Home / Journals / CMES / Vol.140, No.3, 2024
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  • Open AccessOpen Access

    REVIEW

    A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

    Madiha Hameed1,3, Aneela Zameer1,*, Muhammad Asif Zahoor Raja2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124
    Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

  • Open AccessOpen Access

    REVIEW

    A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

    Qusay M. Salih1,2,*, Md. Arafatur Rahman3, Ahmad Firdaus1, Mohammed Rajih Jassim4, Hasan Kahtan5, Jasni Mohamad Zain6, Ahmed Hussein Ali7
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2165-2206, 2024, DOI:10.32604/cmes.2024.028132
    Abstract The race to develop the next generation of wireless networks, known as Sixth Generation (6G) wireless, which will be operational in 2030, has already begun. To realize its full potential over the next decade, 6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ones. However, the studies about 6G are mainly limited and scattered, whereas no bibliometric study covers the 6G field. Thus, this study aims to review, examine, and summarize existing studies and research activities in 6G. This study has examined the Scopus database through a bibliometric analysis of more than More >

    Graphic Abstract

    A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

  • Open AccessOpen Access

    REVIEW

    Applications of Soft Computing Methods in Backbreak Assessment in Surface Mines: A Comprehensive Review

    Mojtaba Yari1,*, Manoj Khandelwal2, Payam Abbasi3, Evangelos I. Koutras4, Danial Jahed Armaghani5,*, Panagiotis G. Asteris4
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2207-2238, 2024, DOI:10.32604/cmes.2024.048071
    Abstract Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to More >

  • Open AccessOpen Access

    REVIEW

    A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

    Deepak Upreti1, Eunmok Yang2, Hyunil Kim3,*, Changho Seo1,4
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2239-2274, 2024, DOI:10.32604/cmes.2024.048932
    (This article belongs to the Special Issue: Artificial Intelligence and Data Science in Healthcare)
    Abstract Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security. It involves constructing machine learning models using datasets spread across several data centers, including medical facilities, clinical research facilities, Internet of Things devices, and even mobile devices. The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information, reducing the risk of data loss, privacy breaches, or data exposure. The application of federated learning in the healthcare industry holds significant promise More >

  • Open AccessOpen Access

    ARTICLE

    Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

    Javaid Ali1, Armando Ciancio2, Kashif Ali Khan3, Nauman Raza4,5, Haci Mehmet Baskonus6,*, Muhammad Luqman1, Zafar-Ullah Khan7
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2275-2296, 2024, DOI:10.32604/cmes.2024.046923
    Abstract This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model’s necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model’s physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical More >

  • Open AccessOpen Access

    ARTICLE

    Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

    Tianyu Wang1,2, Kongyang Chen2,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2297-2314, 2024, DOI:10.32604/cmes.2024.049964
    Abstract Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, these mechanisms face several challenges in traditional crowdsourcing systems: 1) platform security lacks robust guarantees and may be susceptible to attacks; 2) there exists a potential for large-scale privacy breaches; and 3) incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations, occasionally lacking… More >

  • Open AccessOpen Access

    ARTICLE

    AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

    Lirong Yin1, Lei Wang1, Siyu Lu2,*, Ruiyang Wang2, Haitao Ren2, Ahmed AlSanad3, Salman A. AlQahtani3, Zhengtong Yin4, Xiaolu Li5, Wenfeng Zheng3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2315-2347, 2024, DOI:10.32604/cmes.2024.050853
    Abstract At present, super-resolution algorithms are employed to tackle the challenge of low image resolution, but it is difficult to extract differentiated feature details based on various inputs, resulting in poor generalization ability. Given this situation, this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block (AFB) for feature extraction. This module mainly comprises dynamic convolution, attention mechanism, and pixel-based gating mechanism. Combined with dynamic convolution with scale information, the network can extract more differentiated feature information. The introduction of a channel More >

