Joong-Hyun Park¹, Jiho Choi², Libor Mesicek³, Hoon Ko^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084659 - 23 July 2026

Abstract With the rapid expansion of the Internet of Medical Things (IoMT), the importance of digital identity–based security has significantly increased. However, conventional static authentication mechanisms are insufficient to effectively address various identity misuse and abuse attacks. In this study, we model digital identity as a dynamic security entity and propose an AI-based framework that integrates a risk scoring model—combining unsupervised anomaly detection with context-aware analysis—and a multi-level risk-adaptive access control mechanism (Permit, Step-Up, Restrict). Experimental results using an extended version of the CERT Insider Threat Dataset tailored for IoMT environments provide proof-of-concept evidence that the More >

Shih-Lin Lin^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084571 - 23 July 2026

Abstract Connected automated driving increasingly relies on cooperative perception from onboard sensors, roadside units (RSUs), smart traffic lights, and vehicle-to-everything (V2X) links, but communication uncertainty can concentrate residual risk on vulnerable road users (VRUs). This study proposes an Ethical-Improved risk-allocation objective for edge-assisted Internet of Vehicles (IoV) cooperative autonomous driving. The objective internalizes responsibility as a bounded risk weight, normalizes equality and maximin terms, and adds explicit VRU tail-risk and VRU/Ego ratio penalties. The evaluation is organized into two strictly separated tracks. In the planner-objective track, Ethical-Improved reduces physical collision rate, aggregate harm, inequality, and VRU More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084555 - 23 July 2026

Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal^2,*, Mohammed K. Alzaylaee³, Heba G. Mohamed^4,5

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084409 - 23 July 2026

Abstract The proliferation of Internet of Things (IoT) devices has introduced unprecedented security challenges, necessitating efficient intrusion detection systems (IDS) capable of operating under severe resource constraints. This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms, using an ARM Cortex-M4 deployment target as a reference. We evaluate FP32, FP16, and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy, model size, estimated inference latency, estimated energy consumption, and adversarial robustness. INT8-quantized model achieves 99.10% accuracy on clean data while maintaining 97.50%… More >

Ying Cao¹, Penghui Zhao¹, Xinyu Qiao¹, Ningfan Zhan¹, Xiaomei Zou^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026

Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >

Wenchang Yu¹, Xiaoqin Ma^1,2, Zheqing Zhang¹, Kezhong Lu^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083713 - 23 July 2026

Abstract Feature selection grounded in neighborhood rough sets has attracted sustained research attention owing to its principled treatment of classification uncertainty. However, existing forward greedy algorithms typically evaluate uncertainty over the entire object universe at each iteration, resulting in prohibitive computational complexity on large-scale datasets. To address this inefficiency, we introduce a new uncertainty index built upon Boundary Object Sets (BOS). BOS are defined as objects whose neighborhood granules intersect with multiple decision classes, thereby capturing intrinsic classification ambiguity. The proposed measure quantifies the proportion of these boundary objects relative to the total universe size. Grounded More >

Ruijia He¹, Yixin Hu², Jinze Liu³, Qixin Zhang⁴, Duo Peng⁵, Yanyan Chen^5,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083677 - 23 July 2026

Abstract Transthoracic Echocardiography (TTE) often suffers from a limited field of view (FoV), which may obscure peripheral cardiac structures and hinder comprehensive visual assessment. Although FoV extension can be formulated as outpainting, public studies on echocardiographic FoV outpainting remain scarce and are mainly represented by cGAN-based reconstruction methods such as echoGAN. However, in noisy ultrasound images with weak boundaries, the challenge is not only realistic texture synthesis, but also structural continuity across the observed–generated boundary. To address this issue, we propose a structure-aware diffusion framework for echocardiographic FoV outpainting. To the best of our knowledge, this More >

Nada Alzaben¹, Muhammad I. Khan², Hafeez Ur Rehman Siddiqui³, Abeer Rashad Mirdad⁴, Saeed Ali Bahaj^5,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083649 - 23 July 2026

Abstract Industrial predictive maintenance is a critical challenge in modern manufacturing, where unexpected equipment failures cause significant economic losses through downtime, repair costs, and disrupted production. Conventional maintenance approaches, whether reactive or schedule-based, are becoming inadequate to manage the high-dimensional sensor information of the IoT-enabled machineries. The paper presents a novel hybrid neuro-symbolic digital twin that builds upon Remaining Useful Life (RUL) estimation by combining temporal transformers, physics-informed constraints, and counterfactual reasoning. The model integrates complementary approaches into a single and interpretable predictive system. A temporal transformer backbone is a model of long-range dependencies in multivariate… More >

Na Li¹, Yashu Zhang¹, Fengpu Lin¹, Liutao Zhao^2,*, Zhongshan Zhu³, Chen Tom⁴, Tengfei Tu⁵

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083412 - 23 July 2026

Abstract Accurate mapping of video imagery to physical space coordinates represents a fundamental challenge in dynamic target tracking and intelligent video analysis systems. Traditional methods struggle to maintain stable coordinate mapping in real-time video streams due to imaging distortion variations and changing environmental conditions. This paper presents a real-time coordinate mapping approach that integrates geometric constraints with online distortion correction to achieve stable pixel-to-target coordinate transformation for video target tracking applications. The proposed method introduces a planar geometric consistency constraint and an online distortion parameter update mechanism within a unified optimization framework, enabling adaptive adjustment of… More >

Khanh Nguyen-Trong^1,*, Tan Nguyen-Thi-Thanh²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083365 - 23 July 2026

Abstract Automated wood surface defect detection is difficult to evaluate reliably because defects are often small, low-contrast, and visually confounded by natural wood texture, while reported performance can vary substantially with benchmark design and domain shift. To address this issue, we conduct a comparative study across three practically relevant settings: a curated seven-class benchmark, a broader in-domain seven-class protocol derived from the same source dataset, and supervised adaptation to a low-resource Vietnamese target domain. We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network (Faster R-CNN) with MobileNetV3-FPN against a compact You Only Look… More >