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ARTICLE

Syed Zain Ul Abideen¹, Mian Muhammad Kamal^2,*, Eaman Alharbi³, Ashfaq Ahmad Malik⁴, Wadee Alhalabi⁵, Muhammad Shahid Anwar^6,*, Liaqat Ali⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2191-2210, 2025, DOI:10.32604/cmes.2025.061744 - 03 March 2025
（This article belongs to the Special Issue: Leveraging AI and ML for QoS Improvement in Intelligent Programmable Networks)
Abstract The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet of Things (IoT) applications, particularly in terms of ultra-reliable, secure, and energy-efficient communication. This study explores the integration of Reconfigurable Intelligent Surfaces (RIS) into IoT networks to enhance communication performance. Unlike traditional passive reflector-based approaches, RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes, addressing critical IoT challenges such as energy efficiency, limited communication range, and double-fading effects in backscatter communication. We propose a novel computational framework that combines… More >

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Jeta Dobruna^1,2, Zana Limani Fazliu^2,*, Iztok Humar¹, Mojca Volk¹
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2211-2245, 2025, DOI:10.32604/cmes.2025.059541 - 03 March 2025
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract Resource allocation remains a challenging issue in communication networks, and its complexity is continuously increasing with the densification of the networks. With the evolution of new wireless technologies such as Fifth Generation (5G) and Sixth Generation (6G) mobile networks, the service level requirements have become stricter and more heterogeneous depending on the use case. In this paper, we review a large body of literature on various resource allocation schemes that are used in particular in mobile wireless communication networks and compare the proposed schemes in terms of performance indicators as well as techniques used. Our… More >

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Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui^1,*, Amr Abdussalam², Muhammad Asim³, Sajid Shah³, Mohammed ELAffendi³
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2247-2280, 2025, DOI:10.32604/cmes.2025.059192 - 03 March 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Image captioning has seen significant research efforts over the last decade. The goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically accurate. Many real-world applications rely on image captioning, such as helping people with visual impairments to see their surroundings. To formulate a coherent and relevant textual description, computer vision techniques are utilized to comprehend the visual content within an image, followed by natural language processing methods. Numerous approaches and models have been developed to deal with this multifaceted problem. Several models prove to be state-of-the-art solutions… More >

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Leonardo Di G. Sigalotti^*, Carlos A. Vargas
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2281-2337, 2025, DOI:10.32604/cmes.2025.060497 - 03 March 2025
（This article belongs to the Special Issue: Smoothed Particle Hydrodynamics (SPH): Research and Applications to Science and Engineering)
Abstract The evaporation of micrometer and millimeter liquid drops, involving a liquid-to-vapor phase transition accompanied by mass and energy transfer through the liquid-vapor interface, is encountered in many natural and industrial processes as well as in numerous engineering applications. Therefore, understanding and predicting the dynamics of evaporating flows have become of primary importance. Recent efforts have been addressed using the method of Smoothed Particle Hydrodynamics (SPH), which has proven to be very efficient in correctly handling the intrinsic complexity introduced by the multiscale nature of the evaporation process. This paper aims to provide an overview of… More >

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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
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
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 >

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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
（This article belongs to the Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures-II)
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 >

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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 >

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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 >

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Oswaldo González-Gaxiola¹, Anjan Biswas^2,3,4,*, Ahmed H. Arnous⁵, Yakup Yildirim^6,7
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2513-2525, 2025, DOI:10.32604/cmes.2025.062177 - 03 March 2025
Abstract Computational modeling plays a vital role in advancing our understanding and application of soliton theory. It allows researchers to both simulate and analyze complex soliton phenomena and discover new types of soliton solutions. In the present study, we computationally derive the bright and dark optical solitons for a Schrödinger equation that contains a specific type of nonlinearity. This nonlinearity in the model is the result of the combination of the parabolic law and the non-local law of self-phase modulation structures. The numerical simulation is accomplished through the application of an algorithm that integrates the classical… More >

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Gaoshuai Su^1,2, Hongyang Li^1,*, Huafeng Chen^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2527-2545, 2025, DOI:10.32604/cmes.2025.059452 - 03 March 2025
Abstract Brain tumors, one of the most lethal diseases with low survival rates, require early detection and accurate diagnosis to enable effective treatment planning. While deep learning architectures, particularly Convolutional Neural Networks (CNNs), have shown significant performance improvements over traditional methods, they struggle to capture the subtle pathological variations between different brain tumor types. Recent attention-based models have attempted to address this by focusing on global features, but they come with high computational costs. To address these challenges, this paper introduces a novel parallel architecture, ParMamba, which uniquely integrates Convolutional Attention Patch Embedding (CAPE) and the… More >

