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This study presents a hybrid 3D nasal reconstruction approach using 2D facial images, combining photographic input with landmark detection, mesh deformation, and 3D Morphable Models. The method achieves high accuracy and interpretability without relying on deep learning. Applications include personalized medical and cosmetic nasal modeling, offering a transparent alternative to neural network-based reconstructions.
The cover image was produced with AI-generated content via Canva, and the authors verify that it contains no copyrighted elements or misleading representations.

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  • Open AccessOpen Access

    ARTICLE

    Non-Neural 3D Nasal Reconstruction: A Sparse Landmark Algorithmic Approach for Medical Applications

    Nguyen Khac Toan1, Ho Nguyen Anh Tuan2, Nguyen Truong Thinh1,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1273-1295, 2025, DOI:10.32604/cmes.2025.064218 - 30 May 2025
    (This article belongs to the Special Issue: Recent Advances in Signal Processing and Computer Vision)
    Abstract This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods. The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery. The approach leverages advanced image processing techniques, 3D Morphable Models (3DMM), and deformation techniques to overcome the limitations of deep learning models, particularly addressing the interpretability issues commonly encountered in medical applications. The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm. Sub-landmarks… More >

    Graphic Abstract

    Non-Neural 3D Nasal Reconstruction: A Sparse Landmark Algorithmic Approach for Medical Applications

  • Open AccessOpen Access

    EDITORIAL

    Introduction to the Special Issue on Mathematical Aspects of Computational Biology and Bioinformatics-II

    Dumitru Baleanu1,2, Carla M. A. Pinto3, Sunil Kumar4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1297-1299, 2025, DOI:10.32604/cmes.2025.067010 - 30 May 2025
    (This article belongs to the Special Issue: Mathematical Aspects of Computational Biology and Bioinformatics-II)
    Abstract This article has no abstract. More >

  • Open AccessOpen Access

    REVIEW

    Review and Comparative Analysis of System Identification Methods for Perturbed Motorized Systems

    Helen Shin Huey Wee, Nur Syazreen Ahmad*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1301-1354, 2025, DOI:10.32604/cmes.2025.063611 - 30 May 2025
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract This paper reviews recent advancements in system identification methods for perturbed motorized systems, focusing on brushed DC motors, brushless DC motors, and permanent magnet synchronous motors. It examines data acquisition setups and evaluates conventional and metaheuristic optimization algorithms, highlighting their advantages, limitations, and applications. The paper explores emerging trends in model structures and parameter optimization techniques that address specific perturbations such as varying loads, noise, and friction. A comparative performance analysis is also included to assess several widely used optimization methods, including least squares (LS), particle swarm optimization (PSO), grey wolf optimizer (GWO), bat algorithm… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Based Lip-Reading for Vocal Impaired Patient Rehabilitation

    Chiara Innocente1,*, Matteo Boemio2, Gianmarco Lorenzetti2, Ilaria Pulito2, Diego Romagnoli2, Valeria Saponaro2, Giorgia Marullo1, Luca Ulrich1, Enrico Vezzetti1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1355-1379, 2025, DOI:10.32604/cmes.2025.063186 - 30 May 2025
    Abstract Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To… More >

  • Open AccessOpen Access

    ARTICLE

    Methodology for Detecting Non-Technical Energy Losses Using an Ensemble of Machine Learning Algorithms

    Irbek Morgoev1, Roman Klyuev2,*, Angelika Morgoeva1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1381-1399, 2025, DOI:10.32604/cmes.2025.064502 - 30 May 2025
    Abstract Non-technical losses (NTL) of electric power are a serious problem for electric distribution companies. The solution determines the cost, stability, reliability, and quality of the supplied electricity. The widespread use of advanced metering infrastructure (AMI) and Smart Grid allows all participants in the distribution grid to store and track electricity consumption. During the research, a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings. This model is an ensemble meta-algorithm (stacking) that generalizes the algorithms of random… More >

  • Open AccessOpen Access

    ARTICLE

    Confidence Intervals for the Reliability of Dependent Systems: Integrating Frailty Models and Copula-Based Methods

    Osnamir E. Bru-Cordero1, Cecilia Castro2, Víctor Leiva3,*, Mario C. Jaramillo-Elorza4
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1401-1431, 2025, DOI:10.32604/cmes.2025.064487 - 30 May 2025
    Abstract Most reliability studies assume large samples or independence among components, but these assumptions often fail in practice, leading to imprecise inference. We address this issue by constructing confidence intervals (CIs) for the reliability of two-component systems with Weibull distributed failure times under a copula-frailty framework. Our construction integrates gamma-distributed frailties to capture unobserved heterogeneity and a copula-based dependence structure for correlated failures. The main contribution of this work is to derive adjusted CIs that explicitly incorporate the copula parameter in the variance-covariance matrix, achieving near-nominal coverage probabilities even in small samples or highly dependent settings. More >

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    ARTICLE

    Optimal Fuzzy Tracking Synthesis for Nonlinear Discrete-Time Descriptor Systems with T-S Fuzzy Modeling Approach

