Open Access

EDITORIAL

Debiao Meng^1,*, Abílio Manuel Pinho de Jesus², Zeng Meng³
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 1-5, 2024, DOI:10.32604/cmes.2024.056319
（This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)
Abstract This article has no abstract. More >

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EDITORIAL

Xiong Luo^1,*, Yongqiang Cheng², Zhifang Liao³
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 7-11, 2024, DOI:10.32604/cmes.2024.056214
（This article belongs to the Special Issue: Machine Learning-Guided Intelligent Modeling with Its Industrial Applications)
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REVIEW

Heejae Park¹, Tri-Hai Nguyen², Laihyuk Park^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 13-41, 2024, DOI:10.32604/cmes.2024.052394
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract The emergence of various technologies such as terahertz communications, Reconfigurable Intelligent Surfaces (RIS), and AI-powered communication services will burden network operators with rising infrastructure costs. Recently, the Open Radio Access Network (O-RAN) has been introduced as a solution for growing financial and operational burdens in Beyond 5G (B5G) and 6G networks. O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs. By disaggregating conventional Base Band Units (BBUs) into O-RAN Distributed Units (O-DU) and O-RAN Centralized Units (O-CU), O-RAN offers greater flexibility for upgrades and network automation. However, this openness introduces new security More >

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REVIEW

Ebtesam Ahmad Alomari^*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 43-85, 2024, DOI:10.32604/cmes.2024.052256
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract As Natural Language Processing (NLP) continues to advance, driven by the emergence of sophisticated large language models such as ChatGPT, there has been a notable growth in research activity. This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain. This review paper systematically investigates the role of ChatGPT in diverse NLP tasks, including information extraction, Name Entity Recognition (NER), event extraction, relation extraction, Part of Speech (PoS) tagging, text classification, sentiment analysis, emotion recognition and text annotation. The novelty of this work lies in its… More >

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ARTICLE

Lirong Yin¹, Lei Wang¹, Siyu Lu^2,*, Ruiyang Wang², Youshuai Yang², Bo Yang², Shan Liu², Ahmed AlSanad³, Salman A. AlQahtani³, Zhengtong Yin⁴, Xiaolu Li⁵, Xiaobing Chen⁶, Wenfeng Zheng^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 87-106, 2024, DOI:10.32604/cmes.2024.051083
Abstract This study addresses the limitations of Transformer models in image feature extraction, particularly their lack of inductive bias for visual structures. Compared to Convolutional Neural Networks (CNNs), the Transformers are more sensitive to different hyperparameters of optimizers, which leads to a lack of stability and slow convergence. To tackle these challenges, we propose the Convolution-based Efficient Transformer Image Feature Extraction Network (CEFormer) as an enhancement of the Transformer architecture. Our model incorporates E-Attention, depthwise separable convolution, and dilated convolution to introduce crucial inductive biases, such as translation invariance, locality, and scale invariance, into the Transformer… More >

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ARTICLE

Khaled Lotfy^1,2, A. M. S. Mahdy^3,*, Alaa A. El-Bary⁴, E. S. Elidy¹
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 107-126, 2024, DOI:10.32604/cmes.2024.053199
Abstract In this research, we focus on the free-surface deformation of a one-dimensional elastic semiconductor medium as a function of magnetic field and moisture diffusivity. The problem aims to analyze the interconnection between plasma and moisture diffusivity processes, as well as thermo-elastic waves. The study examines the photo-thermoelasticity transport process while considering the impact of moisture diffusivity. By employing Laplace’s transformation technique, we derive the governing equations of the photo-thermo-elastic medium. These equations include the equations for carrier density, elastic waves, moisture transport, heat conduction, and constitutive relationships. Mechanical stresses, thermal conditions, and plasma boundary conditions More >

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ARTICLE

V. G. Saranya^*, S. Karthik
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 127-150, 2024, DOI:10.32604/cmes.2024.053825
Abstract Wireless Sensor Networks (WSNs) are a collection of sensor nodes distributed in space and connected through wireless communication. The sensor nodes gather and store data about the real world around them. However, the nodes that are dependent on batteries will ultimately suffer an energy loss with time, which affects the lifetime of the network. This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability. The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization (MFOA-EACO), where the Mayfly Optimization Algorithm (MFOA) is used to… More >

