Ronghao Pan¹, Tomás Bernal-Beltrán¹, Alejandro Rodríguez-González^2,3, Ernestina Menasalvas-Ruíz^2,3, Rafael Valencia-García^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077501 - 09 April 2026

Abstract The digitization of healthcare has resulted in the production of large amounts of structured and unstructured clinical data, creating the need for accurate and efficient named entity recognition (NER) to support medical procedures. This study evaluates and compares three approaches to NER in the medical domain in Spanish: using Large Language Models (LLMs) with In-Context Learning techniques (Zero-Shot, Few-Shot, and Chain-of-Thought); fine-tuning of LLMs; and fine-tuning of encoder-only models. Experiments were conducted on the Meddocan, Meddoprof, Meddoplace and Symptemist benchmark datasets. Fine-tuned encoder-only models achieve the best performance across all datasets, reaching macro-F1 scores of More >

Muhammad Ishtiaq, Yeonwoo Kim, Sung-Gyu Kang^*, Nagireddy Gari Subba Reddy^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077416 - 09 April 2026

Abstract The long-term reliability of 1.25Cr-0.5Mo steels in high-temperature service critically depends on their creep rupture behavior, which is strongly influenced by alloy composition, microstructural characteristics, and testing conditions. In this study, an advanced Artificial Neural Network (ANN) model was developed to accurately predict the creep-rupture life of 1.25Cr-0.5Mo steels, offering a data-driven framework for alloy design and service-life assessment. The model incorporated eleven compositional variables (C, Si, Mn, P, S, Ni, Cr, Mo, Cu, Al, N), average grain size, non-metallic inclusions (NMI), steel properties including hardness measured on the Rockwell B scale (HRB) yield strength… More >

Kiseok Kim^#, Taehoon Yoo^#, Sangmin Lee, Hwangnam Kim^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077415 - 09 April 2026

Abstract Conventional Low-Rank Adaptation (LoRA) constrains weight updates to a static linear low-rank manifold, which is inherently limited when applied to Reinforcement Learning (RL) tasks for Unmanned Aerial Vehicle (UAV) applications. UAVs operate in highly dynamic and nonstationary environments where rapid variations in sensing and state transitions lead to complex, nonlinear input–output relationships. Such environmental complexity cannot be adequately modeled by a static Low-rank approximation, making conventional LoRA approaches insufficient for the high-dimensional dynamics required in UAV applications. To overcome these limitations, we propose an attention-enhanced LoRA that constructs an input-dependent and intrinsically nonlinear adaptation manifold.… More >

Kexin Jiang^1,2, Yong Fan², Liang Wen¹, Zhigang Xie¹, Enzhi Dong¹, Bo Zhu¹, Zhonghua Cheng^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077392 - 09 April 2026

Abstract With the growing deployment of unmanned aerial vehicles (UAVs), reliable engine health state assessment (HSA) requires methods that are interpretable, auditable, and transferable under noisy data and varying operating conditions. This paper proposes an AHP-enhanced, data-driven HSA framework that builds a unified health vector from four indicators—remaining useful life (RUL) health, absolute state, relative degradation, and condition health. Indicator weights are derived using AHP with consistency checking, and the resulting continuous health index is mapped through nonlinear stretching and four-level thresholds to produce actionable health grades. Experiments on the NASA CMAPSS benchmark (FD001) evaluate conventional More >

Zhaojun Gu¹, Wenlong Yue², Chunbo Liu^1,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077388 - 09 April 2026

Abstract Anomaly detection in system logs is a critical technical means for identifying potential faults and security risks. In distributed environments, traditional deep learning-based log anomaly detection methods often suffer from shortcomings in transparency, computational overhead, and data privacy protection. To address these issues, this paper proposes a federated learning-driven lightweight and explainable log anomaly detection framework named FedXLog. The framework adapts to heterogeneous logs through hierarchical feature extraction, introduces the Federated Gradient Trajectory Aggregation algorithm (FedGradTrace) to enhance the explainability of the parameter aggregation process, constructs lightweight models using knowledge distillation, and achieves globally consistent… More >