    Yi-Chen Lee1, Yann-Horng Lin2, Wen-Jer Chang2,*, Muhammad Shamrooz Aslam3,*, Zi-Yao Lin2
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1433-1461, 2025, DOI:10.32604/cmes.2025.064717 - 30 May 2025
    Abstract An optimal fuzzy tracking synthesis for nonlinear discrete-time descriptor systems is discussed through the Parallel Distributed Compensation (PDC) approach and the Proportional-Difference (P-D) feedback framework. Based on the Takagi-Sugeno Fuzzy Descriptor Model (T-SFDM), a nonlinear discrete-time descriptor system is represented as several linear fuzzy subsystems, which facilitates the linear P-D feedback technique and streamlines the fuzzy controller design process. Leveraging the P-D feedback fuzzy controller, the closed-loop T-SFDM can be transformed into a standard system that guarantees non-impulsiveness and causality for the nonlinear discrete-time descriptor system. In view of the disturbance problems, a passive performance… More >

  • Open AccessOpen Access

    ARTICLE

    Developed Time-Optimal Model Predictive Static Programming Method with Fish Swarm Optimization for Near-Space Vehicle

    Yuanzhuo Wang, Honghua Dai*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1463-1484, 2025, DOI:10.32604/cmes.2025.064416 - 30 May 2025
    Abstract To establish the optimal reference trajectory for a near-space vehicle under free terminal time, a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization. First, the model predictive static programming method is developed by incorporating neighboring terms and trust region, enabling rapid generation of precise optimal solutions. Next, an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution, while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution. To validate the feasibility and accuracy of the proposed method, a near-space More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Approach Deep Learning Framework for Automatic Detection of Diseases in Retinal Fundus Images

    Kachi Anvesh1,2, Bharati M. Reshmi2,3, Shanmugasundaram Hariharan4, H. Venkateshwara Reddy5, Murugaperumal Krishnamoorthy6, Vinay Kukreja7, Shih-Yu Chen8,9,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1485-1517, 2025, DOI:10.32604/cmes.2025.063239 - 30 May 2025
    Abstract Automated classification of retinal fundus images is essential for identifying eye diseases, though there is earlier research on applying deep learning models designed especially for detecting tessellation in retinal fundus images. This study classifies 4 classes of retinal fundus images with 3 diseased fundus images and 1 normal fundus image, by creating a refined VGG16 model to categorize fundus pictures into tessellated, normal, myopia, and choroidal neovascularization groups. The approach utilizes a VGG16 architecture that has been altered with unique fully connected layers and regularization using dropouts, along with data augmentation techniques (rotation, flip, and… More >

  • Open AccessOpen Access

    ARTICLE

    Optimization of Reconfiguration and Resource Allocation for Distributed Generation and Capacitor Banks Using NSGA-II: A Multi-Scenario Approach

    Tareq Hamadneh1, Belal Batiha2, Frank Werner3,*, Mehrdad Ahmadi Kamarposhti4,*, Ilhami Colak5, El Manaa Barhoumi6
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1519-1548, 2025, DOI:10.32604/cmes.2025.063571 - 30 May 2025
    Abstract Reconfiguration, as well as optimal utilization of distributed generation sources and capacitor banks, are highly effective methods for reducing losses and improving the voltage profile, or in other words, the power quality in the power distribution system. Researchers have considered the use of distributed generation resources in recent years. There are numerous advantages to utilizing these resources, the most significant of which are the reduction of network losses and enhancement of voltage stability. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Intersect Mutation Differential Evolution (IMDE) algorithms are used in this… More >

  • Open AccessOpen Access

    ARTICLE

    Dynamic Response of Bridge Pile Foundations under Pile-Soil-Fault Interaction in Seismic Areas

    Yujie Li1, Zhongju Feng1,*, Fuchun Wang1, Jiang Guan2, Xiaoqian Ma3
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1549-1573, 2025, DOI:10.32604/cmes.2025.064785 - 30 May 2025
    Abstract To study the dynamic response rules of pile foundations of mega-bridges over faults in strong seismic areas, a finite element model of the pile foundation-soil-fault interaction of the Haiwen Bridge is established. The 0.2–0.6 g peak acceleration of the 5010 seismic waves is input to study the effect of the seismic wave of different intensities and the distance changes between the fault and the pile foundation on the dynamic response of the pile body. The results show that the soil layer covering the bedrock amplifies the peak pile acceleration, and the amplifying effect decreases with… More >

  • Open AccessOpen Access

    ARTICLE

    Deepfake Detection Using Adversarial Neural Network

    Priyadharsini Selvaraj1,*, Senthil Kumar Jagatheesaperumal2, Karthiga Marimuthu1, Oviya Saravanan1, Bader Fahad Alkhamees3, Mohammad Mehedi Hassan3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1575-1594, 2025, DOI:10.32604/cmes.2025.064138 - 30 May 2025
    Abstract With expeditious advancements in AI-driven facial manipulation techniques, particularly deepfake technology, there is growing concern over its potential misuse. Deepfakes pose a significant threat to society, particularly by infringing on individuals’ privacy. Amid significant endeavors to fabricate systems for identifying deepfake fabrications, existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations, thereby hindering their broad applicability to images and videos produced by unfamiliar technologies. In this manuscript, we endorse resilient training tactics to amplify generalization capabilities. In adversarial training, models are trained using More >