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ARTICLE

Junsong Xiong¹, Zhen Wang², Shaofan Li³, Xin Lai^1,*, Lisheng Liu^2,*, Xiang Liu²
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 151-169, 2024, DOI:10.32604/cmes.2024.053078
Abstract Natural convection is a heat transfer mechanism driven by temperature or density differences, leading to fluid motion without external influence. It occurs in various natural and engineering phenomena, influencing heat transfer, climate, and fluid mixing in industrial processes. This work aims to use the Updated Lagrangian Particle Hydrodynamics (ULPH) theory to address natural convection problems. The Navier-Stokes equation is discretized using second-order nonlocal differential operators, allowing a direct solution of the Laplace operator for temperature in the energy equation. Various numerical simulations, including cases such as natural convection in square cavities and two concentric cylinders, More >

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ARTICLE

Ningjie Wang¹, Yihao Wang¹, Yongle Pei², Luxian Li^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 171-186, 2024, DOI:10.32604/cmes.2024.053520
Abstract A numerical approach is an effective means of solving boundary value problems (BVPs). This study focuses on physical problems with general partial differential equations (PDEs). It investigates the solution approach through the standard forms of the PDE module in COMSOL. Two typical mechanics problems are exemplified: The deflection of a thin plate, which can be addressed with the dedicated finite element module, and the stress of a pure bending beam that cannot be tackled. The procedure for the two problems regarding the three standard forms required by the PDE module is detailed. The results were More >

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ARTICLE

Dijun Rao^1,2,3,4, Min Huang^1,2,3,5, Xiuzhi Shi⁴, Zhi Yu^6,*, Zhengxiang He⁷
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 187-217, 2024, DOI:10.32604/cmes.2024.051402
（This article belongs to the Special Issue: Bio-inspired Optimization in Engineering and Sciences)
Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >

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ARTICLE

Farouq Zitouni^1,*, Saad Harous², Abdulaziz S. Almazyad³, Ali Wagdy Mohamed^4,5, Guojiang Xiong⁶, Fatima Zohra Khechiba¹, Khadidja Kherchouche¹
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 219-265, 2024, DOI:10.32604/cmes.2024.052001
（This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
Abstract Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to… More >

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99

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ARTICLE

Bingrui Ju^1,2, Wenxiang Sun², Wenzhen Qu^1,2,*, Yan Gu²
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 267-280, 2024, DOI:10.32604/cmes.2024.052159
（This article belongs to the Special Issue: New Trends on Meshless Method and Numerical Analysis)
Abstract In this study, we propose an efficient numerical framework to attain the solution of the extended Fisher-Kolmogorov (EFK) problem. The temporal derivative in the EFK equation is approximated by utilizing the Crank-Nicolson scheme. Following temporal discretization, the generalized finite difference method (GFDM) with supplementary nodes is utilized to address the nonlinear boundary value problems at each time node. These supplementary nodes are distributed along the boundary to match the number of boundary nodes. By incorporating supplementary nodes, the resulting nonlinear algebraic equations can effectively satisfy the governing equation and boundary conditions of the EFK equation. More >

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ARTICLE

Gamze Yıldırım^1,2, Şuayip Yüzbaşı^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 281-310, 2024, DOI:10.32604/cmes.2024.052181
（This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
Abstract In this study, a numerical method based on the Pell-Lucas polynomials (PLPs) is developed to solve the fractional order HIV/AIDS epidemic model with a treatment compartment. The HIV/AIDS mathematical model with a treatment compartment is divided into five classes, namely, susceptible patients (S), HIV-positive individuals (I), individuals with full-blown AIDS but not receiving ARV treatment (A), individuals being treated (T), and individuals who have changed their sexual habits sufficiently (R). According to the method, by utilizing the PLPs and the collocation points, we convert the fractional order HIV/AIDS epidemic model with a treatment compartment into… More >

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ARTICLE

Umar Shafique^1,*, Mohamed Mahyoub Al-Shamiri², Ali Raza³, Emad Fadhal^4,*, Muhammad Rafiq^5,6, Nauman Ahmed^5,7
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 311-329, 2024, DOI:10.32604/cmes.2024.052383
（This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
Abstract Based on the World Health Organization (WHO), Meningitis is a severe infection of the meninges, the membranes covering the brain and spinal cord. It is a devastating disease and remains a significant public health challenge. This study investigates a bacterial meningitis model through deterministic and stochastic versions. Four-compartment population dynamics explain the concept, particularly the susceptible population, carrier, infected, and recovered. The model predicts the nonnegative equilibrium points and reproduction number, i.e., the Meningitis-Free Equilibrium (MFE), and Meningitis-Existing Equilibrium (MEE). For the stochastic version of the existing deterministic model, the two methodologies studied are transition… More >

