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  • Open Access

    ARTICLE

    Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

    Israa K. Salman Al-Tameemi1,3, Mohammad-Reza Feizi-Derakhshi1,*, Saeed Pashazadeh2, Mohammad Asadpour2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997

    Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual… More >

  • Open Access

    PROCEEDINGS

    A Shape Optimization Approach for 3D Doubly-Periodic Multi-Layered Systems

    Haibo Chen1,*, Fuhang Jiang1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09414

    Abstract Acoustic wave propagation has been the subject of many studies in engineering and physics. Researchers have shown an increased interest in recent years in the acoustic scattering of periodic systems, such as phononic crystals and metamaterials [1]. These artificial periodic systems possess some particular acoustic characteristics including noise control, waveguides and negative refraction, which manifest excellent potential applicability in acoustic engineering. Based on the isogeometric acoustic boundary element method (BEM) [2], an efficient shape optimization approach is proposed in this research for threedimensional doubly-periodic multi-layered systems. The interfaces between different acoustic mediums are infinite doubly periodic surfaces, which can be… More >

  • Open Access

    PROCEEDINGS

    Micromechanical Study of Heterogenous Deformation of Austenitic Stainless Steel Welded Joints at Different Temperatures

    Lifeng Gan1, Baoyin Zhu2, Chao Ling1,*, Esteban P. Busso1, Dongfeng Li1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.010397

    Abstract Heat-resistant austenitic stainless steels are widely used in the final stages of superheater and reheater in in the new generation of fossil fuel power stations, due to their high creep strength. Similar weld joints, fabricated using gas tungsten arc welding, for connecting different components made of the heat resistant austenitic stainless steels usually suffer from premature failures at elevated temperature [1]. Experimental studies showed that cracks may nucleate in the heat affected zone or weld metal of the similar welded joints under service conditions. In order to reveal the physical origin of unexpected failures of the weld joints, a microstructure-based… More >

  • Open Access

    ARTICLE

    Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

    Abdul Haseeb1, Muhammad Attique Khan1,*, Faheem Shehzad1, Majed Alhaisoni2, Junaid Ali Khan1, Taerang Kim3, Jae-Hyuk Cha3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

    Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion is performed using a canonical… More >

  • Open Access

    ARTICLE

    COVID TCL: A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

    Rui Wen1,*, Jie Zhou2, Zhongliang Shen1, Xiaorui Zhang2,3,4, Sunil Kumar Jha5

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 187-204, 2023, DOI:10.32604/csse.2023.037889

    Abstract Convolution Neural Networks (CNN) can quickly diagnose COVID-19 patients by analyzing computed tomography (CT) images of the lung, thereby effectively preventing the spread of COVID-19. However, the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population. Which reduces the model’s classification sensitivity, resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people. To solve the problem, this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image… More >

  • Open Access

    ARTICLE

    Bioinformatic analysis of lncRNA-associated competing endogenous RNA regulatory networks in synovial tissue of temporomandibular joint osteoarthritis

    CHUYAO WANG1,2,#, CHUAN LU2,#, LUXIANG ZOU2,*, DONGMEI HE2,*

    BIOCELL, Vol.47, No.6, pp. 1293-1306, 2023, DOI:10.32604/biocell.2023.028199

    Abstract Background: Temporomandibular joint osteoarthritis (TMJOA) is an end-stage disease that seriously affects the patients’ quality of life. Molecular insights in advancing our understanding of TMJOA are the need of the hour. Methods: We performed RNA high-throughput sequencing and bioinformatics analysis of differentially expressed (DE) long non-coding RNA (lncRNAs), microRNAs (miRNAs), and messenger RNA (mRNAs) in human synovial TMJOA tissues. Firstly, synovium samples of TMJOA patients and non-TMJOA controls were collected for highthroughput sequencing of lncRNAs, miRNAs, and mRNAs. We then performed biological function analysis of the top 100 mRNAs with more than 2-fold differential expression, and their upstream regulated miRNAs… More >

  • Open Access

    ARTICLE

    Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering

    Zhuohan Jiang1, Jingyi Tu1, Shuncheng Liu1, Jian Peng1, Guang Ouyang2,*

    Energy Engineering, Vol.120, No.7, pp. 1655-1666, 2023, DOI:10.32604/ee.2023.026981

    Abstract The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels. This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions. The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales. More >

  • Open Access

    ARTICLE

    Detection Algorithm of Knee Osteoarthritis Based on Magnetic Resonance Images

    Xin Wang*, Shuang Liu, Chang-Cai Zhou

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766

    Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided… More >

  • Open Access

    ARTICLE

    3D Human Pose Estimation Using Two-Stream Architecture with Joint Training

    Jian Kang1, Wanshu Fan1, Yijing Li2, Rui Liu1, Dongsheng Zhou1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 607-629, 2023, DOI:10.32604/cmes.2023.024420

    Abstract With the advancement of image sensing technology, estimating 3D human pose from monocular video has become a hot research topic in computer vision. 3D human pose estimation is an essential prerequisite for subsequent action analysis and understanding. It empowers a wide spectrum of potential applications in various areas, such as intelligent transportation, human-computer interaction, and medical rehabilitation. Currently, some methods for 3D human pose estimation in monocular video employ temporal convolutional network (TCN) to extract inter-frame feature relationships, but the majority of them suffer from insufficient inter-frame feature relationship extractions. In this paper, we decompose the 3D joint location regression… More >

  • Open Access

    ARTICLE

    Delivery Invoice Information Classification System for Joint Courier Logistics Infrastructure

    Youngmin Kim1, Sunwoo Hwang2, Jaemin Park1, Joouk Kim2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3027-3044, 2023, DOI:10.32604/cmc.2023.027877

    Abstract With the growth of the online market, demand for logistics and courier cargo is increasing rapidly. Accordingly, in the case of urban areas, road congestion and environmental problems due to cargo vehicles are mainly occurring. The joint courier logistics system, a plan to solve this problem, aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies. However, several courier companies use different types of courier invoices. Such a system has a problem of information data transmission interruption. Therefore, the data processing process was systematically analyzed, a practically feasible methodology… More >

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