Ensemble Classifier

    Vaibhav Rupapara1, Furqan Rustam2, Abid Ishaq2, Ernesto Lee3, Imran Ashraf4,*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1931-1949, 2023, DOI:10.32604/iasc.2023.028257

    Abstract Diabetes mellitus is a metabolic disease that is ranked among the top 10 causes of death by the world health organization. During the last few years, an alarming increase is observed worldwide with a 70% rise in the disease since 2000 and an 80% rise in male deaths. If untreated, it results in complications of many vital organs of the human body which may lead to fatality. Early detection of diabetes is a task of significant importance to start timely treatment. This study introduces a methodology for the classification of diabetic and normal people using an ensemble machine learning model… More >

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