  • Open AccessOpen Access

    ARTICLE

    A Low Light Image Enhancement Method Based on Dehazing Physical Model

    Wencheng Wang1,2,*, Baoxin Yin1,2, Lei Li2,*, Lun Li1, Hongtao Liu1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1595-1616, 2025, DOI:10.32604/cmes.2025.063595 - 30 May 2025
    Abstract In low-light environments, captured images often exhibit issues such as insufficient clarity and detail loss, which significantly degrade the accuracy of subsequent target recognition tasks. To tackle these challenges, this study presents a novel low-light image enhancement algorithm that leverages virtual hazy image generation through dehazing models based on statistical analysis. The proposed algorithm initiates the enhancement process by transforming the low-light image into a virtual hazy image, followed by image segmentation using a quadtree method. To improve the accuracy and robustness of atmospheric light estimation, the algorithm incorporates a genetic algorithm to optimize the… More >

  • Open AccessOpen Access

    ARTICLE

    Average Run Length in TEWMA Control Charts: Analytical Solutions for AR(p) Processes and Real Data Applications

    Sirawit Makaew, Yupaporn Areepong*, Saowanit Sukparungsee
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1617-1634, 2025, DOI:10.32604/cmes.2025.063459 - 30 May 2025
    Abstract This study aims to examine the explicit solution for calculating the Average Run Length (ARL) on the triple exponentially weighted moving average (TEWMA) control chart applied to autoregressive model (AR(p)), where AR(p) is an autoregressive model of order p, representing a time series with dependencies on its p previous values. Additionally, the study evaluates the accuracy of both explicit and numerical integral equation (NIE) solutions for AR(p) using the TEWMA control chart, focusing on the absolute percentage relative error. The results indicate that the explicit and approximate solutions are in close agreement. Furthermore, the study More >

  • Open AccessOpen Access

    ARTICLE

    A Novel Data-Annotated Label Collection and Deep-Learning Based Medical Image Segmentation in Reversible Data Hiding Domain

    Lord Amoah1,2, Jinwei Wang1,2,3,*, Bernard-Marie Onzo1,2
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1635-1660, 2025, DOI:10.32604/cmes.2025.063992 - 30 May 2025
    Abstract Medical image segmentation, i.e., labeling structures of interest in medical images, is crucial for disease diagnosis and treatment in radiology. In reversible data hiding in medical images (RDHMI), segmentation consists of only two regions: the focal and nonfocal regions. The focal region mainly contains information for diagnosis, while the nonfocal region serves as the monochrome background. The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images, and manual segmentation is time-consuming, poorly reproducible, and operator-dependent. Implementing state-of-the-art deep learning (DL) models will facilitate key benefits, but the lack of domain-specific labels… More >

  • Open AccessOpen Access

    ARTICLE

    Efficient Resource Management in IoT Network through ACOGA Algorithm

    Pravinkumar Bhujangrao Landge1, Yashpal Singh1, Hitesh Mohapatra2, Seyyed Ahmad Edalatpanah3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1661-1688, 2025, DOI:10.32604/cmes.2025.065599 - 30 May 2025
    Abstract Internet of things networks often suffer from early node failures and short lifespan due to energy limits. Traditional routing methods are not enough. This work proposes a new hybrid algorithm called ACOGA. It combines Ant Colony Optimization (ACO) and the Greedy Algorithm (GA). ACO finds smart paths while Greedy makes quick decisions. This improves energy use and performance. ACOGA outperforms Hybrid Energy-Efficient (HEE) and Adaptive Lossless Data Compression (ALDC) algorithms. After 500 rounds, only 5% of ACOGA’s nodes are dead, compared to 15% for HEE and 20% for ALDC. The network using ACOGA runs for More >

  • Open AccessOpen Access

    ARTICLE

    Multi-Objective Optimization of Marine Winch Based on Surrogate Model and MOGA

    Chunhuan Jin1, Linsen Zhu2, Quanliang Liu1,3,*, Ji Lin1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1689-1711, 2025, DOI:10.32604/cmes.2025.063850 - 30 May 2025
    Abstract This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic (FRP) fishing vessels to address critical limitations of conventional designs, including excessive weight, material inefficiency, and performance redundancy. By integrating surrogate modeling techniques with a multi-objective genetic algorithm (MOGA), we have developed a systematic approach that encompasses parametric modeling, finite element analysis under extreme operational conditions, and multi-fidelity performance evaluation. Through a 10-t electric winch case study, the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity, stiffness behavior, and mass distribution. The comparative analysis identified optimal surrogate models for predicting More >

  • Open AccessOpen Access

    ARTICLE

    Shock-Capturing Particle Hydrodynamics with Reproducing Kernels

    Stephan Rosswog1,2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1713-1741, 2025, DOI:10.32604/cmes.2025.062063 - 30 May 2025
    (This article belongs to the Special Issue: Smoothed Particle Hydrodynamics (SPH): Research and Applications to Science and Engineering)
    Abstract We present and explore a new shock-capturing particle hydrodynamics approach. Our starting point is a commonly used discretization of smoothed particle hydrodynamics. We enhance this discretization with Roe’s approximate Riemann solver, we identify its dissipative terms, and in these terms, we use slope-limited linear reconstruction. All gradients needed for our method are calculated with linearly reproducing kernels that are constructed to enforce the two lowest-order consistency relations. We scrutinize our reproducing kernel implementation carefully on a “glass-like” particle distribution, and we find that constant and linear functions are recovered to machine precision. We probe our More >

