Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, Yongqiang Zhang⁷, Sameh A. Kantoush⁸, Mekonnen Gebremichael⁹, Ruhollah Taghizadeh-Mehrjardi¹⁰, Jinhui Jeanne Huang¹¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074768 - 29 January 2026

Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… More >

Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Ans D. Alghamdi^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-35, 2026, DOI:10.32604/cmc.2025.071210 - 09 December 2025

Abstract Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities. Current approaches struggle with cross-modal alignment, temporal consistency, and robust handling of noisy or incomplete inputs across multiple modalities. We propose MultiAgent-Chain of Thought (CoT), a novel multi-agent chain-of-thought reasoning framework where specialized agents for text, vision, and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms. Our architecture incorporates self-reflection modules, conflict resolution protocols, and dynamic rationale alignment to enhance consistency, factual accuracy, and user engagement. More >

Zhongyun Tang^1,2,3, Hanyi Xu², Haiyang Hu^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… 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 >

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 >

Yingui Qiu¹, Enming Li^1,2,*, Pablo Segarra², Bin Xi³, Jian Zhou¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1607-1629, 2025, DOI:10.32604/cmes.2025.068211 - 31 August 2025

Abstract With the growing demand for sustainable development in the mining industry, cemented paste backfill (CPB) materials, primarily composed of tailings, play a crucial role in mine backfilling and underground support systems. To enhance the mechanical properties of CPB materials, fiber reinforcement technology has gradually gained attention, though challenges remain in predicting its performance. This study develops a hybrid model based on the adaptive equilibrium optimizer (adap-EO)-enhanced XGBoost method for accurately predicting the uniaxial compressive strength of fiber-reinforced CPB. Through systematic comparison with various other machine learning methods, results demonstrate that the proposed hybrid model exhibits… More >

Baishun Zhou^1,2, Haijiao Zhao³, Yuxin Wen², Gangyi Ding¹, Ying Xing^3,*, Xinyang Lin⁴, Lei Xiao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5201-5221, 2025, DOI:10.32604/cmc.2025.065726 - 30 July 2025

Abstract In recent years, with the rapid development of software systems, the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics. Defect prediction methods based on software metric elements highly rely on software metric data. However, redundant software metric data is not conducive to efficient defect prediction, posing severe challenges to current software defect prediction tasks. To address these issues, this paper focuses on the rational clustering of software metric data. Firstly, multiple software projects are evaluated to determine the preset number… More >

Shuqin Zhang¹, Zihao Wang^1,*, Xinyu Su²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5781-5809, 2025, DOI:10.32604/cmc.2025.062080 - 19 May 2025

Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… More >