Abdullah M. Alqahtani^1,*, Kamran Ahmad Awan², Abdulaziz Almaleh³, Osama Aletri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3155-3179, 2025, DOI:10.32604/cmes.2025.061472 - 03 March 2025

Abstract Efficient resource management within Internet of Things (IoT) environments remains a pressing challenge due to the increasing number of devices and their diverse functionalities. This study introduces a neural network-based model that uses Long-Short-Term Memory (LSTM) to optimize resource allocation under dynamically changing conditions. Designed to monitor the workload on individual IoT nodes, the model incorporates long-term data dependencies, enabling adaptive resource distribution in real time. The training process utilizes Min-Max normalization and grid search for hyperparameter tuning, ensuring high resource utilization and consistent performance. The simulation results demonstrate the effectiveness of the proposed method, More >

Baoping Zou^1,2, Chenhao Pei^1,*, Qizhi Chen^1,2, Yansheng Deng^1,2, Yongguo Chen^1,2, Xu Long^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2457-2485, 2025, DOI:10.32604/cmes.2025.061429 - 03 March 2025

Abstract The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata. The high temperatures, pressures and complex geological environments of deep strata frequently result in the coupling of multiple physical fields, including mechanical, thermal and hydraulic fields, during the fracturing of rocks. This review initially presents an overview of the coupling mechanisms of these physical fields, thereby elucidating the interaction processes of mechanical, thermal, and hydraulic fields within rock masses. Secondly, an in-depth analysis of multi-field coupling is… More >

Ahmed Abbas Jasim Al-Hchaimi¹, Yousif Raad Muhsen^2,3,*, Wisam Hazim Gwad⁴, Entisar Soliman Alkayal⁵, Riyadh Rahef Nuiaa Al Ogaili⁶, Zaid Abdi Alkareem Alyasseri^7,8, Alhamzah Alnoor⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2661-2689, 2025, DOI:10.32604/cmes.2025.061318 - 03 March 2025

Abstract The implementation of Countermeasure Techniques (CTs) in the context of Network-On-Chip (NoC) based Multiprocessor System-On-Chip (MPSoC) routers against the Flooding Denial-of-Service Attack (F-DoSA) falls under Multi-Criteria Decision-Making (MCDM) due to the three main concerns, called: traffic variations, multiple evaluation criteria-based traffic features, and prioritization NoC routers as an alternative. In this study, we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios. Consequently, an MCDM approach is essential to address these emerging challenges. While the recent MCDM approach has some issues, such as uncertainty, this… More >

Ahmed Mohamed Galal^1,2, Adebowale Martins Obalalu³, Akintayo Oladimeji Akindele⁴, Umair Khan^5,6, Abdulazeez Adebayo Usman⁷, Olalekan Adebayo Olayemi⁸, Najiyah Safwa Khashi’ie^9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3089-3113, 2025, DOI:10.32604/cmes.2025.061296 - 03 March 2025

Abstract The need for efficient thermal energy systems has gained significant attention due to the growing global concern about renewable energy resources, particularly in residential buildings. One of the biggest challenges in this area is capturing and converting solar energy at maximum efficiency. This requires the use of strong materials and advanced fluids to enhance conversion efficiency while minimizing energy losses. Despite extensive research on thermal energy systems, there remains a limited understanding of how the combined effects of thermal radiation, irreversibility processes, and advanced heat flux models contribute to optimizing solar power performance in residential… More > Graphic Abstract

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

Asma Aldrees¹, Hong Min^2,*, Ashit Kumar Dutta³, Yousef Ibrahim Daradkeh⁴, Mohd Anjum⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2487-2511, 2025, DOI:10.32604/cmes.2025.061103 - 03 March 2025

Abstract Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus… More >

Eliel Martins¹, Javier Bermejo Higuera^2,*, Ricardo Sant’Ana¹, Juan Ramón Bermejo Higuera², Juan Antonio Sicilia Montalvo², Diego Piedrahita Castillo³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >

Akash Kumar Gartia, S. Chakraverty^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2405-2455, 2025, DOI:10.32604/cmes.2025.061039 - 03 March 2025

Abstract This review explores multi-directional functionally graded (MDFG) nanostructures, focusing on their material characteristics, modeling approaches, and mechanical behavior. It starts by classifying different types of functionally graded (FG) materials such as conventional, axial, bi-directional, and tri-directional, and the material distribution models like power-law, exponential, trigonometric, polynomial functions, etc. It also discusses the application of advanced size-dependent theories like Eringen’s nonlocal elasticity, nonlocal strain gradient, modified couple stress, and consistent couple stress theories, which are essential to predict the behavior of structures at small scales. The review covers the mechanical analysis of MDFG nanostructures in nanobeams,… More > Graphic Abstract

Mohammed A. El-Shorbagy¹, Anas Bouaouda^2,*, Laith Abualigah^3,4, Fatma A. Hashim^5,6

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2339-2404, 2025, DOI:10.32604/cmes.2025.061028 - 03 March 2025

Abstract With the rapid advancements in technology and science, optimization theory and algorithms have become increasingly important. A wide range of real-world problems is classified as optimization challenges, and meta-heuristic algorithms have shown remarkable effectiveness in solving these challenges across diverse domains, such as machine learning, process control, and engineering design, showcasing their capability to address complex optimization problems. The Stochastic Fractal Search (SFS) algorithm is one of the most popular meta-heuristic optimization methods inspired by the fractal growth patterns of natural materials. Since its introduction by Hamid Salimi in 2015, SFS has garnered significant attention… More >

Sonia Khan¹, Naqash Younas², Musaed Alhussein³, Wahib Jamal Khan², Muhammad Shahid Anwar^4,*, Khursheed Aurangzeb³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2641-2660, 2025, DOI:10.32604/cmes.2025.060973 - 03 March 2025

Abstract Effective resource management in the Internet of Things and fog computing is essential for efficient and scalable networks. However, existing methods often fail in dynamic and high-demand environments, leading to resource bottlenecks and increased energy consumption. This study aims to address these limitations by proposing the Quantum Inspired Adaptive Resource Management (QIARM) model, which introduces novel algorithms inspired by quantum principles for enhanced resource allocation. QIARM employs a quantum superposition-inspired technique for multi-state resource representation and an adaptive learning component to adjust resources in real time dynamically. In addition, an energy-aware scheduling module minimizes power More >