  • Open AccessOpen Access

    ARTICLE

    Concurrent Design on Three-Legged Jacket Structure and Transition Piece of Offshore Wind Turbine by Exploiting Topology Optimization

    Yiming Zhou1, Jinhua Zhang2,3, Kai Long2,*, Ayesha Saeed2, Yutang Chen2, Rongrong Geng2, Tao Tao4, Xiaohui Guo1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1743-1761, 2025, DOI:10.32604/cmes.2025.063034 - 30 May 2025
    (This article belongs to the Special Issue: Topology Optimization: Theory, Methods, and Engineering Applications)
    Abstract The jacket structure and transition piece comprise the supporting structure of a bottom-fixed offshore wind turbine (OWT) connected to the steel tower, which determines the overall structural dynamic performance of the entire OWT. Ideally, optimal performance can be realized by effectively coordinating two components, notwithstanding their separate design processes. In pursuit of this objective, this paper proposes a concurrent design methodology for the jacket structure and transition piece by exploiting topology optimization (TO). The TO for a three-legged jacket foundation is formulated by minimizing static compliance. In contrast to conventional TO, two separated volume fractions… More >

  • Open AccessOpen Access

    ARTICLE

    Leveraging Neural Networks and Explainable AI for Cost-Effective Retaining Wall Design

    Gebrail Bekdaş1, Yaren Aydın1, Celal Cakiroglu2, Umit Işıkdağ3,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1763-1787, 2025, DOI:10.32604/cmes.2025.063909 - 30 May 2025
    (This article belongs to the Special Issue: Frontiers in Computational Modeling and Simulation of Concrete)
    Abstract Retaining walls are utilized to support the earth and prevent the soil from spreading with natural slope angles where there are differences in the elevation of ground surfaces. As the need for retaining structures increases, the use of retaining walls is increasing. The retaining walls, which increase the stability of levels, are economical and meet existing adverse conditions. A considerable amount of retaining walls is made from steel-reinforced concrete. The construction of reinforced concrete retaining walls can be costly due to its components. For this reason, the optimum cost should be targeted in the design… More >

  • Open AccessOpen Access

    ARTICLE

    A Shuffled Frog-Leaping Algorithm with Competition for Parallel Batch Processing Machines Scheduling in Fabric Dyeing Process

    Mingbo Li, Deming Lei*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1789-1808, 2025, DOI:10.32604/cmes.2025.064886 - 30 May 2025
    (This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
    Abstract As a complicated optimization problem, parallel batch processing machines scheduling problem (PBPMSP) exists in many real-life manufacturing industries such as textiles and semiconductors. Machine eligibility means that at least one machine is not eligible for at least one job. PBPMSP and scheduling problems with machine eligibility are frequently considered; however, PBPMSP with machine eligibility is seldom explored. This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition (CSFLA) to minimize makespan. In CSFLA, the initial population is produced in a heuristic and random way, and the More >

  • Open AccessOpen Access

    ARTICLE

    Design of Chaos Induced Aquila Optimizer for Parameter Estimation of Electro-Hydraulic Control System

    Khizer Mehmood1, Naveed Ishtiaq Chaudhary2,*, Zeshan Aslam Khan3, Khalid Mehmood Cheema4, Muhammad Asif Zahoor Raja2, Sultan S. Alshamrani5, Kaled M. Alshmrany6
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1809-1841, 2025, DOI:10.32604/cmes.2025.064900 - 30 May 2025
    (This article belongs to the Special Issue: Advances in Swarm Intelligence Algorithms)
    Abstract Aquila Optimizer (AO) is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey. AO is applied in various applications and its numerous variants were proposed in the literature. However, chaos theory has not been extensively investigated in AO. Moreover, it is still not applied in the parameter estimation of electro-hydraulic systems. In this work, ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique. An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation (CEC) functions… More >

  • Open AccessOpen Access

    ARTICLE

    Mathematical Model of the Monkeypox Virus Disease via Fractional Order Derivative

    Rajagopalan Ramaswamy1,*, Gunaseelan Mani2, Deepak Kumar3, Ozgur Ege4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1843-1894, 2025, DOI:10.32604/cmes.2025.063672 - 30 May 2025
    (This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
    Abstract The Department of Economic and Social Affairs of the United Nations has released seventeen goals for sustainable development and SDG No. 3 is “Good Health and Well-being”, which mainly emphasizes the strategies to be adopted for maintaining a healthy life. The Monkeypox Virus disease was first reported in 1970. Since then, various health initiatives have been taken, including by the WHO. In the present work, we attempt a fractional model of Monkeypox virus disease, which we feel is crucial for a better understanding of this disease. We use the recently introduced fractional derivative to closely… More >

  • Open AccessOpen Access

    ARTICLE

    A Numerical Study of the Caputo Fractional Nonlinear Rössler Attractor Model via Ultraspherical Wavelets Approach

