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  • Open Access


    Structural Damage Identification Using Modal Energy and Improved Hybrid Gradient-Based Optimizer

    Nizar Faisal Alkayem1, Maosen Cao2,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.26, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.09367

    Abstract Structural damage identification is a key engineering technique that attempts to ensure structural reliability. In this regard, one of the major intelligent approaches is the inverse analysis of structural damage using metaheuristics. By considering the recent achievements, an efficient hybrid objective function that combines the modal kinetic energy and modal strain energy is developed. The objective function aims to extract maximum modal information from the structure and overcome noisy conditions. Moreover, the original methods are usually vulnerable to the associated high multimodality and uncertainty of the inverse problem. Therefore, the particle swarm algorithm (PSO) mechanism is combined with another newly… More >

  • Open Access


    Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

    Guohui Song1,2, Yongbin Wang1,*, Jianfei Li1, Hongbin Hu1

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095

    Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The… More >

  • Open Access


    Deep Facial Emotion Recognition Using Local Features Based on Facial Landmarks for Security System

    Youngeun An, Jimin Lee, EunSang Bak*, Sungbum Pan*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1817-1832, 2023, DOI:10.32604/cmc.2023.039460

    Abstract Emotion recognition based on facial expressions is one of the most critical elements of human-machine interfaces. Most conventional methods for emotion recognition using facial expressions use the entire facial image to extract features and then recognize specific emotions through a pre-trained model. In contrast, this paper proposes a novel feature vector extraction method using the Euclidean distance between the landmarks changing their positions according to facial expressions, especially around the eyes, eyebrows, nose, and mouth. Then, we apply a new classifier using an ensemble network to increase emotion recognition accuracy. The emotion recognition performance was compared with the conventional algorithms… More >

  • Open Access


    Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

    Mingqiang Zhou*, Mengjiao Li, Zhiyuan Qian, Kunpeng Li

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2443-2460, 2023, DOI:10.32604/cmc.2023.038675

    Abstract Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the role-similar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features fusion (REQF), which simultaneously considers… More >

  • Open Access


    Developing a Breast Cancer Resistance Protein Substrate Prediction System Using Deep Features and LDA

    Mehdi Hassan1,2, Safdar Ali3, Jin Young Kim2,*, Muhammad Sanaullah4, Hani Alquhayz5, Khushbakht Safdar6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1643-1663, 2023, DOI:10.32604/cmc.2023.038578

    Abstract Breast cancer resistance protein (BCRP) is an important resistance protein that significantly impacts anticancer drug discovery, treatment, and rehabilitation. Early identification of BCRP substrates is quite a challenging task. This study aims to predict early substrate structure, which can help to optimize anticancer drug development and clinical diagnosis. For this study, a novel intelligent approach-based methodology is developed by modifying the ResNet101 model using transfer learning (TL) for automatic deep feature (DF) extraction followed by classification with linear discriminant analysis algorithm (TLRNDF-LDA). This study utilized structural fingerprints, which are exploited by DF contrary to conventional molecular descriptors. The proposed in… More >

  • Open Access


    Circulating tumor cells: Biological features and survival mechanisms


    BIOCELL, Vol.47, No.8, pp. 1771-1781, 2023, DOI:10.32604/biocell.2023.028343

    Abstract Circulating tumor cells (CTCs) are neoplastic cells that are detached from primary tumors and enter circulation. Enumeration and characterization of CTCs are of significance in cancer diagnosis, prognosis, and treatment monitoring. CTC survival in the bloodstream is a limiting step for the development of metastases in distant organs. Recent technological advances, especially in single-cell molecular analyses have uncovered heterogeneous CTC survival mechanisms. Undergoing epithelial-to-mesenchymal transition (EMT), increasing stem cell-like properties, and forming cell clusters enable CTCs to adapt to the harsh microenvironment of the circulation. Expressing and releasing several immunosuppressive molecules help CTCs escape from anti-cancer immune mechanisms. This review… More >

  • Open Access


    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

    Ahmed Silik1,2,7, Xiaodong Wang3, Chenyue Mei3, Xiaolei Jin3, Xudong Zhou4, Wei Zhou4, Congning Chen4, Weixing Hong1,2, Jiawei Li1,2, Mingjie Mao1,2, Yuhan Liu1,2, Mohammad Noori5,6,*, Wael A. Altabey8,*

    Structural Durability & Health Monitoring, Vol.17, No.4, pp. 257-281, 2023, DOI:10.32604/sdhm.2023.023617

    Abstract Damage detection is an important area with growing interest in mechanical and structural engineering. One of the critical issues in damage detection is how to determine indices sensitive to the structural damage and insensitive to the surrounding environmental variations. Current damage identification indices commonly focus on structural dynamic characteristics such as natural frequencies, mode shapes, and frequency responses. This study aimed at developing a technique based on energy Curvature Difference, power spectrum density, correlation-based index, load distribution factor, and neutral axis shift to assess the bridge deck condition. In addition to tracking energy and frequency over time using wavelet packet… More > Graphic Abstract

    Development of Features for Early Detection of Defects and Assessment of Bridge Decks

  • Open Access


    Suspicious Activities Recognition in Video Sequences Using DarkNet-NasNet Optimal Deep Features

    Safdar Khan1, Muhammad Attique Khan2, Jamal Hussain Shah1,*, Faheem Shehzad2, Taerang Kim3, Jae-Hyuk Cha3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2337-2360, 2023, DOI:10.32604/csse.2023.040410

    Abstract Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The… More >

  • Open Access


    Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

    Ghoroub Talal Bostaji*, Emad Sami Jaha

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1571-1591, 2023, DOI:10.32604/csse.2023.039701

    Abstract Human recognition technology based on biometrics has become a fundamental requirement in all aspects of life due to increased concerns about security and privacy issues. Therefore, biometric systems have emerged as a technology with the capability to identify or authenticate individuals based on their physiological and behavioral characteristics. Among different viable biometric modalities, the human ear structure can offer unique and valuable discriminative characteristics for human recognition systems. In recent years, most existing traditional ear recognition systems have been designed based on computer vision models and have achieved successful results. Nevertheless, such traditional models can be sensitive to several unconstrained… More >

  • Open Access


    Inoculation of Chlorella and Food Waste Improves the Physio-Morphological Features of Red Pepper by Regulating Activating Antioxidant Defense System

    Sang-Mo Kang1,#, Shifa Shaffique1,#, Muhammad Imran2,#, Su-Mi Jeon3, Shabir Hussain Wani5, Muhammad Aaqil Khan4, Peter Odongkara1, Eun-Hae Kwon1, Yosep Kang1, Joon-Ik Son6, Won-Chan Kim1,*, In-Jung Lee1,*

    Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2699-2711, 2023, DOI:10.32604/phyton.2023.028224

    Abstract Food waste is recognized as a valuable source for potential agricultural applications to supply organic matter and nutrients to arable soil. However, the information on the combined application of food waste and the plant growth-promoting bacterial strain, Chlorella, related to plant metabolic features and sodium chloride content in arable soil is limited. The present study was conducted to investigate the exogenous application of food waste along with Chlorella, which improved the physio-morphological features of red pepper. Our results revealed that this combination enhanced the organic matter in the soil, ultimately improving the fertility rate of the soil, and the physio-morphological… More >

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