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 >

Xinrui Ma, Shizhi Chen^*

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1745-1767, 2025, DOI:10.32604/sdhm.2025.071148 - 17 November 2025

Abstract The rapid and accurate assessment of structural damage following an earthquake is crucial for effective emergency response and post-disaster recovery. Traditional manual inspection methods are often slow, labor-intensive, and prone to human error. To address these challenges, this study proposes STPEIC (Swin Transformer-based Framework for Interpretable Post-Earthquake Structural Classification), an automated deep learning framework designed for analyzing post-earthquake images. STPEIC performs two key tasks: structural components classification and damage level classification. By leveraging the hierarchical attention mechanisms of the Swin Transformer (Shifted Window Transformer), the model achieves 85.4% accuracy in structural component classification and 85.1% More >

Mubeen Sabir¹, Zeshan Aslam Khan^2,*, Muhammad Waqar², Khizer Mehmood¹, Muhammad Junaid Ali Asif Raja³, Naveed Ishtiaq Chaudhary⁴, Khalid Mehmood Cheema⁵, Muhammad Asif Zahoor Raja⁴, Muhammad Farhan Khan⁶, Syed Sohail Ahmed⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 807-855, 2025, DOI:10.32604/cmes.2025.068131 - 30 October 2025

Abstract Fingerprint classification is a biometric method for crime prevention. For the successful completion of various tasks, such as official attendance, banking transactions, and membership requirements, fingerprint classification methods require improvement in terms of accuracy, speed, and the interpretability of non-linear demographic features. Researchers have introduced several CNN-based fingerprint classification models with improved accuracy, but these models often lack effective feature extraction mechanisms and complex multineural architectures. In addition, existing literature primarily focuses on gender classification rather than accurately, efficiently, and confidently classifying hands and fingers through the interpretability of prominent features. This research seeks to… More >

Muhammad Sajid Farooq¹, Muhammad Saleem², M.A. Khan^3,4, Muhammad Farrukh Khan⁵, Shahan Yamin Siddiqui⁶, Muhammad Shoukat Aslam⁷, Khan M. Adnan^8,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5183-5206, 2025, DOI:10.32604/cmc.2025.069641 - 23 October 2025

Abstract The rapid evolution of smart cities through IoT, cloud computing, and connected infrastructures has significantly enhanced sectors such as transportation, healthcare, energy, and public safety, but also increased exposure to sophisticated cyber threats. The diversity of devices, high data volumes, and real-time operational demands complicate security, requiring not just robust intrusion detection but also effective feature selection for relevance and scalability. Traditional Machine Learning (ML) based Intrusion Detection System (IDS) improves detection but often lacks interpretability, limiting stakeholder trust and timely responses. Moreover, centralized feature selection in conventional IDS compromises data privacy and fails to… More >

Sari Masri, Ahmad Hasasneh^*

Computer Systems Science and Engineering, Vol.49, pp. 481-497, 2025, DOI:10.32604/csse.2025.068616 - 10 October 2025

Abstract Diagnosing dental disorders using routine photographs can significantly reduce chair-side workload and expand access to care. However, most AI-based image analysis systems suffer from limited interpretability and are trained on class-imbalanced datasets. In this study, we developed a balanced, transformer-based pipeline to detect three common dental disorders: tooth discoloration, calculus, and hypodontia, from standard color images. After applying a color-standardized preprocessing pipeline and performing stratified data splitting, the proposed vision transformer model was fine-tuned and subsequently evaluated using standard classification benchmarks. The model achieved an impressive accuracy of 98.94%, with precision, recall and F1 scores More >

Areeba Masood Siddiqui^1,2,*, Hyder Abbas^3,4, Muhammad Asim^5,6,*, Abdelhamied A. Ateya⁵, Hanaa A. Abdallah⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3135-3168, 2025, DOI:10.32604/cmes.2025.069926 - 30 September 2025

Abstract Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors. Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance. To address these challenges, we propose a novel Squid Game Optimization-Dimension Reduction-based Ensemble (SGO-DRE) method for the precise diagnosis of skin diseases. Our approach begins by selecting pre-trained models named MobileNetV1, DenseNet201, and Xception for robust feature extraction. These models are enhanced with dimension reduction blocks to improve efficiency. To tackle the aggregation problem of various models, we leverage the Squid Game Optimization… More >

Nouman Ahmad^*, Changsheng Zhang

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3321-3334, 2025, DOI:10.32604/cmc.2025.067044 - 23 September 2025

Abstract Source code vulnerabilities present significant security threats, necessitating effective detection techniques. Rigid rule-sets and pattern matching are the foundation of traditional static analysis tools, which drown developers in false positives and miss context-sensitive vulnerabilities. Large Language Models (LLMs) like BERT, in particular, are examples of artificial intelligence (AI) that exhibit promise but frequently lack transparency. In order to overcome the issues with model interpretability, this work suggests a BERT-based LLM strategy for vulnerability detection that incorporates Explainable AI (XAI) methods like SHAP and attention heatmaps. Furthermore, to ensure auditable and comprehensible choices, we present a… More >

Habib Hadj-Mabrouk^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4399-4430, 2025, DOI:10.32604/cmc.2025.067143 - 30 July 2025

Abstract A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to… More >

Jianping Li, Wan Xiong, Tenghang Zhang, Hao Cheng, Kun Shen, Miaojin He, Yu Zhang, Junxin Song, Ying Deng^*, Qiaowang Chen^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2189-2216, 2025, DOI:10.32604/cmc.2025.066128 - 03 July 2025

Abstract Tungsten carbide-based (WC-based) cemented carbides are widely recognized as high-performance tool materials. Traditionally, single metals such as cobalt (Co) or nickel (Ni) serve as the binder phase, providing toughness and structural integrity. Replacing this phase with high-entropy alloys (HEAs) offers a promising approach to enhancing mechanical properties and addressing sustainability challenges. However, the complex multi-element composition of HEAs complicates conventional experimental design, making it difficult to explore the vast compositional space efficiently. Traditional trial-and-error methods are time-consuming, resource-intensive, and often ineffective in identifying optimal compositions. In contrast, artificial intelligence (AI)-driven approaches enable rapid screening and… More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >