Jia Mi¹, Zhikang Liu¹, Chang Li², Jing Wan^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4089-4106, 2025, DOI:10.32604/cmes.2025.074364 - 23 December 2025

Abstract Recognizing essential proteins within bacteriophages is fundamental to uncovering their replication and survival mechanisms and contributes to advances in phage-based antibacterial therapies. Despite notable progress, existing computational techniques struggle to represent the interplay between sequence-derived and structure-dependent protein features. To overcome this limitation, we introduce GLM-EP, a unified framework that fuses protein language models with equivariant graph neural networks. By merging semantic embeddings extracted from amino acid sequences with geometry-aware graph representations, GLM-EP enables an in-depth depiction of phage proteins and enhances essential protein identification. Evaluation on diverse benchmark datasets confirms that GLM-EP surpasses conventional More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… 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 >

Arshad Riaz^1,*, Misbah Ilyas¹, Muhammad Naeem Aslam², Safia Akram³, Sami Ullah Khan⁴, Ghaliah Alhamzi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3481-3509, 2025, DOI:10.32604/cmes.2025.072513 - 23 December 2025

Abstract Peristaltic transport of non-Newtonian nanofluids with double diffusion is essential to biological engineering, microfluidics, and manufacturing processes. The authors tackle the key problem of Sisko nanofluids under double diffusion convection with thermal radiations and electroosmotic effects. The study proposes a solution approach by using Morlet-Wavelet Neural Networks that can effectively solve this complex problem by their superior ability in the capture of nonlinear dynamics. These convergence analyses were calculated across fifty independent runs. Theil’s Inequality Coefficient and the Mean Squared Error values range from 10⁻⁷ to 10⁻⁵ and 10⁻⁷ to 10⁻¹⁰, respectively. These values showed the proposed 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

Zheng Yin, Zhao Zhang^*

Journal on Artificial Intelligence, Vol.7, pp. 633-645, 2025, DOI:10.32604/jai.2025.074450 - 24 December 2025

Abstract Power load forecasting load forecasting is a core task in power system scheduling, operation, and planning. To enhance forecasting performance, this paper proposes a dual-input deep learning model that integrates Convolutional Neural Networks, Gated Recurrent Units, and a self-attention mechanism. Based on standardized data cleaning and normalization, the method performs convolutional feature extraction and recurrent modeling on load and meteorological time series separately. The self-attention mechanism is then applied to assign weights to key time steps, after which the two feature streams are flattened and concatenated. Finally, a fully connected layer is used to generate More >

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 >

Abdelouahab Benseddik^1,*, Djamel Daoud¹, Ahmed Badji^1,2, Hocine Bensaha¹, Tarik Hadibi^3,5, Yunfeng Wang⁴, Li Ming⁴

Energy Engineering, Vol.122, No.12, pp. 5021-5037, 2025, DOI:10.32604/ee.2025.072991 - 27 November 2025

Abstract In Saharan climates, greenhouses face extreme diurnal temperature fluctuations that generate thermal stress, reduce crop productivity, and hinder sustainable agricultural practices. Passive thermal storage using Phase Change Materials (PCM) is a promising solution to stabilize microclimatic conditions. This study aims to evaluate experimentally and numerically the effectiveness of PCM integration for moderating greenhouse temperature fluctuations under Saharan climatic conditions. Two identical greenhouse prototypes were constructed in Ghardaïa, Algeria: a reference greenhouse and a PCM-integrated greenhouse using calcium chloride hexahydrate (CaCl₂·6H₂O). Thermal performance was assessed during a five-day experimental period (7–11 May 2025) under severe ambient conditions.… More > Graphic Abstract

Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

Tae-hyeon Yun¹, Moohong Min^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2707-2731, 2025, DOI:10.32604/cmes.2025.072357 - 26 November 2025

Abstract The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency… More >