Imran Ahsan¹, Muhammad Waseem Anwar², JungYoon Kim³, Mucheol Kim^1,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080382 - 27 July 2026

Abstract Graph neural networks (GNNs) have demonstrated promising results in enhancing machine learning and artificial intelligence techniques by addressing graph-related problems, including graph categorization, edge prediction, and node prediction. Despite the rapid growth of the field, a structured synthesis of recent application-oriented GNN research is necessary. This systematic literature review analyzes 363 peer-reviewed, application-oriented GNN studies published from 2019 through February 2026 from four scientific repositories. These studies are organized by GNN model variant, (including single and multimodel approaches), and four classification task categories: graph, link, node, and node and edge combined. In addition, the review… More >

Marta Leal, Verónica Abad-Alcaraz, María del Mar Castilla, José Domingo Álvarez^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.080261 - 27 July 2026

Abstract The effective control of parabolic trough collectors (PTCs) remains a significant challenge due to the inherent non-linearities of the system and the continuous impact of environmental disturbances. Although PTCs are a key technology for industrial process heat and large-scale electricity generation, classical control strategies often struggle to maintain optimal performance under fluctuating conditions. To address these limitations, this paper presents a novel reinforcement learning (RL)-based controller, designed specifically for solar thermal systems. The proposed RL agent is designed to learn directly from operational data, enabling it to adapt its control policy in real time to More >

Ismail Hossain¹, Mridul Banik², Fahmid Al Farid^3,4, Jia Uddin^5,*, Hezerul bin Abdul Karim^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.075507 - 27 July 2026

Abstract In recent years, the rapid advancement of Large Language Models (LLMs) has significantly transformed natural language processing (NLP), enabling impressive performance across a wide range of tasks. However, these developments have largely benefited high-resource languages, leaving many low-resource and underrepresented languages at risk of further digital marginalization. Addressing this imbalance is crucial to building more inclusive and culturally sustainable AI systems, which is motivating growing research interest in adapting LLMs for linguistically diverse and resource-scarce communities. This systematic review examines recent progress (2020–2025) in the pretraining and adaptation of LLMs for Low-Resource Languages (LRLs). Analysed… More >

Manish Kewalramani¹, Refka Ghodhbani², Arsalan Mahmoodzadeh^3,*, Abdulaziz Alghamdi⁴, Faten Khalid Karim⁵, Abed Alanazi⁶, Abdullah Alqahtani⁶, Shtwai Alsubai⁶, Mounir Ltifi⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084696 - 30 June 2026

Abstract Concrete manufacturing consumes vast quantities of natural resources and contributes significantly to environmental degradation and carbon emissions. Therefore, integrating recycled waste substances into concrete has become a crucial approach to fostering eco-friendly building practices and supporting circular economy concepts. This study investigates the potential of incorporating recycled aluminum beverage can crumbs (RABCC) as a partial replacement for natural coarse aggregates (NCA) in concrete mixtures, focusing on its impact on compressive strength (CS) and the feasibility of its application in structural concrete. A comprehensive experimental program was conducted to assess the mechanical properties of concrete with… More >

Wenbing Zhao^1,*, Pan Wang²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084481 - 30 June 2026

Abstract This article has no abstract. More >

Salman Khan^*, Mai Alzamel^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084282 - 30 June 2026

Abstract Traditional threat detection has proven ineffective in large-scale, moving data in the era of ever-more complex adversarial techniques and interconnected systems. The challenge becomes even more complex when high-volume, unstructured data continuously streams from social media platforms, requiring them to process the data efficiently and intelligently to provide timely security insights. Considering the big data security, the present study presents a scalable deep-learning-based system for real-time cyber threat detection, which has been developed and validated especially for distributed big data processing environments. A hybrid embedding approach that combines Word2Vec and Iterated Dilated Convolutional Neural Networks… More > Graphic Abstract

Siamak Talatahari^*, Amin Beheshti

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084097 - 30 June 2026

Abstract This article has no abstract. More >

Ulysses Lam^1,*, Jin-Hee Cho², Hyuk Lim³, Terrence Moore⁴, Frederica Free-Nelson⁴, Hyunjae Kang¹, Dan Dongseong Kim¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084062 - 30 June 2026

Abstract Autonomous vehicles are potentially more vulnerable to cyber-attacks compared to traditional human-driven ones, as they employ electronic sensors to enable self-driving. Cybersecurity for autonomous vehicles will be crucial in the near future. However, intrusion detection systems (IDSes) for vehicles are still in the early stages. Many IDS models that claim to work for vehicles are actually built with traditional Internet datasets rather than those with real vehicle data, which is impractical in reality. In this paper, IDS models are developed with Federated Learning (FL) with the Car-Hacking and CAN-MIRGU datasets, which are obtained from real More >

Anita Desiani^1,*, Indri Ramayanti², Sigit Priyanta³, Bambang Suprihatin¹, Muhammad Arhami⁴, Deshinta Arrova Dewi⁵, Puspa Sari¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083951 - 30 June 2026

Abstract Image segmentation is essential in medical image analysis for glaucoma screening. Accurate delineation of the optic disc (OD) and optic cup (OC) in retinal fundus images is required for reliable clinical assessment. Manual segmentation is time-consuming and suffers from interobserver variability, which leads to inconsistent results. To address these limitations, this study proposes ASPP Inception Attention U-Net (ASPPIAU-Ne), an enhanced encoder-decoder architecture that integrates Atrous Spatial Pyramid Pooling (ASPP), Inception modules, and attention gates for feature selection in skip connections. The ASPP module is applied after the encoder to capture multi-scale contextual information and improve… More >

Chenghao Kou¹, Zunyi Duan^2,*, Shengjie Wang¹, Jun Ma¹, Zhongwei Yang¹, Xudong Tang¹, Rongchun Hu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083699 - 30 June 2026

Abstract Piezoelectric actuators are widely used in precision positioning systems. However, their inherent nonlinear behaviors, particularly hysteresis and output saturation, degrade modeling accuracy and limit control performance. Existing studies have generally used either black-box models or traditional physical models. The former typically lack physical interpretability, while the latter can exhibit limited accuracy when the actuator response includes coupled nonlinear effects. To address this issue, this paper proposes a hybrid physics-informed neural network (Hybrid-PINN) framework. An equivalent attenuation model, with a calibrated attenuation coefficient, is first established to describe output saturation and provide a nominal physical reference.… More >