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Mohammed Elgarhy¹, Amal S. Hassan², Najwan Alsadat³, Oluwafemi Samson Balogun⁴, Ahmed W. Shawki⁵, Ibrahim E. Ragab^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2547-2583, 2025, DOI:10.32604/cmes.2025.058362 - 03 March 2025
Abstract Accurately modeling heavy-tailed data is critical across applied sciences, particularly in finance, medicine, and actuarial analysis. This work presents the heavy-tailed power XLindley distribution (HTPXLD), a unique heavy-tailed distribution. Adding one more parameter to the power XLindley distribution improves this new distribution, especially when modeling leptokurtic lifetime data. The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness. Its statistical features, like the quantile function, moments, extropy measures, incomplete moments, stochastic ordering, and stress-strength parameters, are explored. We further investigate its use in actuarial science through the computation of pertinent metrics,… More >

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Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Kar Tim Chan¹, Arun Kumar Sangaiah^2,3,4
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2585-2616, 2025, DOI:10.32604/cmes.2025.060548 - 03 March 2025
Abstract This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms. The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments. The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency. A novel closed-form expression is also derived for a generalized asymptotic error variance steady state. Steady and convergence analyses are then presented for the synchronization, with frequency adaptations done using least More >

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Rifat Sarker Aoyon¹, Ismail Hossain², M. Abdullah-Al-Wadud³, Jia Uddin^4,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2617-2640, 2025, DOI:10.32604/cmes.2025.060917 - 03 March 2025
Abstract This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture. However, the computational demand for image processing is very high. Therefore, we have conducted this research to build a system that enables image segmentation training with low-power machines. To accomplish this, all data are divided into several segments, each being trained separately. In the case of prediction, the initial output is predicted from each trained model for an input, where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs, which also ensures data privacy.… More >

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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 >

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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 >

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ARTICLE

Ferzat Anka¹, Ghanshyam G. Tejani^2,3,*, Sunil Kumar Sharma⁴, Mohammed Baljon⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2691-2724, 2025, DOI:10.32604/cmes.2025.061522 - 03 March 2025
Abstract Due to the intense data flow in expanding Internet of Things (IoT) applications, a heavy processing cost and workload on the fog-cloud side become inevitable. One of the most critical challenges is optimal task scheduling. Since this is an NP-hard problem type, a metaheuristic approach can be a good option. This study introduces a novel enhancement to the Artificial Rabbits Optimization (ARO) algorithm by integrating Chaotic maps and Levy flight strategies (CLARO). This dual approach addresses the limitations of standard ARO in terms of population diversity and convergence speed. It is designed for task scheduling… More >

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ARTICLE

Donghyeok Park¹, Sumin Yeon², Hyeon Seo², Seok-Jun Buu², Suwon Lee^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2725-2737, 2025, DOI:10.32604/cmes.2025.061732 - 03 March 2025
Abstract Recent research on adversarial attacks has primarily focused on white-box attack techniques, with limited exploration of black-box attack methods. Furthermore, in many black-box research scenarios, it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts. Unfortunately, this disregard for the real-world practicality of attacks, particularly their potential for human detectability, has left a gap in the research landscape. Considering these limitations, our study focuses on using a similar color attack method, assuming access only to the output label, limiting the number of More >

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Hyeonbin Jeong¹, Young Tae Ryu², Byung Duk Song^1,*, Sang-Duck Lee^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2739-2769, 2025, DOI:10.32604/cmes.2025.061639 - 03 March 2025
Abstract This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems. The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints. Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions, if possible to be physically implemented. Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability. Experimental results demonstrate the extensive robustness of the proposed algorithm More >

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Dah-Jing Jwo^1,2,*, Yi Chang², Ta-Shun Cho³
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2771-2789, 2025, DOI:10.32604/cmes.2025.057825 - 03 March 2025
（This article belongs to the Special Issue: Scientific Computing and Its Application to Engineering Problems)
Abstract In this paper, an advanced satellite navigation filter design, referred to as the Variational Bayesian Maximum Correntropy Extended Kalman Filter (VBMCEKF), is introduced to enhance robustness and adaptability in scenarios with non-Gaussian noise and heavy-tailed outliers. The proposed design modifies the extended Kalman filter (EKF) for the global navigation satellite system (GNSS), integrating the maximum correntropy criterion (MCC) and the variational Bayesian (VB) method. This adaptive algorithm effectively reduces non-line-of-sight (NLOS) reception contamination and improves estimation accuracy, particularly in time-varying GNSS measurements. Experimental results show that the proposed method significantly outperforms conventional approaches in estimation More >