    Ashish Rayal1, Priya Dogra1, Sabri T. M. Thabet2,3,4,*, Imed Kedim5, Miguel Vivas-Cortez6,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1895-1925, 2025, DOI:10.32604/cmes.2025.060989 - 30 May 2025
    (This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
    Abstract The Rössler attractor model is an important model that provides valuable insights into the behavior of chaotic systems in real life and is applicable in understanding weather patterns, biological systems, and secure communications. So, this work aims to present the numerical performances of the nonlinear fractional Rössler attractor system under Caputo derivatives by designing the numerical framework based on Ultraspherical wavelets. The Caputo fractional Rössler attractor model is simulated into two categories, (i) Asymmetric and (ii) Symmetric. The Ultraspherical wavelets basis with suitable collocation grids is implemented for comprehensive error analysis in the solutions of More >

  • Open AccessOpen Access

    ARTICLE

    Numerical Treatments for a Crossover Cholera Mathematical Model Combining Different Fractional Derivatives Based on Nonsingular and Singular Kernels

    Seham M. AL-Mekhlafi1,*, Kamal R. Raslan2, Khalid K. Ali2, Sadam. H. Alssad2,3, Nehaya R. Alsenaideh4
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1927-1953, 2025, DOI:10.32604/cmes.2025.063971 - 30 May 2025
    (This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
    Abstract This study introduces a novel mathematical model to describe the progression of cholera by integrating fractional derivatives with both singular and non-singular kernels alongside stochastic differential equations over four distinct time intervals. The model incorporates three key fractional derivatives: the Caputo-Fabrizio fractional derivative with a non-singular kernel, the Caputo proportional constant fractional derivative with a singular kernel, and the Atangana-Baleanu fractional derivative with a non-singular kernel. We analyze the stability of the core model and apply various numerical methods to approximate the proposed crossover model. To achieve this, the approximation of Caputo proportional constant fractional… More >

  • Open AccessOpen Access

    ARTICLE

    Full Ceramic Bearing Fault Diagnosis with Few-Shot Learning Using GPT-2

    David He1,*, Miao He2, Jay Yoon3
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1955-1969, 2025, DOI:10.32604/cmes.2025.063975 - 30 May 2025
    (This article belongs to the Special Issue: Applications of Large Language Models (LLMs) in Prognostics and Health Management)
    Abstract Full ceramic bearings are mission-critical components in oil-free environments, such as food processing, semiconductor manufacturing, and medical applications. Developing effective fault diagnosis methods for these bearings is essential to ensuring operational reliability and preventing costly failures. Traditional supervised deep learning approaches have demonstrated promise in fault detection, but their dependence on large labeled datasets poses significant challenges in industrial settings where fault-labeled data is scarce. This paper introduces a few-shot learning approach for full ceramic bearing fault diagnosis by leveraging the pre-trained GPT-2 model. Large language models (LLMs) like GPT-2, pre-trained on diverse textual data,… More >

  • Open AccessOpen Access

    ARTICLE

    EffNet-CNN: A Semantic Model for Image Mining & Content-Based Image Retrieval

    Rajendran Thanikachalam1, Anandhavalli Muniasamy2, Ashwag Alasmari3, Rajendran Thavasimuthu4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1971-2000, 2025, DOI:10.32604/cmes.2025.063063 - 30 May 2025
    (This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
    Abstract Content-Based Image Retrieval (CBIR) and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare, security, and various domains. The image retrieval system mainly relies on the efficiency and accuracy of the classification models. This research addresses the challenge of enhancing the image retrieval system by developing a novel approach, EfficientNet-Convolutional Neural Network (EffNet-CNN). The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification, image mining, and CBIR. The novelty of the proposed EffNet-CNN model includes the integration… More >

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    ARTICLE

    Integrating Speech-to-Text for Image Generation Using Generative Adversarial Networks

    Smita Mahajan1, Shilpa Gite1,2, Biswajeet Pradhan3,*, Abdullah Alamri4, Shaunak Inamdar5, Deva Shriyansh5, Akshat Ashish Shah5, Shruti Agarwal5
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2001-2026, 2025, DOI:10.32604/cmes.2025.058456 - 30 May 2025
    (This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
    Abstract The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs. However, humans naturally use speech for visualization prompts. Therefore, this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks (GANs) model, leveraging Speech-to-Text translation along with the CLIP + VQGAN model. The proposed method involves translating speech prompts into text, which is then used by the Contrastive Language-Image Pretraining (CLIP) + Vector Quantized Generative Adversarial Network (VQGAN) model to generate images. This paper outlines the steps required to implement such a… More >

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    ARTICLE

    Deep Learning and Heuristic Optimization for Secure and Efficient Energy Management in Smart Communities

