Hamza Murad Khan¹, Shakila Basheer², Mohammad Tabrez Quasim³, Raja`a Al-Naimi⁴, Vijaykumar Varadarajan⁵, Anwar Khan^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.087222 - 23 July 2026

Abstract This article has no abstract. More >

Zhuodong Liu¹, Xiangyu Li^2,*, Zhihao Zhang¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.085409 - 23 July 2026

Abstract Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR). However, most existing FU methods are designed for the FedAvg paradigm, where all clients share a single global model. In practice, personalized federated learning (pFL) methods such as FedPer, FedRep, Ditto, and FedBN have become widely adopted due to their superior handling of non-IID data. These methods decompose the model into shared global layers and client-specific personalized layers, fundamentally altering the semantics of unlearning, yet this setting has… More >

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 >

Jing-Wen Gao^1,2, Yunan He^1,*, Jian Liu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084503 - 23 July 2026

Abstract The unique topological properties of the electronic band structures in topological materials have increasingly attracted attention in both fundamental research and next-generation technological applications. With the rise of machine learning, the connection between topological materials and machine learning has deepened significantly. This review systematically summarizes the interaction between these two fields, tracing the history of their mutual promotion and synergistic development. We further examine the transformative impact of machine learning across multiple domains of topological materials, with a particular focus on recent progress in inverse design and generation of topological materials, topological superconductivity, and the More >

Yixiang Wan, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084474 - 23 July 2026

Abstract Addressing the two critical challenges of missed detection of distant small targets and difficulty in identifying occluded targets under complex road conditions, this paper proposes YOLO-PBE, an improved high-precision vehicle detection model based on YOLOv11n. First, to tackle the fine-grained feature loss caused by conventional strided convolutions during downsampling, we add a high-resolution P2 detection layer and introduce SPD-Conv, a lossless spatial-to-depth feature transformation technique, for feature extraction. By preserving complete pixel-level information, the model's perception accuracy for distant small vehicles is enhanced. For feature fusion, we design an improved BiFPN incorporating a Ghost module.… 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 >

Amit Pimpalkar^1,*, Kapil N. Vhatkar², Rachna K. Somkunwar³, Shweta Koparde⁴, Dalia H. Elkamchouchi⁵, Ateeq Ur Rehman^6,*, Pooja Verma⁷, Salil Bharany⁸

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084384 - 23 July 2026

Abstract Plants are fundamental to global food security; however, plant diseases significantly reduce agricultural productivity, making early and accurate detection essential. Traditional inspection approaches rely heavily on manual observation, which is labor-intensive, subjective, difficult to scale, and susceptible to human error. In contrast, artificial intelligence (AI) combined with computer vision (CV) offers an effective solution for early-stage disease detection, minimizing yield losses while overcoming the limitations of manual monitoring systems. In this study, a novel deep learning architecture, the Swin Transformer with Harmonic Densely Connected Network (STHarDNet), is proposed. The framework integrates a Swin Transformer (ST)… More >

Zhihao Dong^1,2, Huakui Sun^1,2,*, Yueyue Tao², Daosen Zhai²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084301 - 23 July 2026

Abstract High-mobility Unmanned Aerial Vehicle (UAV) swarm networks suffer from fast-varying connectivity and interference, and therefore routing decisions must jointly account for link instability and topology changes. By leveraging mobile edge computing (MEC) capabilities, each UAV can perform online routing decisions locally without relying on centralized controllers. This paper develops a Predictive-Q learning framework for dynamic routing under interference and mobility, where the Q-value is trained by a multi-factor reward that explicitly models retransmission costs, predicts link lifetime from relative motion, and anticipates forward connectivity and neighbor redundancy. To further enhance reliability under harsh interference, we More >