Shekaina Justin^1,*, Wafaa Saleh², Hind Mohammed Albalawi³, J. Shermina⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5469-5487, 2025, DOI:10.32604/cmc.2025.066888 - 23 October 2025

Abstract Solar radiation is the main source of energy on Earth and plays a major role in the hydrological cycles, surface radiation balance, weather and climate changes, and vegetation photosynthesis. Accurate solar radiation prediction is of paramount importance for both climate research and the solar industry. This prediction includes forecasting techniques and advanced modeling to evaluate the amount of solar energy available at a specific location during a given period. Solar energy is the cheapest form of clean energy, and due to the intermittent nature of the energy, accurate forecasting across multiple timeframes is necessary for… More >

Qiuze Yao, Zhensheng Wu, Xiang Peng^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.2, pp. 1-1, 2025, DOI:10.32604/icces.2025.011507

Abstract Fiber-reinforced composites are widely applied in various fields due to their high strength and modulus, and analyzing their physical field is crucial for improving material performance and structural design. However, traditional analysis methods, such as finite element analysis (FEM) and numerical computation are still limited by computational efficiency and accuracy when applied to microstructures. To address this challenge, convolutional neural network (CNN) approaches are being developed to quickly and accurately predict the physical fields in fiber-reinforced composites. Under static loading, the U-Net framework is developed with an adaptive two-stage training approach to address the generalization… More >

Kadriye Simsek Alan^*, Ayca Durgut, Helin Doga Demirel

Journal on Artificial Intelligence, Vol.7, pp. 397-415, 2025, DOI:10.32604/jai.2025.071133 - 20 October 2025

Abstract Efficient utilization of processor and memory resources is essential for sustaining performance and energy efficiency in modern computing infrastructures. While earlier research has emphasized CPU utilization forecasting, joint prediction of CPU and memory usage under real workload conditions remains underexplored. This study introduces a machine learning–based framework for real-time prediction of CPU and RAM utilization using the Google Cluster Trace 2019 v3 dataset. The framework combines Extreme Gradient Boosting (XGBoost) with a MultiOutputRegressor (MOR) to capture nonlinear interactions across multiple resource dimensions, supported by a leakage-safe imputation strategy that prevents bias from missing values. Nested… 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 >

Jeyapaul Angel Ida Chellam¹, Bright Brailson Mansingh², Daniel Stalin Alex³, Joseph Selvi Binoj^4,*

Journal of Polymer Materials, Vol.42, No.3, pp. 773-794, 2025, DOI:10.32604/jpm.2025.069295 - 30 September 2025

Abstract Machine learning models can predict material properties quickly and accurately at a low computational cost. This study generated novel hybridized nanocomposites with unsaturated polyester resin as the matrix and Areca fruit husk fiber (AFHF), tamarind fruit fiber (TFF), and nano-sized coconut shell powder (NCSP). It is challenging to determine the optimal proportion of raw materials in this composite to achieve maximum mechanical properties. This task was accomplished with the help of ML techniques in this study. The tensile strength of the hybridized nanocomposite was increased by 134.06% compared to the neat unsaturated polyester resin at… More >

Seunghwan Seo^1,2,*, Moonkyung Chung¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2893-2922, 2025, DOI:10.32604/cmes.2025.069668 - 30 September 2025

Abstract Excavation-induced deformations of earth-retaining walls (ERWs) can critically affect the safety of surrounding structures, highlighting the need for reliable prediction models to support timely decision-making during construction. This study utilizes traditional statistical ARIMA (Auto-Regressive Integrated Moving Average) and deep learning-based LSTM (Long Short-Term Memory) models to predict earth-retaining walls deformation using inclinometer data from excavation sites and compares the predictive performance of both models. The ARIMA model demonstrates strengths in analyzing linear patterns in time-series data as it progresses over time, whereas LSTM exhibits superior capabilities in capturing complex non-linear patterns and long-term dependencies within… More > Graphic Abstract

Lakshmi Alekhya Jandhyam^1,*, Ragupathy Rengaswamy¹, Narayana Satyala²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3679-3714, 2025, DOI:10.32604/cmes.2025.068941 - 30 September 2025

Abstract Human Activity Recognition (HAR) has become increasingly critical in civic surveillance, medical care monitoring, and institutional protection. Current deep learning-based approaches often suffer from excessive computational complexity, limited generalizability under varying conditions, and compromised real-time performance. To counter these, this paper introduces an Active Learning-aided Heuristic Deep Spatio-Textural Ensemble Learning (ALH-DSEL) framework. The model initially identifies keyframes from the surveillance videos with a Multi-Constraint Active Learning (MCAL) approach, with features extracted from DenseNet121. The frames are then segmented employing an optimized Fuzzy C-Means clustering algorithm with Firefly to identify areas of interest. A deep ensemble More >

Soumia Zertal^1,2,*, Asma Saighi^1,2, Sofia Kouah^1,2, Souham Meshoul^3,*, Zakaria Laboudi^2,4

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3737-3782, 2025, DOI:10.32604/cmes.2025.068558 - 30 September 2025

Abstract Cardiovascular diseases (CVDs) continue to present a leading cause of mortality worldwide, emphasizing the importance of early and accurate prediction. Electrocardiogram (ECG) signals, central to cardiac monitoring, have increasingly been integrated with Deep Learning (DL) for real-time prediction of CVDs. However, DL models are prone to performance degradation due to concept drift and to catastrophic forgetting. To address this issue, we propose a real-time CVDs prediction approach, referred to as ADWIN-GFR that combines Convolutional Neural Network (CNN) layers, for spatial feature extraction, with Gated Recurrent Units (GRU), for temporal modeling, alongside adaptive drift detection and… More > Graphic Abstract

Muhammad Salman Qamar^1,2,*, Muhammad Fahad Munir², Athar Waseem²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3557-3584, 2025, DOI:10.32604/cmes.2025.067447 - 30 September 2025

Abstract Air pollution, specifically fine particulate matter (PM2.5), represents a critical environmental and public health concern due to its adverse effects on respiratory and cardiovascular systems. Accurate forecasting of PM2.5 concentrations is essential for mitigating health risks; however, the inherent nonlinearity and dynamic variability of air quality data present significant challenges. This study conducts a systematic evaluation of deep learning algorithms including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and the hybrid CNN-LSTM as well as statistical models, AutoRegressive Integrated Moving Average (ARIMA) and Maximum Likelihood Estimation (MLE) for hourly PM2.5 forecasting. Model performance is… More >

Sen Yang¹, Jie Zhong¹, Boyu Gan¹, Yi Sun¹, Changming Bu¹, Mingtao Zhang¹, Jiehong Li^1,*, Yang Yu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2943-2967, 2025, DOI:10.32604/cmes.2025.067282 - 30 September 2025

Abstract Foam concrete is widely used in engineering due to its lightweight and high porosity. Its compressive strength, a key performance indicator, is influenced by multiple factors, showing nonlinear variation. As compressive strength tests for foam concrete take a long time, a fast and accurate prediction method is needed. In recent years, machine learning has become a powerful tool for predicting the compressive strength of cement-based materials. However, existing studies often use a limited number of input parameters, and the prediction accuracy of machine learning models under the influence of multiple parameters and nonlinearity remains unclear.… More >