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Xing Wang^1,*, Huazhen Liu¹, Abdelazim G. Hussien², Gang Hu¹, Li Zhang³
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2791-2839, 2025, DOI:10.32604/cmes.2025.058473 - 03 March 2025
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
Abstract Feature selection (FS) is essential in machine learning (ML) and data mapping by its ability to preprocess high-dimensional data. By selecting a subset of relevant features, feature selection cuts down on the dimension of the data. It excludes irrelevant or surplus features, thus boosting the performance and efficiency of the model. Particle Swarm Optimization (PSO) boasts a streamlined algorithmic framework and exhibits rapid convergence traits. Compared with other algorithms, it incurs reduced computational expenses when tackling high-dimensional datasets. However, PSO faces challenges like inadequate convergence precision. Therefore, regarding FS problems, this paper presents a binary… More >

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Zaid Derea^1,2, Beiji Zou¹, Xiaoyan Kui^1,*, Alaa Thobhani¹, Amr Abdussalam³
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2841-2867, 2025, DOI:10.32604/cmes.2025.059586 - 03 March 2025
（This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
Abstract Enhancing website security is crucial to combat malicious activities, and CAPTCHA (Completely Automated Public Turing tests to tell Computers and Humans Apart) has become a key method to distinguish humans from bots. While text-based CAPTCHAs are designed to challenge machines while remaining human-readable, recent advances in deep learning have enabled models to recognize them with remarkable efficiency. In this regard, we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention (GVA), which sharpens focus on relevant visual features. We have specifically adapted the… More >

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Adil Yousif^*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2869-2892, 2025, DOI:10.32604/cmes.2025.059786 - 03 March 2025
（This article belongs to the Special Issue: Engineering Applications of Discrete Optimization and Scheduling Algorithms)
Abstract The Internet of Things (IoT) has emerged as an important future technology. IoT-Fog is a new computing paradigm that processes IoT data on servers close to the source of the data. In IoT-Fog computing, resource allocation and independent task scheduling aim to deliver short response time services demanded by the IoT devices and performed by fog servers. The heterogeneity of the IoT-Fog resources and the huge amount of data that needs to be processed by the IoT-Fog tasks make scheduling fog computing tasks a challenging problem. This study proposes an Adaptive Firefly Algorithm (AFA) for… More >

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Shanqiao Huang¹, Zifeng Yuan^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2893-2924, 2025, DOI:10.32604/cmes.2025.059948 - 03 March 2025
（This article belongs to the Special Issue: Computational Design and Modeling of Advanced Composites and Structures)
Abstract The multiscale computational method with asymptotic analysis and reduced-order homogenization (ROH) gives a practical numerical solution for engineering problems, especially composite materials. Under the ROH framework, a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions. In the past, the “1-phase, 1-partition” rule was usually adopted in numerical analysis, where one constituent phase at the mesoscale formed one partition. The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the… More >

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G. Sukanya, J. Priyadarshini^*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2925-2958, 2025, DOI:10.32604/cmes.2025.060030 - 03 March 2025
（This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)
Abstract Advancements in Natural Language Processing and Deep Learning techniques have significantly propelled the automation of Legal Judgment Prediction, achieving remarkable progress in legal research. Most of the existing research works on Legal Judgment Prediction (LJP) use traditional optimization algorithms in deep learning techniques falling into local optimization. This research article focuses on using the modified Pelican Optimization method which mimics the collective behavior of Pelicans in the exploration and exploitation phase during cooperative food searching. Typically, the selection of search agents within a boundary is done randomly, which increases the time required to achieve global… More >

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Ahlem Walha¹, Amel Ksibi^2,*, Mohammed Zakariah^3,*, Manel Ayadi², Tagrid Alshalali², Oumaima Saidani², Leila Jamel², Nouf Abdullah Almujally²
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2959-3001, 2025, DOI:10.32604/cmes.2025.060545 - 03 March 2025
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Dementia is a neurological disorder that affects the brain and its functioning, and women experience its effects more than men do. Preventive care often requires non-invasive and rapid tests, yet conventional diagnostic techniques are time-consuming and invasive. One of the most effective ways to diagnose dementia is by analyzing a patient’s speech, which is cheap and does not require surgery. This research aims to determine the effectiveness of deep learning (DL) and machine learning (ML) structures in diagnosing dementia based on women’s speech patterns. The study analyzes data drawn from the Pitt Corpus, which contains… More >