  • Open AccessOpen Access

    ARTICLE

    An Elastoplastic Fracture Model Based on Bond-Based Peridynamics

    Liping Zu1, Yaxun Liu1, Haoran Zhang1, Lisheng Liu2,*, Xin Lai2,*, Hai Mei2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2349-2371, 2024, DOI:10.32604/cmes.2024.050488
    Abstract Fracture in ductile materials often occurs in conjunction with plastic deformation. However, in the bond-based peridynamic (BB-PD) theory, the classic mechanical stress is not defined inherently. This makes it difficult to describe plasticity directly using the classical plastic theory. To address the above issue, a unified bond-based peridynamics model was proposed as an effective tool to solve elastoplastic fracture problems. Compared to the existing models, the proposed model directly describes the elastoplastic theory at the bond level without the need for additional calculation means. The results obtained in the context of this model are shown More >

  • Open AccessOpen Access

    ARTICLE

    A Framework Based on the DAO and NFT in Blockchain for Electronic Document Sharing

    Lin Chen1, Jiaming Zhu1, Yuting Xu1, Huanqin Zheng1, Shen Su1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2373-2395, 2024, DOI:10.32604/cmes.2024.049996
    Abstract In the information age, electronic documents (e-documents) have become a popular alternative to paper documents due to their lower costs, higher dissemination rates, and ease of knowledge sharing. However, digital copyright infringements occur frequently due to the ease of copying, which not only infringes on the rights of creators but also weakens their creative enthusiasm. Therefore, it is crucial to establish an e-document sharing system that enforces copyright protection. However, the existing centralized system has outstanding vulnerabilities, and the plagiarism detection algorithm used cannot fully detect the context, semantics, style, and other factors of the… More >

  • Open AccessOpen Access

    ARTICLE

    Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

    Hongchi Liu1, Xing Deng1,*, Haijian Shao1,2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737
    Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More >

    Graphic Abstract

    Advancements in Remote Sensing Image Dehazing: Introducing URA-Net with Multi-Scale Dense Feature Fusion Clusters and Gated Jump Connection

  • Open AccessOpen Access

    ARTICLE

    GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

    Md. Atiqur Rahman1, Mustavi Ibne Masum1, Khan Md Hasib2, M. F. Mridha3,*, Sultan Alfarhood4, Mejdl Safran4,*, Dunren Che5
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760
    Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

  • Open AccessOpen Access

    ARTICLE

    CoopAI-Route: DRL Empowered Multi-Agent Cooperative System for Efficient QoS-Aware Routing for Network Slicing in Multi-Domain SDN

    Meignanamoorthi Dhandapani*, V. Vetriselvi, R. Aishwarya
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2449-2486, 2024, DOI:10.32604/cmes.2024.050986
    Abstract The emergence of beyond 5G networks has the potential for seamless and intelligent connectivity on a global scale. Network slicing is crucial in delivering services for different, demanding vertical applications in this context. Next-generation applications have time-sensitive requirements and depend on the most efficient routing path to ensure packets reach their intended destinations. However, the existing IP (Internet Protocol) over a multi-domain network faces challenges in enforcing network slicing due to minimal collaboration and information sharing among network operators. Conventional inter-domain routing methods, like Border Gateway Protocol (BGP), cannot make routing decisions based on performance,… More >

  • Open AccessOpen Access

    ARTICLE

    Film Flow of Nano-Micropolar Fluid with Dissipation Effect

    Abuzar Abid Siddiqui1, Mustafa Turkyilmazoglu2,3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2487-2512, 2024, DOI:10.32604/cmes.2024.050525
    Abstract The physical problem of the thin film flow of a micropolar fluid over a dynamic and inclined substrate under the influence of gravitational and thermal forces in the presence of nanoparticles is formulated. Five different types of nanoparticle samples are accounted for in this current study, namely gold Au, silver Ag, molybdenum disulfide MoS2, aluminum oxide Al2O3, and silicon dioxide SiO2. Blood, a micropolar fluid, serves as the common base fluid. An exact closed-form solution for this problem is derived for the first time in the literature. The results are particularly validated against those for the Newtonian fluid… More >

