Chao Zhang¹, Shang-Xi Lai¹, Hua-Ping Wang^1,2,*

Structural Durability & Health Monitoring, Vol.19, No.1, pp. 25-54, 2025, DOI:10.32604/sdhm.2024.053662 - 15 November 2024

Abstract Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure. Therefore, it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring (SHM) system, so as to provide a scientific basis for structural damage identification and dynamic model modification. In view of this, this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters. The paper primarily introduces data-driven modal parameter recognition methods… More >

Farida Asriani^1,2, Azhari Azhari^1,*, Wahyono Wahyono¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3079-3096, 2024, DOI:10.32604/cmc.2024.058193 - 18 November 2024

Abstract Incredible progress has been made in human action recognition (HAR), significantly impacting computer vision applications in sports analytics. However, identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns. Deep learning techniques like convolutional neural networks (CNNs), long short-term memory (LSTM), and graph convolutional networks (GCNs) improve recognition in large datasets, while the traditional machine learning methods like SVM (support vector machines), RF (random forest), and LR (logistic regression), combined with handcrafted features and ensemble approaches, perform well but… More >

Jian Su¹, Haijian Shao^1,2,*, Xing Deng¹, Yingtao Jiang²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2219-2242, 2024, DOI:10.32604/cmc.2024.057604 - 18 November 2024

Abstract The rapidly advancing Convolutional Neural Networks (CNNs) have brought about a paradigm shift in various computer vision tasks, while also garnering increasing interest and application in sensor-based Human Activity Recognition (HAR) efforts. However, the significant computational demands and memory requirements hinder the practical deployment of deep networks in resource-constrained systems. This paper introduces a novel network pruning method based on the energy spectral density of data in the frequency domain, which reduces the model’s depth and accelerates activity inference. Unlike traditional pruning methods that focus on the spatial domain and the importance of filters, this… More >

Mohd Asif Hajam¹, Tasleem Arif¹, Akib Mohi Ud Din Khanday², Mudasir Ahmad Wani^3,*, Muhammad Asim^3,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2077-2131, 2024, DOI:10.32604/cmc.2024.057136 - 18 November 2024

Abstract The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and cost-effectiveness compared to modern drugs. Throughout the extensive history of medicinal plant usage, various plant parts, including flowers, leaves, and roots, have been acknowledged for their healing properties and employed in plant identification. Leaf images, however, stand out as the preferred and easily accessible source of information. Manual plant identification by plant taxonomists is intricate, time-consuming, and prone to errors, relying heavily on human perception. Artificial intelligence (AI) techniques offer a solution by automating plant recognition processes. This study thoroughly examines cutting-edge… More >

Tanvir Fatima Naik Bukht¹, Abdulwahab Alazeb², Naif Al Mudawi², Bayan Alabdullah³, Khaled Alnowaiser⁴, Ahmad Jalal¹, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2987-3002, 2024, DOI:10.32604/cmc.2024.053547 - 18 November 2024

Abstract In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT More >

Wei Yang^1,2,*, Ivy Kim D. Machica¹

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 889-905, 2024, DOI:10.32604/iasc.2024.055133 - 31 October 2024

Abstract Birds play a crucial role in maintaining ecological balance, making bird recognition technology a hot research topic. Traditional recognition methods have not achieved high accuracy in bird identification. This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan. First, a dataset containing five species of local birds in Yunnan was established: C. amherstiae, T. caboti, Syrmaticus humiae, Polyplectron bicalcaratum, and Pucrasia macrolopha. The improved ResNet18 model was then used to identify these species. This method replaces traditional convolution with depth wise separable convolution and introduces an SE (Squeeze and Excitation) module to More >

Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Lingsheng Li¹, Weiqing Bai², Chong Han^2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1613-1640, 2024, DOI:10.32604/cmc.2024.056073 - 15 October 2024

Abstract Gestures are one of the most natural and intuitive approach for human-computer interaction. Compared with traditional camera-based or wearable sensors-based solutions, gesture recognition using the millimeter wave radar has attracted growing attention for its characteristics of contact-free, privacy-preserving and less environment-dependence. Although there have been many recent studies on hand gesture recognition, the existing hand gesture recognition methods still have recognition accuracy and generalization ability shortcomings in short-range applications. In this paper, we present a hand gesture recognition method named multiscale feature fusion (MSFF) to accurately identify micro hand gestures. In MSFF, not only the More >

Menghan Zhang^1,2, Fangfang Shan^1,2,*, Mengyao Liu^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1565-1580, 2024, DOI:10.32604/cmc.2024.056008 - 15 October 2024

Abstract With the emergence and development of social networks, people can stay in touch with friends, family, and colleagues more quickly and conveniently, regardless of their location. This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy. Due to the complexity and subtlety of sensitive information, traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and images. In this context, this paper adopts the CLIP model as a modality discriminator. By using comparative learning between sensitive image descriptions and… More >

Manh-Tuyen Vi¹, Duc-Nghia Tran², Vu Thi Thuong^3,4, Nguyen Ngoc Linh^5,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1729-1756, 2024, DOI:10.32604/cmc.2024.055511 - 15 October 2024

Abstract Analyzing physical activities through wearable devices is a promising research area for improving health assessment. This research focuses on the development of an affordable and real-time Human Activity Recognition (HAR) system designed to operate on low-performance microcontrollers. The system utilizes data from a body-worn accelerometer to recognize and classify human activities, providing a cost-effective, easy-to-use, and highly accurate solution. A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment. The system employs a Random Forest (RF) classifier, which outperforms Gradient Boosting Decision Trees (GBDT), Support Vector Machines… More >