Hamed Alqahtani¹, Gulshan Kumar^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077367 - 09 April 2026

Abstract Large Language Models (LLMs) are becoming integral components of modern cybersecurity ecosystems, simultaneously strengthening defensive capabilities while giving rise to a new class of Artificial Intelligence–Generated Content (AIGC)-driven threats. This PRISMA-guided systematic review synthesises 167 peer-reviewed studies published between 2022 and 2025 and proposes a unified threat–defence–evaluation taxonomy as a central analytical framework to consolidate a previously fragmented body of research. Guided by this taxonomy, the review first examines AIGC-enabled threats, including automated and highly personalised phishing, polymorphic malware and exploit generation, jailbreak and adversarial prompting, prompt-injection attack vectors, multimodal deception, persona-steering attacks, and large-scale… More >

Samar Abbas Mangi^1,*, Samina Rajper¹, Noor Ahmed Shaikh¹, Shehzad Ashraf Chaudhry^2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077337 - 09 April 2026

Abstract Malicious domain detection (MDD) from DNS telemetry enables early threat hunting but is constrained by privacy and data-sharing barriers across organizations. We present a deployable federated learning (FL) pipeline that trains a compact deep neural network (DNN; 64-32-16 with ReLU and dropout 0.3) locally at each client and exchanges only masked model updates. Privacy is enforced via secure aggregation (the server observes only an aggregate of masked updates) and optional server-side differential privacy (DP) via clipping and Gaussian noise. Our feature schema combines DNS-specific lexical cues (character n-grams, entropy, TLD indicators) with lightweight behavioral signals More >

Tsu-Yang Wu¹, Yehai Xue¹, Haonan Li², Saru Kumari³, Lip Yee Por^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077332 - 09 April 2026

Abstract Blockchain technology, characterized by decentralization, transparency, and immutability, has been widely applied in areas such as supply chain tracking, medical data management, and the Internet of Things. However, single blockchain systems suffer from limitations in performance, scalability, and cross-chain interoperability, giving rise to the issue of “blockchain silos.” To address the challenges of data and asset circulation among heterogeneous blockchain networks, both academia and industry have proposed multi-blockchain architectures. In this paper, we categorize current multi-blockchain systems from a network topology perspective into four types: parallel architecture, hierarchical architecture, hybrid architecture, and multi-blockchain networks. We… More >

Jiao Yao, Pujie Wang, Tianyi Zhang, Chenke Zhu, Chenqiang Zhu^*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077329 - 09 April 2026

Abstract In accident scenarios on freeways, traffic congestion, sharp declines in capacity, and the limitations of closed systems where vehicles cannot turn around or exit freely often pose serious challenges. To address these issues, this study develops an improved Markov Decision Process (MDP) framework for dynamic emergency lane opening. Compared with traditional MDP-based traffic control models, the proposed method integrates three enhancements: Firstly, an explicit action decision space transition mechanism that couples variable speed limits with emergency lane opening decisions; Secondly, vehicle-type–differentiated actions to support fine-grained and adaptive opening strategies; and a redesigned reward function incorporating… More >

Yang Lei^1,2, Kangshuo Zhu^3,4,*, Bo Jiang¹, Yaodong Wang^3,4, Feiyu Jia¹, Zhaoning Wang¹, Falin Qi¹, Qiming Qu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077314 - 09 April 2026

Abstract The safe operation of railway systems necessitates efficient and automated inspection of tunnel defects. While deep learning offers solutions, a clear pathway for selecting and optimizing the latest object detectors for distinct defects under strict speed constraints is lacking. This paper presents a two-stage, task-specific framework for high-speed tunnel defect detection. First, this study conducts a comprehensive comparative analysis of state-of-the-art YOLO models (YOLOv5s, YOLOv8s, YOLOv10s, YOLOv11s) on self-constructed datasets. This systematic comparison identifies YOLOv5s as the optimal model for crack detection, achieving an mAP@0.5 of 0.939 at 77.5 FPS, sufficient for inspection at 50… More >