  • Open AccessOpen Access

    ARTICLE

    Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

    Meng Zhang1,2, Xiangyang Luo1,2,*, Ningbo Huang2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2513-2532, 2024, DOI:10.32604/cmes.2024.050517
    Abstract Geolocating social media users aims to discover the real geographical locations of users from their publicly available data, which can support online location-based applications such as disaster alerts and local content recommendations. Social relationship-based methods represent a classical approach for geolocating social media. However, geographically proximate relationships are sparse and challenging to discern within social networks, thereby affecting the accuracy of user geolocation. To address this challenge, we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence (NGSI) to improve geolocation accuracy. Firstly, we propose a method for evaluating the homophily… More >

  • Open AccessOpen Access

    ARTICLE

    A High-Accuracy Curve Boundary Recognition Method Based on the Lattice Boltzmann Method and Immersed Moving Boundary Method

    Jie-Di Weng1, Yong-Zheng Jiang1,*, Long-Chao Chen1, Xu Zhang1, Guan-Yong Zhang2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2533-2557, 2024, DOI:10.32604/cmes.2024.051232
    Abstract Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved boundaries is vital in aircraft design, ocean, and construction engineering. However, current methods such as Lattice Boltzmann (LBM) and the immersion boundary method based on solid ratio (IMB) have limitations in identifying custom curved boundaries. Meanwhile, IBM based on velocity correction (IBM-VC) suffers from inaccuracies and numerical instability. Therefore, this study introduces a high-accuracy curve boundary recognition method (IMB-CB), which identifies boundary nodes by moving the search box, and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,… More >

    Graphic Abstract

    A High-Accuracy Curve Boundary Recognition Method Based on the Lattice Boltzmann Method and Immersed Moving Boundary Method

  • Open AccessOpen Access

    ARTICLE

    MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge

    Tengda Li, Gang Wang, Qiang Fu*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2559-2586, 2024, DOI:10.32604/cmes.2024.052039
    Abstract Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation (DTA) and high-dimensional decision space with single agent, this paper combines the deep reinforcement learning (DRL) theory and an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG-D2) algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA. The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and considers the introduction of a double noise mechanism to increase the action exploration… More >

  • Open AccessOpen Access

    ARTICLE

    Quantifying Uncertainty in Dielectric Solids’ Mechanical Properties Using Isogeometric Analysis and Conditional Generative Adversarial Networks

    Shuai Li1, Xiaodong Zhao1,2,*, Jinghu Zhou1, Xiyue Wang1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2587-2611, 2024, DOI:10.32604/cmes.2024.052203
    Abstract Accurate quantification of the uncertainty in the mechanical characteristics of dielectric solids is crucial for advancing their application in high-precision technological domains, necessitating the development of robust computational methods. This paper introduces a Conditional Generation Adversarial Network Isogeometric Analysis (CGAN-IGA) to assess the uncertainty of dielectric solids’ mechanical characteristics. IGA is utilized for the precise computation of electric potentials in dielectric, piezoelectric, and flexoelectric materials, leveraging its advantage of integrating seamlessly with Computer-Aided Design (CAD) models to maintain exact geometrical fidelity. The CGAN method is highly efficient in generating models for piezoelectric and flexoelectric materials, More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Penetration Testing Path Planning Based on Reinforcement Learning with Episodic Memory

