Mrugendrasinh Rahevar¹, Martin Parmar¹, Hemant Yadav¹, Chun-Ta Li^2,*, Agbotiname Lucky Imoize³, Hiren Mewada⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085393 - 28 August 2026

Abstract Quantum kernel techniques aim to leverage quantum computational capabilities on social data. However, their application to natural language processing tasks faces formidable obstacles, such as extreme dimensionality reduction ($D=384\to k=8$), concentration of measure in quantum feature spaces, and the lack of theoretical understanding of when quantum advantages occur in kernel-based text classification. Filling this gap, we provide a comprehensive study of quantum kernels for text classification that addresses three major challenges in existing studies: general data compression approaches that ignore class structure, the lack of a predictive diagnostic toolkit, and overlooked approaches for handling concentration… More >

Mengyao Wang¹, Ailian Zhu², Longji Zhu^3,*, Chen Lan^4,*, Yang Li⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085279 - 28 August 2026

Abstract Accurate prediction of asphalt pavement distress is essential for proactive maintenance and life-cycle infrastructure management. However, existing data-driven methods often struggle to jointly represent multi-source inspection data, distress evolution mechanisms, and spatial propagation relationships among pavement sections. To address these limitations, this study proposes a Dual-Layer Progressive Physics-Constrained Graph Neural Network (DLPC-GNN) for asphalt pavement distress prediction and maintenance strategy classification. The proposed model represents pavement deterioration using a dual-layer graph structure. At the microscopic level, cracks, surface deterioration, and structural moisture-induced damage are modeled as physically associated distress nodes. At the macroscopic level, pavement-section… More >

Pingan Peng^1,2, Qi Zhang^1,*, Ya Liu¹, Yue Han¹, Zhida Jiang¹, Linli Chen¹, Xuefeng Huo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.083624 - 28 August 2026

Abstract Unfilled goafs located beneath urban areas pose a significant threat to surface safety, and microseismic monitoring is an important tool for capturing rock-mass microfractures and supporting early warning. However, under field conditions, existing sequential models face challenges such as high computational complexity and deep feature degradation when deployed on edge devices, primarily due to the non-stationary characteristics of microseismic signals and the presence of complex ambient background noise. To address these issues, this paper proposes the Hierarchical Network with Manifold Hybrid Connection (H-NET-mHC), a lightweight manifold-aware state space model designed for edge computing. The model… More >

Attilio Barretta^1,2,*, Angelo Mottaran¹, Nicolas Carl², Francesco Prata^2,3, Sara Tamburini^1,2, Edoardo Beatrici², Mario De Angelis^2,4, Francesco Cei^2,4, Natali Rodriguez Peñaranda², Francesco Pepillo², Alessio Guidotti², Vincenzo Cavarra², Claudio Brancelli², Pietro Pasquini², Pietro Piazza¹, Cristian Vincenzo Pultrone¹, Hussam Dababneh¹, Lorenzo Bianchi¹, Alessandro Larcher⁴, Alexandre Mottrie², Rocco Papalia³, Riccardo Schiavina¹

Canadian Journal of Urology, Vol.33, No.4, pp. 783-793, 2026, DOI:10.32604/cju.2026.078354 - 21 August 2026

Abstract Objectives: Complex cystic renal lesions pose a significant diagnostic challenge in the preoperative assessment of malignancy. Although percutaneous renal mass biopsy is well established for solid tumours diagnosis, its role in cystic lesions remains controversial. This systematic review aims to evaluate the diagnostic performance, safety, and clinical impact of percutaneous sampling—fine-needle aspiration (FNA) and core needle biopsy (CNB)—in Bosniak III–IV renal cysts. Methods: A systematic review and meta-analysis were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [International Prospective Register of Systematic Reviews (PROSPERO) ID CRD420251124563]. PubMed/MEDLINE (Medical Literature… More >

Animaw Kerie Aseres^1,2,*, Asrat Mulatu Beyene^3,2, Lemlem Kassa Tegegne^1,2

Journal of Cyber Security, Vol.8, pp. 559-576, 2026, DOI:10.32604/jcs.2026.086559 - 21 August 2026

