Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

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

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069327 - 10 November 2025

Abstract With the increasing growth of online news, fake electronic news detection has become one of the most important paradigms of modern research. Traditional electronic news detection techniques are generally based on contextual understanding, sequential dependencies, and/or data imbalance. This makes distinction between genuine and fabricated news a challenging task. To address this problem, we propose a novel hybrid architecture, T5-SA-LSTM, which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attention-enhanced (SA) Long Short-Term Memory (LSTM). The LSTM is trained using the Adam optimizer, which provides faster and more stable convergence compared… More >

Hefei Wang, Ruichun Gu^*, Jingyu Wang, Xiaolin Zhang, Hui Wei

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069251 - 10 November 2025

Abstract Graph Federated Learning (GFL) has shown great potential in privacy protection and distributed intelligence through distributed collaborative training of graph-structured data without sharing raw information. However, existing GFL approaches often lack the capability for comprehensive feature extraction and adaptive optimization, particularly in non-independent and identically distributed (NON-IID) scenarios where balancing global structural understanding and local node-level detail remains a challenge. To this end, this paper proposes a novel framework called GFL-SAR (Graph Federated Collaborative Learning Framework Based on Structural Amplification and Attention Refinement), which enhances the representation learning capability of graph data through a dual-branch… More >

Seerat Singla¹, Gunjan Shandilya¹, Ayman Altameem², Ruby Pant³, Ajay Kumar⁴, Ateeq Ur Rehman^5,*, Ahmad Almogren^6,*

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 4021-4057, 2025, DOI:10.32604/phyton.2025.073354 - 29 December 2025

Abstract Turmeric Leaf diseases pose a major threat to turmeric cultivation, causing significant yield loss and economic impact. Early and accurate identification of these diseases is essential for effective crop management and timely intervention. This study proposes DenseSwinGNNNet, a hybrid deep learning framework that integrates DenseNet-121, the Swin Transformer, and a Graph Neural Network (GNN) to enhance the classification of turmeric leaf conditions. DenseNet121 extracts discriminative low-level features, the Swin Transformer captures long-range contextual relationships through hierarchical self-attention, and the GNN models inter-feature dependencies to refine the final representation. A total of 4361 images from the… More >

Asma Batool¹, Fahad Ahmed¹, Naila Sammar Naz¹, Ayman Altameem², Ateeq Ur Rehman^3,4, Khan Muhammad Adnan^5,*, Ahmad Almogren^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4129-4152, 2025, DOI:10.32604/cmes.2025.073149 - 23 December 2025

Abstract Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, More >

Kangjie Li, Wenjing Ye^*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1665-1688, 2025, DOI:10.32604/cmes.2025.072447 - 26 November 2025

Abstract Recent progress in topology optimization (TO) has seen a growing integration of machine learning to accelerate computation. Among these, online learning stands out as a promising strategy for large-scale TO tasks, as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data. Stress-constrained lightweight design is an important class of problem with broad engineering relevance. Most existing frameworks use pixel or voxel-based representations and employ the finite element method (FEM) for analysis. The limited continuity across finite elements often compromises the accuracy of stress evaluation. To overcome this… More >

Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2573-2599, 2025, DOI:10.32604/cmes.2025.070426 - 26 November 2025

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >

Sadia Fatima¹, Fadl Dahan^2,*, Jamal Hussain Shah¹, Refan Almohamedh², Mohammed Aloqaily², Samia Riaz¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 971-994, 2025, DOI:10.32604/cmes.2025.070272 - 30 October 2025

Abstract The human gastrointestinal (GI) tract is influenced by numerous disorders. If not detected in the early stages, they may result in severe consequences such as organ failure or the development of cancer, and in extreme cases, become life-threatening. Endoscopy is a specialised imaging technique used to examine the GI tract. However, physicians might neglect certain irregular morphologies during the examination due to continuous monitoring of the video recording. Recent advancements in artificial intelligence have led to the development of high-performance AI-based systems, which are optimal for computer-assisted diagnosis. Due to numerous limitations in endoscopic image… More >

Markapudi Bhanu Prasad¹, Borhen Louhichi², Santosh Kumar Sahu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 483-519, 2025, DOI:10.32604/cmes.2025.069395 - 30 October 2025

Abstract This study investigates the mechanical behavior of polyurethane (PU) nanocomposites reinforced with nanodiamonds (NDs) and proposes an integrated optimization–prediction framework that combines the Taguchi method with machine learning (ML). The Taguchi design of experiments (DOE), based on an L9 orthogonal array, was applied to investigate the influence of composite type (pure PU, 0.1 wt.% ND, 0.5 wt.% ND), temperature (145°C–165°C), screw speed (50–70 rpm), and pressure (40–60 bar). The mechanical tests included tensile, hardness, and modulus measurements, performed under varying process parameters. Results showed that the addition of 0.5 wt.% ND substantially improved PU performance,… More >

Noman Khan¹, Afnan², Mi Young Lee^3,*, Jakyoung Min^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2935-2958, 2025, DOI:10.32604/cmc.2025.069058 - 23 September 2025

Abstract Fine-grained dog breed classification presents significant challenges due to subtle inter-class differences, pose variations, and intra-class diversity. To address these complexities and limitations of traditional handcrafted approaches, a novel and efficient two-stage Deep Learning (DL) framework tailored for robust fine-grained classification is proposed. In the first stage, a lightweight object detector, YOLO v8N (You Only Look Once Version 8 Nano), is fine-tuned to localize both the head and full body of the dog from each image. In the second stage, a dual-stream Vision Transformer (ViT) architecture independently processes the detected head and body regions, enabling… More >