Kuen-Suan Chen^1,2,3, Tsai-Sung Lin⁴, Ruey-Chyn Tsaur^4,*, Minh T. N. Nguyen⁵

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079476 - 09 April 2026

Abstract As artificial intelligence, the Internet of Things, edge computing, and blockchain are increasingly integrated into long-term care (LTC) services, policymakers face complex and often non-compensatory trade-offs among affordability, workforce sustainability, service reliability, and data governance. Conventional compensatory evaluation models tend to mask critical structural weaknesses and limiting their usefulness for Smart LTC policy assessment. This study proposes and applies a Fuzzy Trade-Off-Aware Scoring with Conflicts (Fuzzy TASC) framework to evaluate Smart LTC system performance. Four digital-integration configurations—conventional cloud-based LTC, AI+IoT, AI+Edge, and AI+Blockchain—were compared across 12 OECD countries. A Monte Carlo perturbation procedure was incorporated… More >

Yan-Hao Huang^*, Chung-Ming Kao

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.079381 - 09 April 2026

Abstract Photovoltaic (PV) equivalent-circuit models are widely used for performance evaluation and diagnostics, but their usefulness relies on both accurate calibration and interpretable understanding of how parameters shape current–voltage (I–V) behavior. For nonlinear and strongly coupled PV models, conventional global sensitivity analysis can be computationally demanding and offer limited insight into effect direction and operating-point dependence. This study presents an method-oriented framework that integrates nature-inspired optimization with surrogate-based explainable global sensitivity analysis under a specified operating condition. The Starfish Optimization Algorithm (SFOA) is first used for parameter identification by searching for the optimal parameter set that… More >

Gangtao Han¹, Yurui Chen¹, Song Wang^1,*, Enqing Chen¹, Lingling Li², Gaofeng Pan³

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077768 - 09 April 2026

Abstract The integrity of perception data transmitted over in-vehicle networks is important for the safety of autonomous driving. However, legacy protocols like the Controller Area Network (CAN) bus which lacks essential security features make In-Vehicle Networks (IVNs) vulnerable to data tampering attacks. Current research typically focuses on detecting the attack itself but ignores the information recovery from the missing data, leading to an unsafe autonomous driving system. To address the issue, we propose a 3D object recovery framework to recover the missing data caused by the tampering attack that occurred in in-vehicle networks. The proposed framework… 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 >

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 >

Yu Yang, Yuheng Gu^*, Zhuoyun Yang, Shangjun Wu

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077275 - 09 April 2026

Abstract The performance of network traffic detection heavily relies on high-quality annotated data, yet the widespread presence of noisy labels in real-world scenarios severely undermines model reliability and generalization. Existing methods predominantly rely on training dynamics signals and struggle to distinguish between noisy labels and valuable hard samples, leading to diminished model sensitivity to emerging threats. To address this fundamental challenge, this paper proposes a noise-tolerant label recognition and correction framework based on graph-structured neighbor consistency (NCRT). The framework leverages the inherent clustering characteristics of network traffic in feature space, constructs an adaptive K-nearest neighbor graph… More >

Fengqi Hao^1,2,3, Yawen Hou^2,3, Conghui Gao^2,3, Jinqiang Bai^2,3, Gang Liu⁴, Hoiio Kong^1,*, Xiangjun Dong^1,2,3

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077252 - 09 April 2026

Abstract Field–road classification, a fine-grained form of agricultural machinery operation-mode identification, aims to use Global Navigation Satellite System (GNSS) trajectory data to assign each trajectory point a semantic label indicating whether the machine is performing field work or travelling on roads. Existing methods struggle with highly imbalanced class distributions, noisy measurements, and intricate spatiotemporal dependencies. This paper presents AgroGeoDB-Net, a unified framework that combines a residual BiLSTM backbone with two tightly coupled innovations: (i) a Density-Aware Local Interpolator (DALI), which balances the minority road class via density-aware interpolation while preserving road-segment structure; and (ii) a geometry-aware… More >

Abuzar Khan¹, Abid Iqbal^2,*, Ghassan Husnain^1,*, Fahad Masood¹, Mohammed Al-Naeem³, Sajid Iqbal⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077222 - 09 April 2026

Abstract Cloud computing now supports large-scale maritime analytics, yet offloading rich Automatic Identification System (AIS) data to the cloud exposes sensitive operational patterns and complicates compliance with cross-border privacy regulations. This work addresses the gap between growing demand for AI-driven vessel intelligence and the limited availability of practical, privacy-preserving cloud solutions. We introduce a privacy-by-design edge-cloud framework in which ports and vessels serve as federated clients, training vessel-type classifiers on local AIS trajectories while transmitting only clipped, Gaussian-perturbed updates to a zero-trust cloud coordinator employing secure and robust aggregation. Using a public AIS corpus with realistic… More >

Chung-Wei Kuo^1,2,*, Cheng-Xuan Wu¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076767 - 09 April 2026

Abstract The rapid proliferation of the Internet of Things (IoT) has not only reshaped the digital ecosystem but also significantly widened the attack surface, leading to a surge in network traffic and diverse security threats. Deploying effective defense mechanisms in such environments is challenging, as conventional Intrusion Detection Systems (IDS) often struggle to balance computational efficiency with the reliable detection of low-frequency, high-impact threats, particularly within the tight resource constraints of edge devices. To address these limitations, we propose a lightweight, high-efficiency IDS framework specifically optimized for edge-based IoT applications, incorporating Mutual Information (MI)-based feature selection… More >

Qinglei Zhang, Zhenzhen Wang^*, Jianguo Duan, Jiyun Qin, Ying Zhou

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076562 - 09 April 2026

Abstract In recent years, increasing urban mobility and complex traffic dynamics have intensified the need for accurate traffic flow forecasting in intelligent transportation systems. However, existing models often struggle to jointly capture short-term fluctuations and long-term temporal dependencies under noisy and heterogeneous traffic conditions. To address this challenge, this paper proposes a hybrid traffic flow forecasting framework that integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the Mamba state-space model, and the Transformer architecture. The framework first applies DBSCAN to multidimensional traffic features to enhance traffic state representation and reduce noise. The prediction module alternates… More >