    Ziqiao Zhou1, Tianyang Zhou1,*, Jinghao Xu2, Junhu Zhu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2613-2634, 2024, DOI:10.32604/cmes.2023.028553
    (This article belongs to the Special Issue: Cyberspace Intelligent Mapping and Situational Awareness)
    Abstract Intelligent penetration testing is of great significance for the improvement of the security of information systems, and the critical issue is the planning of penetration test paths. In view of the difficulty for attackers to obtain complete network information in realistic network scenarios, Reinforcement Learning (RL) is a promising solution to discover the optimal penetration path under incomplete information about the target network. Existing RL-based methods are challenged by the sizeable discrete action space, which leads to difficulties in the convergence. Moreover, most methods still rely on experts’ knowledge. To address these issues, this paper… More >

  • Open AccessOpen Access

    ARTICLE

    Rock Mass Quality Rating Based on the Multi-Criteria Grey Metric Space

    Miloš Gligorić1,*, Zoran Gligorić1, Saša Jovanović2, Suzana Lutovac1, Dragan Pamučar3,4, Ivan Janković1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2635-2664, 2024, DOI:10.32604/cmes.2024.050898
    (This article belongs to the Special Issue: Meta-heuristic Algorithms in Materials Science and Engineering)
    Abstract Assessment of rock mass quality significantly impacts the design and construction of underground and open-pit mines from the point of stability and economy. This study develops the novel Gromov-Hausdorff distance for rock quality (GHDQR) methodology for rock mass quality rating based on multi-criteria grey metric space. It usually presents the quality of surrounding rock by classes (metric spaces) with specified properties and adequate interval-grey numbers. Measuring the distance between surrounding rock sample characteristics and existing classes represents the core of this study. The Gromov-Hausdorff distance is an especially useful discriminant function, i.e., a classifier to… More >

  • Open AccessOpen Access

    ARTICLE

    An Integrated Bipolar Picture Fuzzy Decision Driven System to Scrutinize Food Waste Treatment Technology through Assorted Factor Analysis

    Navaneethakrishnan Suganthi Keerthana Devi1, Samayan Narayanamoorthy1, Thirumalai Nallasivan Parthasarathy1, Chakkarapani Sumathi Thilagasree2, Dragan Pamucar3,4,*, Vladimir Simic5,6, Hasan Dinçer7,8, Serhat Yüksel7,8
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2665-2687, 2024, DOI:10.32604/cmes.2024.050954
    (This article belongs to the Special Issue: Control Systems and Machine Learning for Intelligent Computing)
    Abstract Food Waste (FW) is a pressing environmental concern that affects every country globally. About one-third of the food that is produced ends up as waste, contributing to the carbon footprint. Hence, the FW must be properly treated to reduce environmental pollution. This study evaluates a few available Food Waste Treatment (FWT) technologies, such as anaerobic digestion, composting, landfill, and incineration, which are widely used. A Bipolar Picture Fuzzy Set (BPFS) is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model. A novel Criteria Importance Through… More >

  • Open AccessOpen Access

    ARTICLE

    LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

    Brij B. Gupta1,2,3,*, Akshat Gaurav4, Razaz Waheeb Attar5, Varsha Arya6,7, Ahmed Alhomoud8, Kwok Tai Chui9
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2689-2706, 2024, DOI:10.32604/cmes.2024.050825
    (This article belongs to the Special Issue: Control Systems and Machine Learning for Intelligent Computing)
    Abstract This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion More >

  • Open AccessOpen Access

    ARTICLE

    Finite Difference-Peridynamic Differential Operator for Solving Transient Heat Conduction Problems

    Chunlei Ruan1,2,*, Cengceng Dong1, Zeyue Zhang1, Boyu Chen1, Zhijun Liu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2707-2728, 2024, DOI:10.32604/cmes.2024.050003
    (This article belongs to the Special Issue: New Trends on Meshless Method and Numerical Analysis)
    Abstract Transient heat conduction problems widely exist in engineering. In previous work on the peridynamic differential operator (PDDO) method for solving such problems, both time and spatial derivatives were discretized using the PDDO method, resulting in increased complexity and programming difficulty. In this work, the forward difference formula, the backward difference formula, and the centered difference formula are used to discretize the time derivative, while the PDDO method is used to discretize the spatial derivative. Three new schemes for solving transient heat conduction equations have been developed, namely, the forward-in-time and PDDO in space (FT-PDDO) scheme,… More >