    Murad Khan1,*, Mohammed Faisal1, Fahad R. Albogamy2, Muhammad Diyan3
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2027-2052, 2025, DOI:10.32604/cmes.2025.063764 - 30 May 2025
    (This article belongs to the Special Issue: Emerging Technologies in Information Security )
    Abstract The rapid advancements in distributed generation technologies, the widespread adoption of distributed energy resources, and the integration of 5G technology have spurred sharing economy businesses within the electricity sector. Revolutionary technologies such as blockchain, 5G connectivity, and Internet of Things (IoT) devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand. Nevertheless, sharing electricity within a smart community presents numerous challenges, including intricate design considerations, equitable allocation, and accurate forecasting due to the lack of well-organized temporal parameters. To address these challenges, this proposed system is focused on sharing extra electricity… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Post-Quantum Information Security: A Novel Two-Dimensional Chaotic System for Quantum Image Encryption

    Fatima Asiri*, Wajdan Al Malwi
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2053-2077, 2025, DOI:10.32604/cmes.2025.064348 - 30 May 2025
    (This article belongs to the Special Issue: Emerging Technologies in Information Security )
    Abstract Ensuring information security in the quantum era is a growing challenge due to advancements in cryptographic attacks and the emergence of quantum computing. To address these concerns, this paper presents the mathematical and computer modeling of a novel two-dimensional (2D) chaotic system for secure key generation in quantum image encryption (QIE). The proposed map employs trigonometric perturbations in conjunction with rational-saturation functions and hence, named as Trigonometric-Rational-Saturation (TRS) map. Through rigorous mathematical analysis and computational simulations, the map is extensively evaluated for bifurcation behaviour, chaotic trajectories, and Lyapunov exponents. The security evaluation validates the map’s… More >

  • Open AccessOpen Access

    ARTICLE

    Deep Learning-Based Natural Language Processing Model and Optical Character Recognition for Detection of Online Grooming on Social Networking Services

    Sangmin Kim1, Byeongcheon Lee1, Muazzam Maqsood2, Jihoon Moon3,*, Seungmin Rho4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2079-2108, 2025, DOI:10.32604/cmes.2025.061653 - 30 May 2025
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
    Abstract The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel… More >

  • Open AccessOpen Access

    ARTICLE

    Enhancing Multi-Class Cyberbullying Classification with Hybrid Feature Extraction and Transformer-Based Models

    Suliman Mohamed Fati1,*, Mohammed A. Mahdi2, Mohamed A.G. Hazber2, Shahanawaj Ahamad3, Sawsan A. Saad4, Mohammed Gamal Ragab5, Mohammed Al-Shalabi2
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2109-2131, 2025, DOI:10.32604/cmes.2025.063092 - 30 May 2025
    (This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)
    Abstract Cyberbullying on social media poses significant psychological risks, yet most detection systems oversimplify the task by focusing on binary classification, ignoring nuanced categories like passive-aggressive remarks or indirect slurs. To address this gap, we propose a hybrid framework combining Term Frequency-Inverse Document Frequency (TF-IDF), word-to-vector (Word2Vec), and Bidirectional Encoder Representations from Transformers (BERT) based models for multi-class cyberbullying detection. Our approach integrates TF-IDF for lexical specificity and Word2Vec for semantic relationships, fused with BERT’s contextual embeddings to capture syntactic and semantic complexities. We evaluate the framework on a publicly available dataset of 47,000 annotated social… More >

  • Open AccessOpen Access

    ARTICLE

    A Design of Predictive Intelligent Networks for the Analysis of Fractional Model of TB-Virus

    Muhammad Asif Zahoor Raja1, Aqsa Zafar Abbasi2, Kottakkaran Sooppy Nisar3,*, Ayesha Rafiq2, Muhammad Shoaib4
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2133-2153, 2025, DOI:10.32604/cmes.2025.058020 - 30 May 2025
    (This article belongs to the Special Issue: Recent Developments on Computational Biology-II)
    Abstract Being a nonlinear operator, fractional derivatives can affect the enforcement of existence at any given time. As a result, the memory effect has an impact on all nonlinear processes modeled by fractional order differential equations (FODEs). The goal of this study is to increase the fractional model of the TB virus’s (FMTBV) accuracy. Stochastic solvers have never been used to solve FMTBV previously. The Bayesian regularized artificial (BRA) method and neural networks (NNs), often referred to as BRA-NNs, were used to solve the FMTBV model. Each scenario features five occurrences that each reflect a different… More >

  • Open AccessOpen Access

    ARTICLE

    Promoting Tailored Hotel Recommendations Based on Traveller Preferences: A Circular Intuitionistic Fuzzy Decision Support Model

    Sana Shahab1, Ibtehal Alazman2, Ashit Kumar Dutta3, Mohd Anjum4, Vladimir Simic5,6,7,*, Željko Stević8, Nouf Abdulrahman Alqahtani2
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2155-2183, 2025, DOI:10.32604/cmes.2025.064553 - 30 May 2025
    (This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract With the increasing complexity of hotel selection, traditional decision-making models often struggle to account for uncertainty and interrelated criteria. Multi-criteria decision-making (MCDM) techniques, particularly those based on fuzzy logic, provide a robust framework for handling such challenges. This paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets (C-IFS) by combining three distinct methodologies: Weighted Aggregated Sum Product Assessment (WASPAS), an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN), and the CRITIC method (Criteria Importance Through Intercriteria Correlation). To address the dynamic nature of traveler preferences in hotel selection,… More >

