Zheda Zhao, Tao Xu, Tong Yang, Yunpeng Wu^*, Fengxiang Guo^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1457-1472, 2025, DOI:10.32604/sdhm.2025.069611 - 17 November 2025

Abstract Utilizing unmanned aerial vehicle (UAV) photography to timely detect and evaluate potential safety hazards (PSHs) around high-speed rail has great potential to complement and reform the existing manual inspections by providing better overhead views and mitigating safety issues. However, UAV inspections based on manual interpretation, which heavily rely on the experience, attention, and judgment of human inspectors, still inevitably suffer from subjectivity and inaccuracy. To address this issue, this study proposes a lightweight hybrid learning algorithm named HDTA (hybrid dual tasks architecture) to automatically and efficiently detect the PSHs of UAV imagery. First, this HDTA… More >

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025

Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >

Bassam Mohammad Elzaghmouri¹, Yosef Hasan Fayez Jbara², Said Elaiwat³, Nisreen Innab^4,*, Ahmed Abdelgader Fadol Osman⁵, Mohammed Awad Mohammed Ataelfadiel⁵, Farah H. Zawaideh⁶, Mouiad Fadeil Alawneh⁷, Asef Al-Khateeb⁸, Marwan Abu-Zanona⁸

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2299-2316, 2024, DOI:10.32604/cmc.2024.054836 - 18 November 2024

Abstract As the Internet of Things (IoT) continues to expand, incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats, necessitating robust defense mechanisms. This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings. Our proposed model combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BLSTM), Gated Recurrent Units (GRU), and Attention mechanisms into a cohesive framework. This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.… More >

Jinseok Kim¹, Babar Shah², Ki-Il Kim^3,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 283-301, 2021, DOI:10.32604/cmc.2021.016042 - 22 March 2021

Abstract Load forecasting has received crucial research attention to reduce peak load and contribute to the stability of power grid using machine learning or deep learning models. Especially, we need the adequate model to forecast the maximum load duration based on time-of-use, which is the electricity usage fare policy in order to achieve the goals such as peak load reduction in a power grid. However, the existing single machine learning or deep learning forecasting cannot easily avoid overfitting. Moreover, a majority of the ensemble or hybrid models do not achieve optimal results for forecasting the maximum… More >

Moazam Ali, Susmit Bagchi^*

Intelligent Automation & Soft Computing, Vol.24, No.4, 2018, DOI:10.31209/2018.100000043

Abstract Due to the rapid advancements and developments in wide area networks and powerful computational resources, the load balancing mechanisms in distributed systems have gained pervasive applications covering wired as well as mobile distributed systems. In large-scale distributed systems, sharing of distributed resources is required for enhancing overall resource utilization. This paper presents a comprehensive study and detailed comparative analysis of different load balancing algorithms employing fuzzy logic and mobile agents. We have proposed a hybrid architecture for integrated load balancing and monitoring in distributed computing systems employing fuzzy logic and autonomous mobile agents. Furthermore, we More >