Abstract Conventional multi-factor and one-time authentication approaches, such as passwords and one-time passwords (OTPs), have become increasingly vulnerable to advanced attack methods, motivating the need for continuous authentication (CA) systems that can verify user identity throughout an active session rather than only at login. For such a system to be effective, it must analyze user behavior reliably and in real time. This paper presents a novel approach to implementing CA on mobile devices using tap and swipe behavioral biometrics combined with machine learning (ML) and multimodal fusion. The dataset was collected from 400 volunteer participants using… More >

Amine Gonca Toprak^1,*, Aytuğ Onan²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085268 - 13 August 2026

Abstract Biomedical texts present significant challenges for natural language processing (NLP) due to their complex terminology, intricate contextual dependencies, and highly domain-specific semantics. This study investigates the effectiveness of knowledge distillation (KD) for biomedical text classification, aiming to develop lightweight, resource-efficient models that remain competitive with larger architectures. A balanced dataset of 25,000 PubMed records was constructed, equally distributed across five biomedical domains. Two teacher models (BERT and PubMedBERT) and five student models (DistilBERT, BioClinicalBERT, BioBERT, DistilBioBERT, and DistilRoBERTa) were evaluated across ten distinct KD configurations. Each student model was also directly fine-tuned to serve as… More >

Muhammad Naeem Zafar¹, Yunfei Yin^1,*, Junaid Abbas², Bayan Alabdullah³, Khaled Alnowaiser⁴, Yunyoung Nam⁵, Zepa Yang^5,*

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085136 - 13 August 2026

Abstract Accurate medical image classification increasingly relies on the joint modeling of global contextual semantics and fine-grained local structural cues, since many lesions are only reliably recognized when subtle local details are interpreted within their broader anatomical context. However, most recent hybrid CNN–Transformer and global–local frameworks still extract these features in separate streams and merge them only through late-stage static fusion, without explicit bidirectional interaction during representation learning. As a result, global context cannot effectively guide the refinement of subtle local structures, and local discriminative cues cannot recalibrate higher-level semantic reasoning before classification, which limits reciprocal… More >

Ye Lu¹, Haoyang Hu^1,*, Wenyi Chang¹, Wanbin Liu¹, Wenyuan Zhang²

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084490 - 13 August 2026

Abstract Fine-grained multiclass intrusion detection over flow-level traffic remains difficult, largely because class boundaries are often entangled, temporal dependence is non-negligible, and the label distribution is heavily long-tailed. In this study, a compact temporal convolutional network (TCN)-bidirectional gated recurrent unit (BiGRU)-TinyTransformer framework is developed to bring these issues into a single modeling pipeline: the TCN branch focuses on short-range anomalous patterns, the BiGRU branch captures bidirectional temporal structure, and the TinyTransformer branch complements them with broader contextual interaction learning. To reduce the bias induced by extreme imbalance, training is not driven by a single correction mechanism, More >

Ren-Jieh Kuo^1,*, Muhammad Rizki¹, Ferani Eva Zulvia², Eddy Roflin³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084436 - 13 August 2026

Abstract Imbalanced data remain a critical challenge in classification, as skewed distributions bias models toward majority classes and diminish sensitivity to minority classes, which are often the most critical. To address this issue, this paper proposes the Information Filtered Hybrid Algorithm (IF-HA), a novel entropy-based sampling method that integrates undersampling and oversampling guided by information theory. IF-HA quantifies instance importance through an instance-wise difference statistic. In the undersampling stage, majority of instances with low difference statistics in the border area are eliminated, while in the oversampling stage, synthetic samples are generated from two minority core points… More >

Shunran Duan, Meijuan Yin^*, Xiangyang Luo, Lunchong Cui, Chenyu Wang

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.084290 - 13 August 2026

Abstract Few-shot relation classification aims to identify semantic relations between entity pairs under limited annotated data. Although recent prompt learning-based methods have achieved promising performance, they often rely on manually crafted, domain-specific prompt templates, which restrict their transferability across domains. In this paper, building upon the multi-task prompt transfer paradigm of MPT, we propose a Confidence-guided Orthogonality-constraint Prompt Adaptation framework for few-shot relation classification, named COPA. The proposed framework learns a shared domain-invariant prompt matrix together with domain-specific low-rank prompt matrices via multi-domain soft prompt tuning, enabling the transfer of domain-invariant relational knowledge across domains. Unlike… More >