  • Open AccessOpen Access

    ARTICLE

    Composite Fractional Trapezoidal Rule with Romberg Integration

    Iqbal M. Batiha1,2,*, Rania Saadeh3, Iqbal H. Jebril1, Ahmad Qazza3, Abeer A. Al-Nana4, Shaher Momani2,5
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2024.051588
    (This article belongs to the Special Issue: New Trends on Meshless Method and Numerical Analysis)
    Abstract The aim of this research is to demonstrate a novel scheme for approximating the Riemann-Liouville fractional integral operator. This would be achieved by first establishing a fractional-order version of the -point Trapezoidal rule and then by proposing another fractional-order version of the -composite Trapezoidal rule. In particular, the so-called divided-difference formula is typically employed to derive the -point Trapezoidal rule, which has accordingly been used to derive a more accurate fractional-order formula called the -composite Trapezoidal rule. Additionally, in order to increase the accuracy of the proposed approximations by reducing the true errors, we incorporate More >

  • Open AccessOpen Access

    ARTICLE

    Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

    Farman Saifi1,*, Mohd Javaid1, Abid Haleem1, S. M. Anas2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2747-2777, 2024, DOI:10.32604/cmes.2024.051490
    (This article belongs to the Special Issue: Recent Advances in Computational Methods for Performance Assessment of Engineering Structures and Materials against Dynamic Loadings)
    Abstract Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infrastructure systems and networks capable of withstanding blast loading. Initially centered on high-profile facilities such as embassies and petrochemical plants, this concern now extends to a wider array of infrastructures and facilities. Engineers and scholars increasingly prioritize structural safety against explosions, particularly to prevent disproportionate collapse and damage to nearby structures. Urbanization has further amplified the reliance on oil and gas pipelines, making them vital for urban life and prime targets for terrorist activities. Consequently, there is a growing imperative for computational… More >

  • Open AccessOpen Access

    ARTICLE

    Time Parameter Based Low-Energy Data Encryption Method for Mobile Applications

    Li-Woei Chen1, Kun-Lin Tsai2,*, Fang-Yie Leu3, Wen-Cheng Jiang2, Shih-Ting Tseng2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2779-2794, 2024, DOI:10.32604/cmes.2024.052124
    (This article belongs to the Special Issue: Advanced Security for Future Mobile Internet: A Key Challenge for the Digital Transformation)
    Abstract Various mobile devices and applications are now used in daily life. These devices require high-speed data processing, low energy consumption, low communication latency, and secure data transmission, especially in 5G and 6G mobile networks. High-security cryptography guarantees that essential data can be transmitted securely; however, it increases energy consumption and reduces data processing speed. Therefore, this study proposes a low-energy data encryption (LEDE) algorithm based on the Advanced Encryption Standard (AES) for improving data transmission security and reducing the energy consumption of encryption in Internet-of-Things (IoT) devices. In the proposed LEDE algorithm, the system time More >

  • Open AccessOpen Access

    ARTICLE

    Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

    Naif Alotaibi1, A. S. Al-Moisheer2, Ibrahim Elbatal1, Mohammed Elgarhy3,4, Ehab M. Almetwally1,5,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2795-2823, 2024, DOI:10.32604/cmes.2024.049188
    (This article belongs to the Special Issue: Frontiers in Parametric Survival Models: Incorporating Trigonometric Baseline Distributions, Machine Learning, and Beyond)
    Abstract This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of More >

