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  • Open Access


    CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

    Mahmood Ul Haq1, Muhammad Athar Javed Sethi1, Najib Ben Aoun2,3, Ala Saleh Alluhaidan4,*, Sadique Ahmad5,6, Zahid farid7

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2169-2186, 2024, DOI:10.32604/cmc.2024.049645

    Abstract Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security, authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neural networks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since they do not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networks as a more robust design capable of retaining pose information and spatial correlations to recognize objects more like the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, and so on, which are… More >

  • Open Access


    Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

    Shoukun Xu1, Lujun Zhang1, Guangqi Jiang1, Yining Hua2, Yi Liu1,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853

    Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature… More >

  • Open Access


    Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

    Sarah B. Basahel1, Saleh Bajaba2, Mohammad Yamin3, Sachi Nandan Mohanty4, E. Laxmi Lydia5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1353-1369, 2023, DOI:10.32604/cmc.2023.036453

    Abstract The current advancement in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT) transformed the traditional healthcare system into smart healthcare. Healthcare services could be enhanced by incorporating key techniques like AI and IoT. The convergence of AI and IoT provides distinct opportunities in the medical field. Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population. Therefore, earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support. Lately, the emergence of IoT,… More >

  • Open Access


    Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

    Tianliang Lu*, Yuxuan Bao, Lanting Li

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 715-740, 2023, DOI:10.32604/cmc.2023.034963

    Abstract Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms, presenting risks for numerous countries, societies, and individuals, and posing a serious threat to cyberspace security. To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection, we propose a detection method based on improved CapsNet and temporal–spatial features (iCapsNet–TSF). First, the dynamic routing algorithm of CapsNet is improved using weight initialization and updating. Then, the optical flow algorithm is used to extract interframe temporal… More >

  • Open Access


    A Hybrid Approach for Plant Disease Detection Using E-GAN and CapsNet

    N. Vasudevan*, T. Karthick

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 337-356, 2023, DOI:10.32604/csse.2023.034242

    Abstract Crop protection is a great obstacle to food safety, with crop diseases being one of the most serious issues. Plant diseases diminish the quality of crop yield. To detect disease spots on grape leaves, deep learning technology might be employed. On the other hand, the precision and efficiency of identification remain issues. The quantity of images of ill leaves taken from plants is often uneven. With an uneven collection and few images, spotting disease is hard. The plant leaves dataset needs to be expanded to detect illness accurately. A novel hybrid technique employing segmentation, augmentation,… More >

  • Open Access


    Emotion Recognition with Capsule Neural Network

    Loan Trinh Van1, Quang H. Nguyen1,*, Thuy Dao Thi Le2

    Computer Systems Science and Engineering, Vol.41, No.3, pp. 1083-1098, 2022, DOI:10.32604/csse.2022.021635

    Abstract For human-machine communication to be as effective as human-to-human communication, research on speech emotion recognition is essential. Among the models and the classifiers used to recognize emotions, neural networks appear to be promising due to the network’s ability to learn and the diversity in configuration. Following the convolutional neural network, a capsule neural network (CapsNet) with inputs and outputs that are not scalar quantities but vectors allows the network to determine the part-whole relationships that are specific 6 for an object. This paper performs speech emotion recognition based on CapsNet. The corpora for speech emotion… More >

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