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  • Open Access

    ARTICLE

    A YOLOv11-Based Deep Learning Framework for Multi-Class Human Action Recognition

    Nayeemul Islam Nayeem1, Shirin Mahbuba1, Sanjida Islam Disha1, Md Rifat Hossain Buiyan1, Shakila Rahman1,*, M. Abdullah-Al-Wadud2, Jia Uddin3,*

    CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

    Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

  • Open Access

    ARTICLE

    Context-Aware Feature Extraction Network for High-Precision UAV-Based Vehicle Detection in Urban Environments

    Yahia Said1,*, Yahya Alassaf2, Taoufik Saidani3, Refka Ghodhbani3, Olfa Ben Rhaiem4, Ali Ahmad Alalawi1

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4349-4370, 2024, DOI:10.32604/cmc.2024.058903 - 19 December 2024

    Abstract The integration of Unmanned Aerial Vehicles (UAVs) into Intelligent Transportation Systems (ITS) holds transformative potential for real-time traffic monitoring, a critical component of emerging smart city infrastructure. UAVs offer unique advantages over stationary traffic cameras, including greater flexibility in monitoring large and dynamic urban areas. However, detecting small, densely packed vehicles in UAV imagery remains a significant challenge due to occlusion, variations in lighting, and the complexity of urban landscapes. Conventional models often struggle with these issues, leading to inaccurate detections and reduced performance in practical applications. To address these challenges, this paper introduces CFEMNet,… More >

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