  • Open AccessOpen Access

    ARTICLE

    A Novel ISSA–DELM Model for Predicting Rock Mass Permeability

    Chen Xing1, Leihua Yao1,*, Yingdong Wang2
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2825-2848, 2024, DOI:10.32604/cmes.2024.049330
    (This article belongs to the Special Issue: Intelligent Analysis of Imperfect Data in Complex Scenes: Modeling, Learning, and Optimization)
    Abstract In pumped storage projects, the permeability of rock masses is a crucial parameter in engineering design and construction. The rock mass permeability coefficient (K) is influenced by various geological parameters, and previous studies aimed to establish an accurate relationship between K and geological parameters. This study uses the improved sparrow search algorithm (ISSA) to optimize the parameter settings of the deep extreme learning machine (DELM), constructing a prediction model with flexible parameter selection and high accuracy. First, the Spearman method is applied to analyze the correlation between geological parameters. A sample database is built by comprehensively… More >

  • Open AccessOpen Access

    ARTICLE

    Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

    Ronghao Pan, José Antonio García-Díaz*, Rafael Valencia-García
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2849-2868, 2024, DOI:10.32604/cmes.2024.049631
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
    Abstract Large Language Models (LLMs) are increasingly demonstrating their ability to understand natural language and solve complex tasks, especially through text generation. One of the relevant capabilities is contextual learning, which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates. In recent years, the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior. In this study, we investigate the ability of different LLMs, ranging from zero-shot… More >

  • Open AccessOpen Access

    ARTICLE

    Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

    Adéla Hamplová1,*, Alexey Lyavdansky2,*, Tomáš Novák1, Ondřej Svojše1, David Franc1, Arnošt Veselý1
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
    Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

  • Open AccessOpen Access

    ARTICLE

    In-Depth Study of Potential-Based Routing and New Exploration of Its Scheduling Integration

    Jihoon Sung1, Yeunwoong Kyung2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2891-2911, 2024, DOI:10.32604/cmes.2024.051772
    (This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)
    Abstract Industrial wireless mesh networks (WMNs) have been widely deployed in various industrial sectors, providing services such as manufacturing process monitoring, equipment control, and sensor data collection. A notable characteristic of industrial WMNs is their distinct traffic pattern, where the majority of traffic flows originate from mesh nodes and are directed towards mesh gateways. In this context, this paper adopts and revisits a routing algorithm known as ALFA (autonomous load-balancing field-based anycast routing), tailored specifically for anycast (one-to-one-of-many) networking in WMNs, where traffic flows can be served through any one of multiple gateways. In essence, the… More >

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    ARTICLE

    Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

    Lina Dong, Yeming Dai*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2913-2956, 2024, DOI:10.32604/cmes.2024.049214
    (This article belongs to the Special Issue: Data-Driven Robust Group Decision-Making Optimization and Application)
    Abstract This study delves into the formation dynamics of alliances within a closed-loop supply chain (CLSC) that encompasses a manufacturer, a retailer, and an e-commerce platform. It leverages Stackelberg game for this exploration, contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models. The non-alliance model acts as a crucial benchmark, enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations. Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships. We thoroughly investigate the consequences of… More >

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    ARTICLE

    Enhancing Critical Path Problem in Neutrosophic Environment Using Python

    M. Navya Pratyusha, Ranjan Kumar*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2957-2976, 2024, DOI:10.32604/cmes.2024.051581
    (This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
    Abstract In the real world, one of the most common problems in project management is the unpredictability of resources and timelines. An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach, often known as neutrosophic logic. Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number. This innovative approach evaluates the inherent uncertainty in project durations of the planning phase, which enhances the potential significance of the decision-making process in the project. Our proposed method, for the first time… More >

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    ARTICLE

    Calculation of Mass Concrete Temperature and Creep Stress under the Influence of Local Air Heat Transfer

