Ahmad Zia¹, Nazia Azim², Bekarystankyzy Akbayan³, Khalid J. Alzahrani⁴, Ateeq Ur Rehman^5,*, Faheem Ullah Khan⁶, Nouf Al-Kahtani⁷, Hend Khalid Alkahtani^8,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073818 - 12 January 2026

Abstract The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks. Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay. In this network, the task processed at fog nodes reduces transmission delay. Still, it increases energy consumption, while routing tasks to the cloud server saves energy at the cost of higher communication delay. Moreover, the… More >

Xiang Luo¹, Yuxuan Peng², Renghong Xie¹, Peng Li³, Yuwen Qian^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073700 - 12 January 2026

Abstract Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects… More >

Md Minhazul Islam^1,2, Yunfei Yin^1,2,*, Md Tanvir Islam^1,2, Zheng Yuan^1,2, Argho Dey^1,2

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

Muhammad Ejaz¹, Muhammad Asim^2,*, Mudasir Ahmad Wani^2,3, Kashish Ara Shakil^4,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072464 - 12 January 2026

Abstract The integration of High-Altitude Platform Stations (HAPS) with Reconfigurable Intelligent Surfaces (RIS) represents a critical advancement for next-generation wireless networks, offering unprecedented opportunities for ubiquitous connectivity. However, existing research reveals significant gaps in dynamic resource allocation, joint optimization, and equitable service provisioning under varying channel conditions, limiting practical deployment of these technologies. This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning (FAIR-DQL) framework for joint resource management and phase configuration in HAPS-RIS systems. Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control, priority-based user scheduling, and dynamic learning mechanisms. More >

Yuanyuan Wang^1,*, Yemeng Zhu¹, Xiuchuan Chen¹, Tongtong Yin¹, Shiwei Su²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072404 - 12 January 2026

Abstract To solve the false detection and missed detection problems caused by various types and sizes of defects in the detection of steel surface defects, similar defects and background features, and similarities between different defects, this paper proposes a lightweight detection model named multiscale edge and squeeze-and-excitation attention detection network (MSESE), which is built upon the You Only Look Once version 11 nano (YOLOv11n). To address the difficulty of locating defect edges, we first propose an edge enhancement module (EEM), apply it to the process of multiscale feature extraction, and then propose a multiscale edge enhancement… More >

Qing Long¹, Bing Yi², Haiqiao Liu^3,*, Zhiling Peng¹, Xiang Liu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072058 - 12 January 2026

Abstract Accurate detection of smoke and fire sources is critical for early fire warning and environmental monitoring. However, conventional detection approaches are highly susceptible to noise, illumination variations, and complex environmental conditions, which often reduce detection accuracy and real-time performance. To address these limitations, we propose Lightweight and Precise YOLO (LP-YOLO), a high-precision detection framework that integrates a self-attention mechanism with a feature pyramid, built upon YOLOv8. First, to overcome the restricted receptive field and parameter redundancy of conventional Convolutional Neural Networks (CNNs), we design an enhanced backbone based on Wavelet Convolutions (WTConv), which expands the… More >

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Chao Li^1,3,#, Jianing Wang^1,3,#, Caichang Ding^2,*, Zhiwei Ye^1,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071242 - 12 January 2026

Abstract Underwater images frequently suffer from chromatic distortion, blurred details, and low contrast, posing significant challenges for enhancement. This paper introduces AquaTree, a novel underwater image enhancement (UIE) method that reformulates the task as a Markov Decision Process (MDP) through the integration of Monte Carlo Tree Search (MCTS) and deep reinforcement learning (DRL). The framework employs an action space of 25 enhancement operators, strategically grouped for basic attribute adjustment, color component balance, correction, and deblurring. Exploration within MCTS is guided by a dual-branch convolutional network, enabling intelligent sequential operator selection. Our core contributions include: (1) a More >

Jun Li¹, Chunyan Liang^1,*, Zhiguo Liu¹, Fengpei Ge²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071011 - 12 January 2026

Abstract To enhance speech emotion recognition capability, this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup (AAM) and improved coordinate and shuffle attention (ICASA) methods. The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients, thus enabling information fusion of speech data with different emotions at the acoustic level. The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention (ICA) and shuffle attention (SA) techniques. The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and More >

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070224 - 12 January 2026

Abstract Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for More >