Ali Raza^1,*, Umar Shafique¹, Marek Lampart¹, Dumitru Baleanu², Emad Fadhal³, Hadil Alhazmi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084939 - 27 July 2026

Abstract Kawasaki disease (KD) is an acute, self-limited pediatric vasculitis of unknown etiology and is one of the leading causes of acquired coronary artery complications in children. Endothelial dysfunction, vascular endothelial growth factor (VEGF) activity, adhesion molecule/chemokine activation, and inflammatory cytokine responses play important roles in its pathogenesis. This paper presents a delay differential equation model with stochastic perturbations to study lesion-level inflammatory mechanisms involved in Kawasaki disease pathogenesis. The model describes interactions among healthy endothelial cells, vascular endothelial growth factor (VEGF), adhesion molecules/chemokines, and inflammatory cytokine activity. Mathematically, endothelial-cell injury promotes VEGF production, VEGF contributes… More >

Emrah Altun^1,*, Christophe Chesneau², Atacan Erdis¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083459 - 27 July 2026

Abstract Modeling bounded response variables is an important problem in computational statistics, especially in applications involving skewed, heavy-tailed data. In such cases, the modal regression is a robust alternative to traditional mean-based modeling approaches. In this study, a new bounded distribution, called the extended Bradford distribution, is proposed as a flexible extension of the classical Bradford distribution. By incorporating an additional shape parameter, the corresponding model can capture various shape structures, such as left and right skewness, increasing, and bathtub hazard shapes. The new distribution provides an explicit expression for the mode, making it suitable for More >

Yiyan Zhang^1,*, Yi Xin², Qin Li²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.082841 - 27 July 2026

Abstract Medical data has specificity compared to other fields of data, and the description of medical data characteristics is still in a qualitative stage. This study included 293 sub-datasets of 138 independent datasets. First, data preprocessing was performed using methods such as incomplete data removal, inconsistent data normalization, and data integration. Then, the characteristics of 293 research datasets were quantified using 26 indicators in three categories: simple indicators, statistical indicators, and informational indicators. Furthermore, statistical analysis was performed on the above-mentioned quantitative characteristics, and stepwise regression and decision tree methods were used for modeling learning. The… More >

Manal Ghannam¹, Bilgen Kaymakamzade^1,2, Muhammad Farman^1,3,4, Kottakkaran Sooppy Nisar^5,*, Mohammed Altaf Ahmed⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081075 - 27 July 2026

Abstract In Vitro Fertilization (IVF) has been a major medical advancement in the field of fertility treatment. It has helped millions of individuals and couples overcome infertility by providing a workable option. It involves removing eggs from the ovaries of a female, fertilizing those eggs with male sperm in a monitored lab condition. In this work, we developed a new model to show the success of In Vitro Fertilization rates in women through a fractional-order compartmental modeling framework by using real data. The developed model is analyzed statistically, and the biological feasibility of the model. The Lipschitz condition,… More >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

Jing-Wen Gao^1,2, Yunan He^1,*, Jian Liu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084503 - 23 July 2026

Abstract The unique topological properties of the electronic band structures in topological materials have increasingly attracted attention in both fundamental research and next-generation technological applications. With the rise of machine learning, the connection between topological materials and machine learning has deepened significantly. This review systematically summarizes the interaction between these two fields, tracing the history of their mutual promotion and synergistic development. We further examine the transformative impact of machine learning across multiple domains of topological materials, with a particular focus on recent progress in inverse design and generation of topological materials, topological superconductivity, and the More >

Ying Cao¹, Penghui Zhao¹, Xinyu Qiao¹, Ningfan Zhan¹, Xiaomei Zou^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026

Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >

Akash Shah¹, Mudasir Ahmad Wani^2,*, Ravi Prakash Chaturvedi³, Shri Kant³, Nidhi Sindhwani¹, Kashish Ara Shakil⁴, Sulieman Alshuhri²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082713 - 23 July 2026

Abstract Membership Inference Attacks (MIAs) pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training, particularly in sensitive domains such as social media and mental health analytics. To address this challenge, this paper proposes HEbdMIA, a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications. The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs. Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction More >

Israt Jahan¹, Afsana Begum¹, Bibhas Roy Chowdhury Piyas^1,*, Fahmid Al Farid^2,3,*, Fatama Jannat Tisha¹, Shahrin Islam¹, Abu Saleh Musa Miah⁴, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081631 - 23 July 2026

Abstract Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare. Despite advances in cardiology, early-stage cardiovascular disease often remains undetected, which hinders timely intervention and leads to preventable deaths. To overcome this problem, this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease (CVD). Initially, this study examined several data-balancing strategies, for example, SMOTE (Synthetic Minority Over-sampling Technique), SMOTETomek (Synthetic Minority Over-sampling Technique + Tomek Links), Tomek Links, ADASYN (Adaptive Synthetic Sampling), and SMOTE-ENN (Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors) within the data-preprocessing pipeline. We… More >

Pardis Sadatian Moghaddam¹, Mahyar Mahmoudi², Nuria Serrano³, Francisco Hernando-Gallego⁴, Diego Martín^3,*, José Vicente Álvarez-Bravo³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081311 - 23 July 2026

Abstract The rapid proliferation of the Internet of Things (IoT) and cyber-physical systems (CPS) within critical infrastructure sectors has significantly expanded the attack surface for advanced and stealthy cyber threats. Since these systems increasingly rely on real-time data exchange and autonomous control, developing intelligent, scalable, and adaptive anomaly detection mechanisms has become a pressing requirement. This paper proposes a novel hybrid framework, evolutionary-transformer-long short-term memory (Evo-Transformer-LSTM), that integrates the temporal modeling capability of LSTM networks, the global attention mechanism of Transformer encoders, and the optimization power of the improved chimp optimization algorithm (IChOA) for hyper-parameter tuning.… More >