  • Open AccessOpen Access

    ARTICLE

    Development of AHP-Based Divergence Distance Measure between –Spherical Fuzzy Sets with Applications in Multi-Criteria Decision Making

    Shah Zeb Khan1, Muhammad Rahim2, Adel M. Widyan3,*, A. Almutairi3, Njood Shaher Ethaar Almutire3, Hamiden Abd El-Wahed Khalifa3
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2185-2211, 2025, DOI:10.32604/cmes.2025.063929 - 30 May 2025
    (This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
    Abstract This study introduces a novel distance measure (DM) for spherical fuzzy sets (SFSs) to improve decision-making in complex and uncertain environments. Many existing distance measures either fail to satisfy essential axiomatic properties or produce unintuitive outcomes. To address these limitations, we propose a new three-dimensional divergence-based DM that ensures mathematical consistency, enhances the discrimination of information, and adheres to the axiomatic framework of distance theory. Building on this foundation, we construct a multi-criteria decision-making (MCDM) model that utilizes the proposed DM to evaluate and rank alternatives effectively. The applicability and robustness of the model are More >

  • Open AccessOpen Access

    ARTICLE

    Suzuki-Type ()-Weak Contraction for the Hesitant Fuzzy Soft Set Valued Mappings with Applications in Decision Making

    Muhammad Sarwar1,2,*, Rafiq Alam1, Kamaleldin Abodayeh2,*, Saowaluck Chasreechai3,4, Thanin Sitthiwirattham4,5
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2213-2236, 2025, DOI:10.32604/cmes.2025.062139 - 30 May 2025
    (This article belongs to the Special Issue: Advances in Ambient Intelligence and Social Computing under uncertainty and indeterminacy: From Theory to Applications)
    Abstract In this manuscript, the notion of a hesitant fuzzy soft fixed point is introduced. Using this notion and the concept of Suzuki-type ()-weak contraction for hesitant fuzzy soft set valued-mapping, some fixed point results are established in the framework of metric spaces. Based on the presented work, some examples reflecting decision-making problems related to real life are also solved. The suggested method’s flexibility and efficacy compared to conventional techniques are demonstrated in decision-making situations involving uncertainty, such as choosing the best options in multi-criteria settings. We noted that the presented work combines and generalizes two More >

  • Open AccessOpen Access

    ARTICLE

    Demand Forecasting of a Microgrid-Powered Electric Vehicle Charging Station Enabled by Emerging Technologies and Deep Recurrent Neural Networks

    Sahbi Boubaker1,*, Adel Mellit2,3,*, Nejib Ghazouani4, Walid Meskine5, Mohamed Benghanem6, Habib Kraiem7,8
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2237-2259, 2025, DOI:10.32604/cmes.2025.064530 - 30 May 2025
    (This article belongs to the Special Issue: Advances in Deep Learning for Time Series Forecasting: Research and Applications)
    Abstract Electric vehicles (EVs) are gradually being deployed in the transportation sector. Although they have a high impact on reducing greenhouse gas emissions, their penetration is challenged by their random energy demand and difficult scheduling of their optimal charging. To cope with these problems, this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand forecasting. The present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’ charging scheduling task. By using predictive algorithms for solar generation and load demand… More >

  • Open AccessOpen Access

    ARTICLE

    SSA-LSTM-Multi-Head Attention Modelling Approach for Prediction of Coal Dust Maximum Explosion Pressure Based on the Synergistic Effect of Particle Size and Concentration

    Yongli Liu1,2, Weihao Li1,2,*, Haitao Wang1,2,3, Taoren Du4
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2261-2286, 2025, DOI:10.32604/cmes.2025.064179 - 30 May 2025
    (This article belongs to the Special Issue: Advances in Deep Learning for Time Series Forecasting: Research and Applications)
    Abstract Coal dust explosions are severe safety accidents in coal mine production, posing significant threats to life and property. Predicting the maximum explosion pressure () of coal dust using deep learning models can effectively assess potential risks and provide a scientific basis for preventing coal dust explosions. In this study, a 20-L explosion sphere apparatus was used to test the maximum explosion pressure of coal dust under seven different particle sizes and ten mass concentrations (), resulting in a dataset of 70 experimental groups. Through Spearman correlation analysis and random forest feature selection methods, particle size… More >

  • Open AccessOpen Access

    ARTICLE

    Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

    Namal Rathnayake1, Jeevani Jayasinghe2,3, Rashmi Semasinghe2, Upaka Rathnayake4,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R2 values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Fault Detection and Diagnosis in Photovoltaic Arrays Using a Hybrid NCA-CNN Model

    Umit Cigdem Turhal1, Yasemin Onal1,*, Kutalmis Turhal2
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2307-2332, 2025, DOI:10.32604/cmes.2025.064269 - 30 May 2025
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other More >

    Graphic Abstract

    Enhanced Fault Detection and Diagnosis in Photovoltaic Arrays Using a Hybrid NCA-CNN Model

  • Open AccessOpen Access

    ARTICLE

    BioSkinNet: A Bio-Inspired Feature-Selection Framework for Skin Lesion Classification

    Tallha Akram1,*, Fahdah Almarshad1, Anas Alsuhaibani1, Syed Rameez Naqvi2,3
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025
    (This article belongs to the Special Issue: Machine Learning and Deep Learning-Based Pattern Recognition)
    Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >