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ARTICLE

Pengbiao Zhao¹, Qi Zhong², Jingxue Chen¹, Xiaopei Wang³, Zhen Qin¹, Erqiang Zhou^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 331-352, 2024, DOI:10.32604/cmes.2024.050148
（This article belongs to the Special Issue: Information Security and Trust Issues in the Digital World)
Abstract Information steganography has received more and more attention from scholars nowadays, especially in the area of image steganography, which uses image content to transmit information and makes the existence of secret information undetectable. To enhance concealment and security, the Steganography without Embedding (SWE) method has proven effective in avoiding image distortion resulting from cover modification. In this paper, a novel encrypted communication scheme for image SWE is proposed. It reconstructs the image into a multi-linked list structure consisting of numerous nodes, where each pixel is transformed into a single node with data and pointer domains.… More >

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ARTICLE

Da-Chun Wu^*, Zong-Nan Shih
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 353-383, 2024, DOI:10.32604/cmes.2024.050813
（This article belongs to the Special Issue: Information Security and Trust Issues in the Digital World)
Abstract A new steganographic method by pixel-value differencing (PVD) using general quantization ranges of pixel pairs’ difference values is proposed. The objective of this method is to provide a data embedding technique with a range table with range widths not limited to powers of 2, extending PVD-based methods to enhance their flexibility and data-embedding rates without changing their capabilities to resist security attacks. Specifically, the conventional PVD technique partitions a grayscale image into 1 × 2 non-overlapping blocks. The entire range [0, 255] of all possible absolute values of the pixel pairs’ grayscale differences in the… More >

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ARTICLE

Meng Ma^1,2,*, Liu Fu^1,2, Xu Guo³, Zhi Zhai^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 385-399, 2024, DOI:10.32604/cmes.2024.052585
（This article belongs to the Special Issue: Machine Learning Based Computational Mechanics)
Abstract Partial Differential Equation (PDE) is among the most fundamental tools employed to model dynamic systems. Existing PDE modeling methods are typically derived from established knowledge and known phenomena, which are time-consuming and labor-intensive. Recently, discovering governing PDEs from collected actual data via Physics Informed Neural Networks (PINNs) provides a more efficient way to analyze fresh dynamic systems and establish PED models. This study proposes Sequentially Threshold Least Squares-Lasso (STLasso), a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares (STLS) algorithm, which can complete sparse regression of PDE coefficients with the constraints More >

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ARTICLE

Amit Singhal¹, Krishna Kant Agrawal², Angeles Quezada³, Adrian Rodriguez Aguiñaga⁴, Samantha Jiménez⁴, Satya Prakash Yadav^5,,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 401-441, 2024, DOI:10.32604/cmes.2024.051363
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract The use of Explainable Artificial Intelligence (XAI) models becomes increasingly important for making decisions in smart healthcare environments. It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms. These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence. Nevertheless, the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images. This research presents an advanced investigation of XAI models to classify cancer images. It describes the different levels of explainability… More >

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ARTICLE

Prasanalakshmi Balaji^1,*, Omar Alqahtani¹, Sangita Babu², Mousmi Ajay Chaurasia³, Shanmugapriya Prakasam⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 443-458, 2024, DOI:10.32604/cmes.2024.053158
（This article belongs to the Special Issue: Intelligent Medical Decision Support Systems: Methods and Applications)
Abstract Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an More >

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ARTICLE

Refah Alotaibi¹, Sanku Dey², Ahmed Elshahhat^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 459-489, 2024, DOI:10.32604/cmes.2024.053255
（This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
Abstract A novel inverted generalized gamma (IGG) distribution, proposed for data modelling with an upside-down bathtub hazard rate, is considered. In many real-world practical situations, when a researcher wants to conduct a comparative study of the life testing of items based on cost and duration of testing, censoring strategies are frequently used. From this point of view, in the presence of censored data compiled from the most well-known progressively Type-II censoring technique, this study examines different parameters of the IGG distribution. From a classical point of view, the likelihood and product of spacing estimation methods are… More >

