Xuan Kien Pham¹, Van Dat Nguyen^2,*, Van Thanh Phan^3,*, Duc Trien Nguyen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.086196 - 27 July 2026

Abstract Accurate energy consumption forecasting faces two major challenges: limited historical data and complex consumption patterns. To address these challenges, this study proposes a new hybrid framework named the Decomposition-based Grey-Neuro-Fuzzy Architecture (DeGNA). The model first uses the Denton method to convert limited annual records into high-frequency monthly data. Next, it applies STL decomposition to separate the data into trend, seasonal and residuals components. A rolling-window GM(1,1) model is then used to predict the main growth trend, while a GWO-optimized ANFIS model uses economic indicators (IIP and FDI) to forecast complex seasonal changes. This study evaluates… More >

Tanzila Saba¹, Muhammad Mujahid¹, Faten S. Alamri^2,*, Roaa Khalil Mohamed Ali Abed³

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084816 - 27 July 2026

Abstract In the era of artificial intelligence, pattern recognition techniques have become fundamental in advancing medical image processing, diagnosis, and automated disease classification systems. Among various clinical challenges, breast cancer is the second most dangerous leading cause of death in women worldwide. Early and accurate detection of breast cancer is crucial to develop advanced diagnostic methods to control further loss or reduce mortality rates. This study proposes a dynamic graph multi-scale network for breast cancer diagnosis, integrated with multi-scale convolutional feature extraction, a squeeze-and-excitation block, and a graph convolutional network to jointly model local spatial features… More > Graphic Abstract

Awais Qasim¹, Aniqa Iftikhar¹, Hanaa Nafea², Nay Chi Moe Oo³, Byung-Seo Kim^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.084691 - 27 July 2026

Abstract Optimal task assignment in holonic multi-agent systems has emerged as a pivotal problem in modern distributed systems. Despite substantial gains in agent coordination, many large-scale systems still suffer from poor job allocation, resulting in performance bottlenecks and resource waste. Effective task assignment is critical for these systems since it influences individual agent performance and overall adaptability. A significant challenge within holonic multi-agent systems is ensuring optimal task assignment while resolving performative inconsistencies, such as role conflicts and coordination failures among agents. This research proposes a novel optimization framework to address these inconsistencies, enabling more efficient More >

Waqas Ahmad¹, Shahzad Anwar², Abid Iqbal^3,*, Abuzar Khan⁴, Saad Arif⁵, Ali S. Alzahrani³, Mohammed Al-Naeem⁶, Fatimah Alhayan⁷, Syed Hashim Raza Bukhari³, Ghassan Husnain^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083950 - 27 July 2026

Abstract Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks (VANETs), including emergency response, navigation, traffic monitoring, and cooperative driving. However, conventional GPS/GNSS positioning systems have often shown degradation in tunnels, dense urban corridors, and non-line-of-sight (NLOS) environments, where satellite visibility and signal reliability are limited. This paper proposes a calibrated Link Quality Indicator (LQI)-based closed-form localization framework for partially connected Roadside Unit (RSU)-assisted VANETs. The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates. The distances are processed through a variance-aware weighted… More >

Li-Woei Chen¹, Kun-Lin Tsai^2,*, Fang-Yie Leu³, Chao-Tung Yang^3,4,5, Wei-Zong Liang²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083880 - 27 July 2026

Abstract Unmanned aerial vehicle (UAV) swarm networks are increasingly deployed in surveillance, disaster response, and intelligent transportation systems, where secure and efficient communication is critical under resource-constrained environments. However, conventional public-key-based security mechanisms introduce excessive computational overhead, while standalone intrusion detection systems are insufficient to defend against dynamic and multi-vector attacks in swarm networks. To address these challenges, in this paper, a lightweight time-indexed secure communication framework with intrusion detection modeling (TSCID) is proposed for resource-constrained UAV swarm networks. The proposed TSCID integrates a time-indexed session key derivation mechanism with lightweight authenticated encryption to ensure confidentiality,… More >

Alireza Norouzpour Shahrbejari¹, Nafiseh Pishbin², Mohammad Reza Maghami^3,*, Mazlan Mohamed^4,*, Mohammad Golmohammad¹

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083763 - 27 July 2026

Abstract This study presents a weighted-sum multi-criteria optimization framework using PSO for the optimal siting, sizing, and scenario-based operation of energy storage systems (ESSs) in renewable-integrated distribution networks. The proposed model concurrently addresses technical, economic, and reliability objectives—minimizing active power losses (PL), voltage deviation (VD), expected energy not supplied (EENS), and short-circuit level (SCL), while maximizing voltage sensitivity index (VSI) and power-loss sensitivity factor (PLSF). A Particle Swarm Optimization (PSO) algorithm with weighted-sum scalarization is employed to solve this complex, nonlinear optimization problem and effectively balance the conflicting operational goals. The framework is validated using IEEE… More >

AL-Wesabi Ibrahim¹, Hassan M. Hussein Farh^2,*, Jiazhu Xu^1,*, Mohamad A. Alawad², Ahmed Alqurashi³, Abdullrahman A. Al-Shamma’a²

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081371 - 27 July 2026

Abstract Load frequency control (LFC) in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system. Although various PI/PID and other advanced control strategies have been employed for LFC in power systems, the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems. Moreover, the optimization methods employed for the tuning of… More >

Malika Abid¹, Mohammed Kamel Benkaddour¹, Mohamed Benouis², Amine Khaldi¹, Monalisa Sahu³, Aditya Kumar Sahu^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081055 - 27 July 2026

Abstract Federated Learning (FL) is a cutting-edge method in the medical imaging field that allows hospitals to collaboratively build models without revealing patient data. Nevertheless, FL is still vulnerable to numerous security and privacy issues, including, but not limited to, data poisoning, Byzantine attacks, and inference attacks. The existing literature has only partly dealt with this topic by focusing either on particular threats or on mitigation strategies, thus leaving the overall comprehension of the problems and their solutions in medical imaging as inadequate. The main threats to FL are systematically classified in this systematic review, with… More >

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 >