  • Open AccessOpen Access

    ARTICLE

    DriveMe: Towards Lightweight and Practical Driver Authentication System Using Single-Sensor Pressure Data

    Mohsen Ali Alawami1, Dahyun Jung2, Yewon Park2, Yoonseo Ku2, Gyeonghwan Choi2, Ki-Woong Park2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2361-2389, 2025, DOI:10.32604/cmes.2025.063819 - 30 May 2025
    (This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
    Abstract To date, many previous studies have been proposed for driver authentication; however, these solutions have many shortcomings and are still far from practical for real-world applications. In this paper, we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system, dubbed DriveMe, for identifying drivers on cars. Our novelty aspects are ① Lightweight scheme that depends only on a single sensor data (i.e., pressure readings) attached to the driver’s seat and belt. ② Practical evaluation in which one-class authentication models are trained from only the owner users and tested using… More >

  • Open AccessOpen Access

    ARTICLE

    Ensemble Encoder-Based Attack Traffic Classification for Secure 5G Slicing Networks

    Min-Gyu Kim1, Hwankuk Kim2,*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2391-2415, 2025, DOI:10.32604/cmes.2025.063558 - 30 May 2025
    (This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)
    Abstract This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service (DDoS) attacks in 5th generation technology standard (5G) slicing networks. The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data. These representations are then used as input for a support vector machine (SVM)-based metadata classifier, enabling precise detection of attack traffic. This architecture is designed to achieve both high detection accuracy and training efficiency, while adapting flexibly to the diverse service requirements and complexity of 5G network… More >

  • Open AccessOpen Access

    ARTICLE

    Defending against Backdoor Attacks in Federated Learning by Using Differential Privacy and OOD Data Attributes

    Qingyu Tan, Yan Li, Byeong-Seok Shin*
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2417-2428, 2025, DOI:10.32604/cmes.2025.063811 - 30 May 2025
    (This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
    Abstract Federated Learning (FL), a practical solution that leverages distributed data across devices without the need for centralized data storage, which enables multiple participants to jointly train models while preserving data privacy and avoiding direct data sharing. Despite its privacy-preserving advantages, FL remains vulnerable to backdoor attacks, where malicious participants introduce backdoors into local models that are then propagated to the global model through the aggregation process. While existing differential privacy defenses have demonstrated effectiveness against backdoor attacks in FL, they often incur a significant degradation in the performance of the aggregated models on benign tasks.… More >

  • Open AccessOpen Access

    ARTICLE

    Hybrid Techniques of Multi-CNN and Ensemble Learning to Analyze Handwritten Spiral and Wave Drawing for Diagnosing Parkinson’s Disease

    Mohammed Al-Jabbar1, Mohammed Alshahrani1,*, Ebrahim Mohammed Senan2,3, Ibrahim Abunadi4, Sultan Ahmed Almalki1, Eman A Alshari3,5
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2429-2457, 2025, DOI:10.32604/cmes.2025.063938 - 30 May 2025
    (This article belongs to the Special Issue: Advanced Computational Intelligence Techniques, Uncertain Knowledge Processing and Multi-Attribute Group Decision-Making Methods Applied in Modeling of Medical Diagnosis and Prognosis)
    Abstract Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by tremors, rigidity, and decreased movement. PD poses risks to individuals’ lives and independence. Early detection of PD is essential because it allows timely intervention, which can slow disease progression and improve outcomes. Manual diagnosis of PD is problematic because it is difficult to capture the subtle patterns and changes that help diagnose PD. In addition, the subjectivity and lack of doctors compared to the number of patients constitute an obstacle to early diagnosis. Artificial intelligence (AI) techniques, especially deep and automated learning models, provide promising… More >

  • Open AccessOpen Access

    ARTICLE

    Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning

    Karim Gasmi1, Olfa Hrizi1,*, Najib Ben Aoun2,3, Ibrahim Alrashdi1, Ali Alqazzaz4, Omer Hamid5, Mohamed O. Altaieb1, Alameen E. M. Abdalrahman1, Lassaad Ben Ammar6, Manel Mrabet6, Omrane Necibi1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817 - 30 May 2025
    (This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
    Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

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    ARTICLE

    Analytical Solutions for 1-Dimensional Peridynamic Systems by Considering the Effect of Damping

    Zhenghao Yang1, Erkan Oterkus2,*, Selda Oterkus2, Konstantin Naumenko1
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2491-2508, 2025, DOI:10.32604/cmes.2025.062998 - 30 May 2025
    (This article belongs to the Special Issue: Peridynamic Theory and Multi-physical/Multiscale Methods for Complex Material Behavior)
    Abstract For the solution of peridynamic equations of motion, a meshless approach is typically used instead of utilizing semi-analytical or mesh-based approaches. In contrast, the literature has limited analytical solutions. This study develops a novel analytical solution for one-dimensional peridynamic models, considering the effect of damping. After demonstrating the details of the analytical solution, various demonstration problems are presented. First, the free vibration of a damped system is considered for under-damped and critically damped conditions. Peridynamic solutions and results from the classical theory are compared against each other, and excellent agreement is observed between the two More >

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