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ARTICLE

Zhen Wang¹, Junsong Xiong¹, Shaofan Li², Xin Lai^1,3,*, Xiang Liu³, Lisheng Liu^1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 491-523, 2024, DOI:10.32604/cmes.2024.052923
（This article belongs to the Special Issue: Peridynamic Theory and Multi-physical/Multiscale Methods for Complex Material Behavior)
Abstract A fluid-structure interaction approach is proposed in this paper based on Non-Ordinary State-Based Peridynamics (NOSB-PD) and Updated Lagrangian Particle Hydrodynamics (ULPH) to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid. In the coupled framework, the NOSB-PD theory describes the deformation and fracture of the solid material structure. ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy. The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid… More >

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ARTICLE

Mohamed Abdel-Mongy¹, Mudassir Iqbal², M. Farag³, Ahmed. M. Yosri^1,*, Fahad Alsharari¹, Saif Eldeen A. S. Yousef⁴
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505
（This article belongs to the Special Issue: Recent Advances in Computational Methods for Performance Assessment of Engineering Structures and Materials against Dynamic Loadings)
Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >

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ARTICLE

Yongjin Jeon, Seungjun Baek^#, Jongsung Kim^*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 545-561, 2024, DOI:10.32604/cmes.2024.052374
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract Quantum computers accelerate many algorithms based on the superposition principle of quantum mechanics. The Grover algorithm provides significant performance to malicious users attacking symmetric key systems. Since the performance of attacks using quantum computers depends on the efficiency of the quantum circuit of the encryption algorithms, research research on the implementation of quantum circuits is essential. This paper presents a new framework to construct quantum circuits of substitution boxes (S-boxes) using system modeling. We model the quantum circuits of S-boxes using two layers: Toffoli and linear layers. We generate vector spaces based on the values… More >

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ByeongYeol An¹, JeaHyuk Yang², Seoyeon Kim², Taeguen Kim^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 563-584, 2024, DOI:10.32604/cmes.2024.052403
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract This paper proposes a new approach to counter cyberattacks using the increasingly diverse malware in cyber security. Traditional signature detection methods that utilize static and dynamic features face limitations due to the continuous evolution and diversity of new malware. Recently, machine learning-based malware detection techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), have gained attention. While these methods demonstrate high performance by leveraging static and dynamic features, they are limited in detecting new malware or variants because they learn based on the characteristics of existing malware. To overcome these limitations, malware… More >

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ARTICLE

Min-Gyu Kim¹, Hwankuk Kim^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 585-607, 2024, DOI:10.32604/cmes.2024.051221
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract In the IoT (Internet of Things) domain, the increased use of encryption protocols such as SSL/TLS, VPN (Virtual Private Network), and Tor has led to a rise in attacks leveraging encrypted traffic. While research on anomaly detection using AI (Artificial Intelligence) is actively progressing, the encrypted nature of the data poses challenges for labeling, resulting in data imbalance and biased feature extraction toward specific nodes. This study proposes a reconstruction error-based anomaly detection method using an autoencoder (AE) that utilizes packet metadata excluding specific node information. The proposed method omits biased packet metadata such as… More >

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Yuwei Xu, Baokang Zhao^*, Huan Zhou, Jinshu Su
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 609-629, 2024, DOI:10.32604/cmes.2024.053462
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract The rapid expansion of artificial intelligence (AI) applications has raised significant concerns about user privacy, prompting the development of privacy-preserving machine learning (ML) paradigms such as federated learning (FL). FL enables the distributed training of ML models, keeping data on local devices and thus addressing the privacy concerns of users. However, challenges arise from the heterogeneous nature of mobile client devices, partial engagement of training, and non-independent identically distributed (non-IID) data distribution, leading to performance degradation and optimization objective bias in FL training. With the development of 5G/6G networks and the integration of cloud computing… More >

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ARTICLE

Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

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Arpita Dinesh Sarang¹, Mohsen Ali Alawami², Ki-Woong Park^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 655-669, 2024, DOI:10.32604/cmes.2024.053434
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract Nowadays, the use of Avatars that are unique digital depictions has increased by users to access Metaverse—a virtual reality environment—through multiple devices and for various purposes. Therefore, the Avatar and Metaverse are being developed with a new theory, application, and design, necessitating the association of more personal data and devices of targeted users every day. This Avatar and Metaverse technology explosion raises privacy and security concerns, leading to cyber attacks. MV-Honeypot, or Metaverse-Honeypot, as a commercial off-the-shelf solution that can counter these cyber attack-causing vulnerabilities, should be developed. To fill this gap, we study user’s More >

