Nikhil S. Mane¹, Sheetal Kumar Dewangan^2,*, Sayantan Mukherjee³, Pradnyavati Mane⁴, Deepak Kumar Singh¹, Ravindra Singh Saluja⁵

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.072090 - 10 November 2025

Abstract The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids. Researchers rely on experimental investigations to explore nanofluid properties, as it is a necessary step before their practical application. As these investigations are time and resource-consuming undertakings, an effective prediction model can significantly improve the efficiency of research operations. In this work, an Artificial Neural Network (ANN) model is developed to predict the thermal conductivity of metal oxide water-based nanofluid. For this, a comprehensive set of 691 data points was collected from the literature. This dataset is split More >

Adnan Ashique^1,#, Khalid Masood², Usman Afzal¹, Mati Ur Rahman², Maddina Dinesh Kumar³, Sohaib Abdal³, Nehad Ali Shah^1,#,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3627-3699, 2025, DOI:10.32604/cmes.2025.072523 - 23 December 2025

Abstract This study outlines a quantitative and data-driven study of the mixed convection heat transfer processes that concern Cu-water nanofluids in a Γ-shaped enclosure with one to five rotating cylinders. The dimensionless equations of mass, momentum, and energy are solved using the finite element method as implemented in the COMSOL Multiphysics 6.3 software in different rotating Reynolds numbers and cylinder geometries. An artificial Neural Network that is trained using Bayesian Regularization on data produced by the COMSOL is utilized to estimate the average Nusselt numbers. The analysis is conducted for a wide range of rotational… More >

Carlos Torres-Aguilar^1,*, Pedro Moreno^2,*, Diego Rossit³, Sergio Nesmachnow⁴, Karla M. Aguilar-Castro¹, Edgar V. Macias-Melo¹, Luis Hernández-Callejo⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3859-3881, 2025, DOI:10.32604/cmes.2025.069996 - 23 December 2025

Abstract Solar chimneys are renewable energy systems designed to enhance natural ventilation, improving thermal comfort in buildings. As passive systems, solar chimneys contribute to energy efficiency in a sustainable and environmentally friendly way. The effectiveness of a solar chimney depends on its design and orientation relative to the cardinal directions, both of which are critical for optimal performance. This article presents a supervised learning approach using artificial neural networks to forecast the performance indicators of solar chimneys. The dataset includes information from 2784 solar chimney configurations, which encompasses various factors such as chimney height, channel thickness, More > Graphic Abstract

Chih-Yin Chien^1,2, Tsae-Jyy Wang¹, Pei-Hung Liao¹, Ying-Hsiang Lee^3,4,5,*, Wei-Sho Ho^6,7,*

Congenital Heart Disease, Vol.20, No.5, pp. 601-612, 2025, DOI:10.32604/chd.2025.072431 - 30 November 2025

Abstract Background: Cardiac implantable electronic devices (CIEDs) are essential for preventing sudden cardiac death in patients with cardiovascular diseases, but implantation procedures carry risks of complications such as infection, hematoma, and bleeding, with incidence rates of 3–4%. Previous studies have examined individual risk factors separately, but integrated predictive models are lacking. We compared the predictive performance and interpretability of artificial neural network (ANN) and logistic regression models to evaluate their respective strengths in clinical risk assessment. Methods: This retrospective study analyzed data from 180 patients who underwent cardiac implantable electronic device (CIED) implantation in Taiwan between 2017… More >

Yidong Xu¹, Weijie Zhuge^1,2, Jialei Wang¹, Xiaopeng Yu^3,*, Kan Wu⁴

Structural Durability & Health Monitoring, Vol.19, No.6, pp. 1507-1527, 2025, DOI:10.32604/sdhm.2025.069021 - 17 November 2025

Abstract The performance of concrete can be affected by many factors, including the material composition, environmental conditions, and construction methods, and it is challenging to predict the performance evolution accurately. The rise of artificial intelligence provides a way to meet the above challenges. This article elaborates on research overview of artificial neural network (ANN) and its prediction for concrete strength, deformation, and durability. The focus is on the comparative analysis of the prediction accuracy for different types of neural networks. Numerous studies have shown that the prediction accuracy of ANN can meet the standards of the More >

Mir Majid Etghani¹, Homayoun Boodaghi^2,*

Energy Engineering, Vol.122, No.11, pp. 4385-4474, 2025, DOI:10.32604/ee.2025.070668 - 27 October 2025

Abstract Energy system optimization has become crucial for enhancing efficiency and environmental sustainability. This comprehensive review examines the synergistic application of Artificial Neural Networks (ANN) and Taguchi methods in optimizing diverse energy systems. While previous reviews have focused on these methods separately, this paper presents the first integrated analysis of both approaches across multiple energy applications. We systematically analyze their implementation in: Internal combustion engines, Thermal energy storage systems, Solar energy systems, Wind and tidal turbines, Heat exchangers, and hybrid energy systems. Our findings reveal that ANN models consistently achieve prediction accuracies exceeding 90% when compared More > Graphic Abstract

Bappa Muktar^*, Vincent Fono, Adama Nouboukpo

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025

Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… More >

Ashwini Bhat¹, Nagaraj N. Katagi¹, M. C. Gowrishankar², Manjunath Shettar^2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2715-2728, 2025, DOI:10.32604/cmc.2025.069842 - 23 September 2025

Abstract This research explores the water uptake behavior of glass fiber/epoxy composites filled with nanoclay and establishes an Artificial Neural Network (ANN) to predict water uptake percentage from experimental parameters. Composite laminates are fabricated with varying glass fiber and nanoclay contents. Water absorption is evaluated for 70 days of immersion following ASTM D570-98 standards. The inclusion of nanoclay reduces water uptake by creating a tortuous path for moisture diffusion due to its high aspect ratio and platelet morphology, thereby enhancing the composite’s barrier properties. The ANN model is developed with a 3–4–1 feedforward structure and learned… More >

Fatih Selimefendigil¹, Kaouther Ghachem^2,*, Hind Albalawi³, Badr M. AlShammari⁴, Lioua Kolsi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 899-925, 2025, DOI:10.32604/cmes.2025.066555 - 31 July 2025

Abstract Cooling system design applicable to more than one photovoltaic (PV) unit may be challenging due to the arrangement and geometry of the modules. Different cooling techniques are provided in this study to regulate the temperature of conductive panels that are arranged perpendicular to each other. The model uses two vented cavity systems and one L-shaped channel with ternary nanofluid enhanced non-uniform magnetic field. Their cooling performances and comparative results between different systems are provided. The finite element method is used to conduct a numerical analysis for a range of values of the following: the strength… More >

Sandra Gajević¹, Slavica Miladinović¹, Jelena Jovanović¹, Onur Güler², Serdar Özkaya², Blaža Stojanović^1,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4341-4361, 2025, DOI:10.32604/cmc.2025.065645 - 30 July 2025

Abstract This paper presents an investigation of the tribological performance of AA2024–B₄C composites, with a specific focus on the influence of reinforcement and processing parameters. In this study three input parameters were varied: B₄C weight percentage, milling time, and normal load, to evaluate their effects on two output parameters: wear loss and the coefficient of friction. AA2024 alloy was used as the matrix alloy, while B₄C particles were used as reinforcement. Due to the high hardness and wear resistance of B₄C, the optimized composite shows strong potential for use in aerospace structural elements and automotive brake components. The… More >