Huiguo Wu^1,2,3, Yuedong Wu^1,2,3,*, Jian Liu^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083977 - 27 July 2026

Abstract The resilient modulus (M_R) is a key mechanical parameter in geotechnical engineering, but conventional laboratory measurement is time-consuming and labor-intensive. Deep learning models provide an alternative for predicting M_R using easily obtainable soil properties, yet their performance is often limited by the small size of available datasets. To address this limitation, this study develops an interpretable data-enhanced deep learning framework for M_R prediction. In the proposed framework, a multilayer perceptron (MLP) is adopted as the base prediction model, a generative adversarial network (GAN) is used to generate synthetic samples from the limited training data, Optuna is employed… More >

Rana Muhammad Adnan Ikram¹, Jing-Cheng Han^1,*, Ahmed A. Ewees², Mo Wang³, Ozgur Kisi^4,5,6,*, Salim Heddam⁷, Mohammad Zounemat-Kermani⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083313 - 27 July 2026

Abstract Two-phase optimized machine learning and deep learning models play a key role in enhancing the prediction accuracy of nonlinear time series modeling. This study assesses the performance of a novel two-phase optimized Long Short-Term Memory (LSTM) model with integration of Aquila Optimizer (AO) and Wild Horse Optimizer (WHO) in predicting monthly streamflow in a snow-fed catchment. The two-phase optimized LSTM-WHOAO model is compared with single-phase optimized models such as LSTM-GA (Genetic Algorithm), LSTM-GWO (Grey Wolf Optimizer), LSTM-WOA (Whale Optimization Algorithm), LSTM-AO, and LSTM-WHO. The outcomes acquired from the deep learning models were compared using four… More >

Mohammed Ansar Mohammed Manaz^1,*, Shang Ping Hong², Tzung-Lin Lee¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083270 - 27 July 2026

Abstract Silicon Carbide Metal Oxide Semiconductor Field Effect Transistors (SiC MOSFETs) have superior characteristics compared to traditional Silicon-based switching devices. SiC devices can support fast switching speeds and high blocking voltages. Due to limited historical data and rapid technological improvements, there is not enough field data to correctly evaluate the reliability of the state-of-the-art SiC MOSFETs. An accurate model of their reliability and aging characteristics is needed to expedite their rapid commercial adoption in mission-critical applications, such as offshore wind farms and electric vehicles. Classical handbook-based methods produce large errors due to their inability to correctly… More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084555 - 23 July 2026

Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >

Mohammed Hassan Alnemari^1,2,*, Abdelrahman Osman Elfaki³, Anas Bushnag¹, Mohamed Hussien Mohamed Nerma¹

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082976 - 23 July 2026

Abstract Sparse finite impulse response (FIR) filters reduce computational cost on resource-constrained devices, but selecting the sparsification threshold λ is typically left to grid search or hand tuning. We propose a two-stage method: a 67,331-parameter surrogate network predicts (Ap,As,S) (passband ripple in dB, stopband attenuation in dB, sparsity in %) from a filter specification and a candidate λ, and split conformal prediction (CP) calibrates ± intervals around each prediction. We then select λ by minimizing a worst-case penalty computed on the conservative ends of the intervals (the upper bound on Ap and… More >

Riya Sharma^1,*, Ashima Singh¹, Anju Bala¹, Mukesh Singh²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082720 - 23 July 2026

Abstract State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional… More >

Suchang Yang, Hongtao Yu^*, Ruiyang Huang, Huansha Wang, Ran Li, Junzheng Li

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082651 - 23 July 2026

Abstract Modeling dynamic graphs in continuous time is critical for applications such as user behavior prediction and recommendation systems. These models can effectively capture fine-grained and long-term temporal dependencies. However, existing approaches often suffer from high computational costs and optimization difficulties, especially when handling time-sorted neighborhood sequences over long horizons. In this work, we propose DyG-Hyena, a novel continuous-time dynamic graph learning framework that combines conditional variational autoencoder (CVAE)-assisted temporal modeling with efficient feature fusion. Our approach has two main innovations: (i) Efficient temporal fusion—we replace the Transformer with an improved, lightweight Hyena module to model More >

Yuxuan Feng, Lili Liang^*, Guanglu Sun, Yanrui Wei

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.081876 - 23 July 2026

Abstract In doped two-dimensional nanomaterials, magnetism is one of the important physical properties. By introducing foreign doping atoms or molecules, the electronic structure of the material can be effectively regulated, leading to changes in magnetic behavior. Currently, magnetic property prediction has achieved considerable results with the help of traditional CNNs, but there are still obvious limitations: (1) The feature extraction of dopant sites is constrained by fixed receptive fields, making it difficult to characterize local structural perturbations in the vicinity of dopant atoms and their spatial influence propagating to surrounding regions; (2) CNNs lack the capability… 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 >

Mohammad Q. Al-Jamal¹, Mahmoud Al Jamal², Bashar S. Khassawneh^3,*, Ayoub Alsarhan^4,5, Amina Salhi⁶, Tahani Alsubait⁷

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.079984 - 23 July 2026

Abstract Energy sustainability and secure operation are persistent challenges in Internet-of-Things (IoT) wireless sensor networks (WSNs), where limited battery capacity, heterogeneous traffic, and security procedures jointly drive premature node depletion and service degradation. This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust, energy-aware scheduling for clustered IoT-WSNs. At the lower level, a lightweight temporal predictor (TCN + LSTM with stochastic sampling) learns short-horizon residual-energy evolution from multivariate, dataset-aligned windows capturing sensing/communication activity, proximity-to-cluster-head effects, and security overhead (authentication latency, key exchange, and rekeying), and produces both point forecasts and uncertainty estimates to… More >