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Youngho Park¹, Su Jin Shin², Sang Uk Shin^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 671-688, 2024, DOI:10.32604/cmes.2024.052378
（This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
Abstract Data trading enables data owners and data requesters to sell and purchase data. With the emergence of blockchain technology, research on blockchain-based data trading systems is receiving a lot of attention. Particularly, to reduce the on-chain storage cost, a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform. Moreover, the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace. In the data marketplace, it is a challenge how to trade the data securely… More >

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Khushal Das¹, Fazeel Abid², Jawad Rasheed^3,4,*, Kamlish⁵, Tunc Asuroglu^6,*, Shtwai Alsubai⁷, Safeeullah Soomro⁸
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 689-711, 2024, DOI:10.32604/cmes.2024.051335
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Deaf people or people facing hearing issues can communicate using sign language (SL), a visual language. Many works based on rich source language have been proposed; however, the work using poor resource language is still lacking. Unlike other SLs, the visuals of the Urdu Language are different. This study presents a novel approach to translating Urdu sign language (UrSL) using the UrSL-CNN model, a convolutional neural network (CNN) architecture specifically designed for this purpose. Unlike existing works that primarily focus on languages with rich resources, this study addresses the challenge of translating a sign language… More >

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Sheng Xiang¹, Junhao Ma¹, Qunli Shang¹, Xianbao Wang^1,*, Defu Chen^1,2
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 713-731, 2024, DOI:10.32604/cmes.2024.052759
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection. However, small objects are difficult to detect accurately because they contain less information. Many current methods, particularly those based on Feature Pyramid Network (FPN), address this challenge by leveraging multi-scale feature fusion. However, existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers, leading to suboptimal small object detection. To address this problem, we propose the Two-layer Attention Feature Pyramid Network (TA-FPN), featuring two key modules: the Two-layer Attention Module (TAM) and the… More >

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Yahia Said^1,*, Yahya Alassaf², Refka Ghodhbani³, Yazan Ahmad Alsariera⁴, Taoufik Saidani³, Olfa Ben Rhaiem⁴, Mohamad Khaled Makhdoum¹, Manel Hleili⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 733-749, 2024, DOI:10.32604/cmes.2024.052369
（This article belongs to the Special Issue: Artificial Intelligence Emerging Trends and Sustainable Applications in Image Processing and Computer Vision)
Abstract Enhancing road safety globally is imperative, especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations. Acknowledging the critical role of helmets in rider protection, this paper presents an innovative approach to helmet violation detection using deep learning methodologies. The primary innovation involves the adaptation of the PerspectiveNet architecture, transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone, aimed at bolstering detection capabilities. Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset (IDD) for training and validation, the system demonstrates exceptional More >

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İsmail Akgül^*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 751-770, 2024, DOI:10.32604/cmes.2024.052549
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural… More >

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Lirong Yin¹, Lei Wang¹, Zhuohang Cai², Siyu Lu^2,*, Ruiyang Wang², Ahmed AlSanad³, Salman A. AlQahtani³, Xiaobing Chen⁴, Zhengtong Yin⁵, Xiaolu Li⁶, Wenfeng Zheng^2,3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 771-786, 2024, DOI:10.32604/cmes.2024.052622
（This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
Abstract Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems. However, with the constant evolution of algorithms, data, and computing power, the increasing size and complexity of these models have led to increased training costs and reduced efficiency. This study aims to minimize the inference time of such models while maintaining computational performance. It also proposes a novel Distillation model for PAL-BERT (DPAL-BERT), specifically, employs knowledge distillation, using the PAL-BERT model as the teacher model to train two student models: DPAL-BERT-Bi and DPAL-BERT-C. This research enhances the dataset More >

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Ibrahim Alameri^1,*, Jitka Komarkova², Tawfik Al-Hadhrami³, Abdulsamad Ebrahim Yahya⁴, Atef Gharbi⁵
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 787-807, 2024, DOI:10.32604/cmes.2024.052107
（This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
Abstract This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks (MANETs) from dynamic topologies that pose great challenges because of the mobility of nodes. The main objective was to delve into and refine the application of the Dijkstra's algorithm in this context, a method conventionally esteemed for its efficiency in static networks. Thus, this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm, considering adaptation to the dynamic network conditions that are typical for MANETs. This paper has shown through detailed algorithmic analysis that Dijkstra’s… More >