    Heng Zhang1,2, Chao Su2,*, Xiaohu Chen1, Zhizhong Song1, Weijie Zhan3
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2977-3000, 2024, DOI:10.32604/cmes.2024.047972
    (This article belongs to the Special Issue: Structural Design and Optimization)
    Abstract Temperature-induced cracking during the construction of mass concrete is a significant concern. Numerical simulations of concrete temperature have primarily assumed that the concrete is placed in an open environment. The problem of heat transfer between the air and concrete has been simplified to the concrete’s heat dissipation boundary. However, in the case of tubular concrete structures, where air inlet and outlet are relatively limited, the internal air temperature does not dissipate promptly to the external environment as it rises. To accurately simulate the temperature and creep stress in tubular concrete structures with enclosed air spaces… More >

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    ARTICLE

    A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment

    Weijian Song1,, Xi Li1,, Peng Chen1,*, Juan Chen1, Jianhua Ren2, Yunni Xia3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3001-3016, 2024, DOI:10.32604/cmes.2024.048563
    (This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)
    Abstract With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques.… More >

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    ARTICLE

    Dynamic Hypergraph Modeling and Robustness Analysis for SIoT

    Yue Wan, Nan Jiang*, Ziyu Liu
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3017-3034, 2024, DOI:10.32604/cmes.2024.051101
    (This article belongs to the Special Issue: Privacy-Preserving Technologies for Large-scale Artificial Intelligence)
    Abstract The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT’s focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation… More >

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    ARTICLE

    FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

    Rui Wang1, Zhihui Liu2,*, Hongdi Liu3, Baozhong Su4, Chuanyi Ma5
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806
    (This article belongs to the Special Issue: Multiscale, Multifield, and Continuum-Discontinuum Analysis in Geomechanics )
    Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

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    ARTICLE

    Influence of High-Density Bedding Plane Characteristics on Hydraulic Fracture Propagation in Shale Oil Reservoir

    Xiao Yan1,2,3, Di Wang1,2,4, Haitao Yu1,2,3,5,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3051-3071, 2024, DOI:10.32604/cmes.2024.051832
    (This article belongs to the Special Issue: Multiscale, Multifield, and Continuum-Discontinuum Analysis in Geomechanics )
    Abstract The existence of high-density bedding planes is a typical characteristic of shale oil reservoirs. Understanding the behavior of hydraulic fracturing in high-density laminated rocks is significant for promoting shale oil production. In this study, a hydraulic fracturing model considering tensile failure and frictional slip of the bedding planes is established within the framework of the unified pipe-interface element method (UP-IEM). The model developed for simulating the interaction between the hydraulic fracture and the bedding plane is validated by comparison with experimental results. The hydraulic fracturing patterns in sealed and unsealed bedding planes are compared. Additionally,… More >

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    ARTICLE

    Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

    Refah Alotaibi1, Hoda Rezk2, Ahmed Elshahhat3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3073-3103, 2024, DOI:10.32604/cmes.2024.050606
    (This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
    Abstract A new one-parameter Chris-Jerry distribution, created by mixing exponential and gamma distributions, is discussed in this article in the presence of incomplete lifetime data. We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry (CJ) distribution. When the indicated censored data is present, Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices, including the hazard rate and reliability functions. We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity. More >

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    ARTICLE

    Impact Performance Research of Re-Entrant Octagonal Negative Poisson’s Ratio Honeycomb with Gradient Design

    Yiyuan Li1, Yongjing Li1,2, Shilin Yan1,2,*, Pin Wen1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3105-3119, 2024, DOI:10.32604/cmes.2024.051375
    (This article belongs to the Special Issue: Advanced Structural Optimization Methods and their Applications in Designing Metamaterials)
    Abstract Based on the traditional re-entrant honeycomb, a novel re-entrant octagon honeycomb (ROH) is proposed. The deformation mode of the honeycomb under quasi-static compression is analyzed by numerical simulation, and the results are in good agreement with the experimental ones. The deformation modes, mechanical properties, and energy absorption characteristics of ROH along the impact and perpendicular directions gradient design are investigated under different velocities. The results indicated that the deformation mode of ROH is affected by gradient design along the direction of impact and impact speed. In addition, gradient design along the direction of impact can… More >

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