Fang Hao¹, Puyuan Hu², Yumo Jiang², Ruonan Liu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075655 - 12 March 2026

Abstract Industrial fault diagnosis is a critical challenge in complex systems, where sensor data is often noisy and interdependencies between components are difficult to capture. Traditional methods struggle to effectively model these complexities. This paper presents a novel approach by transforming fault diagnosis into a graph recognition task, using sensor data represented as graph-structured data with the k-nearest neighbors (KNN) algorithm. A Graph Transformer is applied to extract node and graph features, with a combined loss function of cross-entropy and weighted consistency loss to stabilize graph representations. Experiments on the TFF dataset show that Graph Transformer More >

Suman Sukhavasi, Thinagaran Perumal^*, Norwati Mustapha, Razali Yaakob

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075613 - 12 March 2026

Abstract The Internet of Things (IoT) ecosystem is inherently heterogeneous, comprising diverse devices that must interoperate seamlessly to enable federated message and data exchange. However, as the number of service requests grows, existing approaches suffer from increased discovery time and degraded Quality of Service (QoS). Moreover, the massive data generated by heterogeneous IoT devices often contain redundancy and noise, posing challenges to efficient data management. To address these issues, this paper proposes a lightweight ontology-based architecture that enhances service discovery and QoS-aware semantic data management. The architecture employs Modified-Ordered Points to Identify the Clustering Structure (M-OPTICS)… More >

Zhengyu Wu¹, Kejun Kang², Yixiu Liu^3,*, Chenpu Li³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075593 - 12 March 2026

Abstract Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention More >

Ho Hai Quan^1,2, Le Huu Binh^1,*, Nguyen Dinh Hoa Cuong³, Le Duc Huy⁴

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075549 - 12 March 2026

Abstract Wireless Sensor Networks (WSNs) play a crucial role in numerous Internet of Things (IoT) applications and next-generation communication systems, yet they continue to face challenges in balancing energy efficiency and reliable connectivity. This study proposes SAC-HTC (Soft Actor-Critic-based High-performance Topology Control), a deep reinforcement learning (DRL) method based on the Actor-Critic framework, implemented within a Software Defined Wireless Sensor Network (SDWSN) architecture. In this approach, sensor nodes periodically transmit state information, including coordinates, node degree, transmission power, and neighbor lists, to a centralized controller. The controller acts as the reinforcement learning (RL) agent, with the… More >

Randika K. Makumbura¹, Hasanthi Wijesundara², Hirushan Sajindra¹, Upaka Rathnayake^1,*, Vikram Kumar³, Dineshbabu Duraibabu¹, Sumit Sen³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075539 - 12 March 2026

Abstract Accurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges… More >

Nurbek Sissenov^1,*, Gulden Ulyukova^1,*, Dina Satybaldina², Nikolaj Goranin³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075504 - 12 March 2026

Abstract The reliability of information systems (IS) is a key factor in the sustainable operation of modern digital services. However, existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments. This paper proposes a hybrid methodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023, multicriteria analysis methods (ARAS, CoCoSo, and TOPSIS), and the XGBoost machine learning algorithm for missing data imputation. The structure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria, while the AHP method allows for… More >

Chia-Hui Liu¹, Zhen-Yu Wu^2,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075495 - 12 March 2026

Abstract Federated Learning (FL) enables collaborative medical model training without sharing sensitive patient data. However, existing FL systems face increasing security risks from post quantum adversaries and often incur non-negligible computational and communication overhead when encryption is applied. At the same time, training high performance AI models requires large volumes of high quality data, while medical data such as patient information, clinical records, and diagnostic reports are highly sensitive and subject to strict privacy regulations, including HIPAA and GDPR. Traditional centralized machine learning approaches therefore pose significant challenges for cross institutional collaboration in healthcare. To address… More >

Atef Gharbi¹, Mohamed Ayari², Nasser Albalawi³, Ahmad Alshammari³, Nadhir Ben Halima^4,*, Zeineb Klai³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075474 - 12 March 2026

Abstract This paper presents Dual Adaptive Neural Topology (Dual ANT), a distributed dual-network meta-adaptive framework that enhances ant-colony-based multi-agent coordination with online introspection, adaptive parameter control, and privacy-preserving interactions. This approach improves standard Ant Colony Optimization (ACO) with two lightweight neural components: a forward network that estimates swarm efficiency in real time and an inverse network that converts these descriptors into parameter adaptations. To preserve the privacy of individual trajectories in shared pheromone maps, we introduce a locally differentially private pheromone update mechanism that adds calibrated noise to each agent’s pheromone deposit while preserving the efficacy More >

Omar Almomani^1,*, Mahran Al-Zyoud¹, Ahmad Adel Abu-Shareha², Ammar Almomani^3,4,*, Said A. Salloum⁵, Khaled Mohammad Alomari⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075465 - 12 March 2026

Abstract In Wireless Sensor Networks (WSNs), survivability is a crucial issue that is greatly impacted by energy efficiency. Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints in WSNs. This paper presents an Adaptive Enhanced Grey Wolf Optimizer (AEGWO) for energy-efficient cluster head (CH) selection that mitigates the exploration–exploitation imbalance, preserves population diversity, and avoids premature convergence inherent in baseline GWO. The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation, a hybrid velocity-momentum update based on the dynamics of PSO, and… More >

Yanping Chen¹, Zhengxin Zhan¹, Xiaohui Yan¹, Le Zou^1,*, Yucheng Zhong¹, Hailei Wang²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075459 - 12 March 2026

Abstract With the increasing complexity of substation inspection tasks, achieving efficient and safe path planning for Unmanned Aerial Vehicles in densely populated and structurally complex three-dimensional (3D) environments remains a critical challenge. To address this problem, this paper proposes an improved path planning algorithm—Random Geometric Graph (RGG)-guided Rapidly-exploring Random Tree (R-RRT)—based on the classical Rapidly-exploring Random Tree (RRT) framework. First, a refined 3D occupancy grid map is constructed from Light Detection and Ranging point cloud data through ground filtering, noise removal, coordinate transformation, and obstacle inflation using spherical structuring elements. During the planning stage, a dynamic… More >