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Iuon-Chang Lin¹, I-Ling Yeh¹, Ching-Chun Chang², Jui-Chuan Liu³, Chin-Chen Chang^3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 809-822, 2024, DOI:10.32604/cmes.2024.051612
（This article belongs to the Special Issue: Key Technologies and Applications of Blockchain Technology in Supply Chain Intelligence and Trust Establishment)
Abstract Centralized storage and identity identification methods pose many risks, including hacker attacks, data misuse, and single points of failure. Additionally, existing centralized identity management methods face interoperability issues and rely on a single identity provider, leaving users without control over their identities. Therefore, this paper proposes a mechanism for identity identification and data sharing based on decentralized identifiers. The scheme utilizes blockchain technology to store the identifiers and data hashed on the chain to ensure permanent identity recognition and data integrity. Data is stored on InterPlanetary File System (IPFS) to avoid the risk of single More >

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Muaadh A. Alsoufi^1,*, Maheyzah Md Siraj¹, Fuad A. Ghaleb², Muna Al-Razgan³, Mahfoudh Saeed Al-Asaly³, Taha Alfakih³, Faisal Saeed²
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 823-845, 2024, DOI:10.32604/cmes.2024.052112
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract The rapid growth of Internet of Things (IoT) devices has brought numerous benefits to the interconnected world. However, the ubiquitous nature of IoT networks exposes them to various security threats, including anomaly intrusion attacks. In addition, IoT devices generate a high volume of unstructured data. Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks, such as resource constraints and heterogeneous data sources. Given the unpredictable nature of network technologies and diverse intrusion methods, conventional machine-learning approaches seem to lack efficiency. Across numerous research domains, deep learning techniques have demonstrated… More >

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Zengxiang Li¹, Yongchong Wu², Alanoud Al Mazroa³, Donghua Jiang⁴, Jianhua Wu⁵, Xishun Zhu^6,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 847-869, 2024, DOI:10.32604/cmes.2024.051762
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract Hidden capacity, concealment, security, and robustness are essential indicators of hiding algorithms. Currently, hiding algorithms tend to focus on algorithmic capacity, concealment, and security but often overlook the robustness of the algorithms. In practical applications, the container can suffer from damage caused by noise, cropping, and other attacks during transmission, resulting in challenging or even impossible complete recovery of the secret image. An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms. In this proposed algorithm, a secret… More >

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Shengdong Cheng¹, Juncheng Gao^1,*, Hongning Qi^2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 871-892, 2024, DOI:10.32604/cmes.2024.052830
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments… More >

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Sonali Das¹, Saroja Kumar Rout², Sujit Kumar Panda¹, Pradyumna Kumar Mohapatra³, Abdulaziz S. Almazyad⁴, Muhammed Basheer Jasser^5,6,*, Guojiang Xiong⁷, Ali Wagdy Mohamed^8,9
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 893-916, 2024, DOI:10.32604/cmes.2024.051856
（This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)
Abstract In blood or bone marrow, leukemia is a form of cancer. A person with leukemia has an expansion of white blood cells (WBCs). It primarily affects children and rarely affects adults. Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body. Identifying leukemia in the initial stage is vital to providing timely patient care. Medical image-analysis-related approaches grant safer, quicker, and less costly solutions while ignoring the difficulties of these invasive processes. It can be simple to generalize Computer vision (CV)-based and image-processing techniques and eradicate human… More >

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Chih-Ping Wu^*, Ruei-Syuan Chang
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 917-949, 2024, DOI:10.32604/cmes.2024.052307
（This article belongs to the Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures-II)
Abstract This work develops a Hermitian C differential reproducing kernel interpolation meshless (DRKIM) method within the consistent couple stress theory (CCST) framework to study the three-dimensional (3D) microstructure-dependent static flexural behavior of a functionally graded (FG) microplate subjected to mechanical loads and placed under full simple supports. In the formulation, we select the transverse stress and displacement components and their first- and second-order derivatives as primary variables. Then, we set up the differential reproducing conditions (DRCs) to obtain the shape functions of the Hermitian C differential reproducing kernel (DRK) interpolant’s derivatives without using direct differentiation. The interpolant’s… More >

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