Oyku Berfin Mercan^1,2, Nezihe Turhan Turan³, Aytuğ Onan^4,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087209 - 15 September 2026

Abstract Biomedical texts naturally contain multiple biological and medical concepts within a document, resulting in a semantically rich and complex structure. Consequently, multi-label text classification (MLTC) has become a suitable framework for comprehensively modeling biomedical texts, including clinical reports, laboratory records, and scientific abstracts. However, relying solely on contextual language representations may be insufficient to explicitly reflect the broader scientific focus and conceptual orientation of a document. In this study, the MLTC problem in the biomedical domain is investigated using the Hallmarks of Cancer (HoC) dataset. Topic probability distributions obtained from CombinedTM are incorporated as an… More >

Chuanlin Jian¹, Yuanchen Sun², Xiangcheng Liu¹, Xuming Huang³, Samaneh Beheshti Kashi⁴, Xing Hu^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086441 - 15 September 2026

Abstract Heterogeneous clusters now carry most artificial-intelligence, scientific-computing, cloud, and edge-assisted workloads, yet scheduling them well remains difficult: workload semantics, hardware capability, queue state, and energy behavior are tightly coupled. Existing schedulers, whether heuristic, learning-based, or built on reinforcement learning, tend to rely on coarse resource requests, overlook the compatibility between task types and node classes, or carry a heavy training and deployment cost. This paper presents Classification-Aware Scheduling for Heterogeneous clusters (CASH), a confidence-calibrated, classification-aware scheduling framework that integrates workload profiling, deep feature crossing, gradient-boosted boundary modeling, and risk-aware heterogeneous resource mapping. CASH constructs a… More >

Hadeel Alsolai¹, Muhammad Waqas Ahmed², Bayan Alabdullah¹, Fatimah Alhayan¹, Mohammed Alonazi³, Ahmad Jalal^4,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085838 - 15 September 2026

Abstract Robust scene-level classification and semantic understanding from aerial and disaster-related imagery are essential for intelligent vision systems deployed in emergency response, UAV-based monitoring, and safety-critical environments. However, existing deep learning approaches, including convolutional neural networks and Vision Transformers (ViTs), often struggle to simultaneously capture fine-grained local object characteristics and global semantic scene context, while also lacking reliable uncertainty estimation mechanisms for trustworthy decision-making. To address these limitations, this paper proposes MS-SLCA-ViT, a novel multi-scale scene–local cross-attention Vision Transformer framework for robust and uncertainty-aware image scene understanding. The proposed architecture introduces three major contributions. First, a… More >

Yunis Torun^1,*, Burak Seckin¹, Rukiye Karakis²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085198 - 15 September 2026

Abstract Marble classification has traditionally relied on human visual inspection, where operators assess color, texture, and pattern alignment to determine quality. However, this manual process is subjective, inconsistent, and inefficient for large-scale industrial applications. To address these limitations, this study proposes DeepMarbleVision, a texture-aware ensemble deep learning framework with energy-layer-based feature fusion for marble quality classification. A real-world dataset was created using the MarbleVision system, including three marble quality classes acquired from an industrial marble classification environment. The proposed approach integrates energy-layer-based feature fusion into TCNN variants of AlexNet, ResNet, and DenseNet, which were initialized through… More >

Sheraz Ali Hassan¹, Hamid Masood Khan¹, Muhammad Javed¹, Mohd Faizal Bin Yusof², Jamil Abedalrahim Jamil Alsayaydeh^3,*, Fida Muhammad Khan⁴, Inam Ullah^5,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084547 - 15 September 2026

Abstract The increasing frequency of natural and human-induced disasters has intensified the need for reliable methods to identify crisis-relevant information from Twitter/X streams. However, tweets are often short, noisy, informal, ambiguous, and context-dependent, making disaster-related tweet classification challenging. This study proposes a lightweight attention-enhanced hybrid deep learning framework for binary disaster-related tweet classification. The framework integrates CNN-based local feature extraction, recurrent contextual modeling, static pre-trained word embeddings, class weighting, training-only data augmentation, and learned neural attention. Two architectures, CNN–LSTM–Attention and CNN–BiGRU–Attention, are evaluated on the labeled Kaggle Disaster Tweets dataset using a leakage-aware protocol in which More >

Anurag Sinha^1,*, Pranto Halder², Aditya Pandey³, Avi Mohan Kumar Shukla⁴, Shravan Kumar⁵, Ashutosh Rastogi⁶, Asima Akter Chowdhury⁶, Suryansh Rai⁷, Sagar Singh⁸

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 125-153, 2026, DOI:10.32604/jimh.2026.075373 - 14 September 2026

Abstract Accurate classification of pancreatic endocrinogenesis-related abnormalities in capsule endoscopy images remains challenging because of class imbalance, high intra-class variability, and limited annotated data. This study proposes an optimal data distribution framework based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) perturbations to enhance automated classification performance. The workflow combines perturbation-guided data augmentation, feature-importance analysis, and deep-learning classifiers, including convolutional neural networks (CNNs), U-Net, and You Only Look Once (YOLO). The approach is evaluated on the Kvasir-CapsuleSeg dataset using accuracy, macro F1-score, balanced area under the receiver operating characteristic curve (AUC), and confusion-matrix More >

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 >