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Manjit Singh, Sunil Kumar Singla^*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3003-3029, 2025, DOI:10.32604/cmes.2025.060863 - 03 March 2025
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security. The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data. Considering the concerns of existing methods, in this work, a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism. Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a… More >

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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
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
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 >

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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
（This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
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 >

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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
（This article belongs to the Special Issue: Innovative Computational Methods and Applications of Nanofluids in Engineering)
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 >

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Josef Izák^1,*, Marek Benč², Petr Opěla²
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3115-3133, 2025, DOI:10.32604/cmes.2025.059234 - 03 March 2025
（This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
Abstract The paper deals with the FEM (Finite Element Method) simulation of rotary swaging of Dievar alloy produced by additive manufacturing technology Selective Laser Melting and conventional process. Swaging was performed at a temperature of 900°C. True flow stress-strain curves were determined for 600°C–900°C and used to construct a Hensel-Spittel model for FEM simulation. The process parameters, i.e., stress, temperature, imposed strain, and force, were investigation during the rotary swaging process. Firstly, the stresses induced during rotary swaging and the resistance of the material to deformation were investigated. The amount and distribution of imposed strain in… More >

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Josef Walek^*, Petr Lichý
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3135-3153, 2025, DOI:10.32604/cmes.2025.059889 - 03 March 2025
（This article belongs to the Special Issue: Computer Aided Simulation in Metallurgy and Material Engineering)
Abstract Plastometric experiments, supplemented with numerical simulations using the finite element method (FEM), can be advantageously used to characterize the deformation behavior of metallic materials. The accuracy of such simulations predicting deformation behaviors of materials is, however, primarily affected by the applied rheology law. The presented study focuses on the characterization of the deformation behavior of AISI 1045 type carbon steel, widely used e.g., in automotive and power engineering, under extreme conditions (i.e., high temperatures, strain rates). The study consists of two main parts: experimentally analyzing the flow stress development of the steel under different thermomechanical… More >

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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
（This article belongs to the Special Issue: Leveraging AI and ML for QoS Improvement in Intelligent Programmable Networks)
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 >

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Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*, Yahia Said³, Shaaban M. Shaaban³, Husam Lahza⁴, Aws I. AbuEid⁵, Abdulrahman Alzahrani⁶
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3181-3212, 2025, DOI:10.32604/cmes.2025.061608 - 03 March 2025
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract Email communication plays a crucial role in both personal and professional contexts; however, it is frequently compromised by the ongoing challenge of spam, which detracts from productivity and introduces considerable security risks. Current spam detection techniques often struggle to keep pace with the evolving tactics employed by spammers, resulting in user dissatisfaction and potential data breaches. To address this issue, we introduce the Divide and Conquer-Generative Adversarial Network Squeeze and Excitation-Based Framework (DaC-GANSAEBF), an innovative deep-learning model designed to identify spam emails. This framework incorporates cutting-edge technologies, such as Generative Adversarial Networks (GAN), Squeeze and… More >

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So-Eun Jeon¹, Yeon-Ji Lee², Il-Gu Lee^1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3213-3228, 2025, DOI:10.32604/cmes.2025.062082 - 03 March 2025
（This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
Abstract The Internet of Things (IoT) is extensively applied across various industrial domains, such as smart homes, factories, and intelligent transportation, becoming integral to daily life. Establishing robust policies for managing and governing IoT devices is imperative. Secure authentication for IoT devices in resource-constrained environments remains challenging due to the limitations of conventional complex protocols. Prior methodologies enhanced mutual authentication through key exchange protocols or complex operations, which are impractical for lightweight devices. To address this, our study introduces the privacy-preserving software-defined range proof (SDRP) model, which achieves secure authentication with low complexity. SDRP minimizes the More >

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Mohamed A.G. Hazber^1,*, Ebrahim Mohammed Senan^2,3, Hezam Saud Alrashidi¹
CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3229-3254, 2025, DOI:10.32604/cmes.2025.062302 - 03 March 2025
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions, and it has many types, from normal to serious. Hepatitis is diagnosed through many blood tests and factors; Artificial Intelligence (AI) techniques have played an important role in early diagnosis and help physicians make decisions. This study evaluated the performance of Machine Learning (ML) algorithms on the hepatitis data set. The dataset contains missing values that have been processed and outliers removed. The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique (SMOTE). The features of the data set were processed… More >

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