Open Access
ARTICLE
Anandhavalli Muniasamy1,*, Ashwag Alasmari2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.060484
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of patients. Today, the mass disease that needs attention in this context is cataracts. Although deep learning has significantly advanced the analysis of ocular disease images, there is a need for a probabilistic model to generate the distributions of potential outcomes and thus make decisions related to uncertainty quantification. Therefore, this study implements a Bayesian Convolutional Neural Networks (BCNN) model for predicting cataracts by assigning probability values to the predictions. It prepares convolutional neural network (CNN) and BCNN models. More >
Graphic Abstract
Open Access
ARTICLE
Aishwarya Rajasekar*, Vetriselvi Vetrian
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062642
Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… More >
Open Access
ARTICLE
Saad Alahmari1,*, Abdulwhab Alkharashi2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062549
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract The Internet of Things (IoT) is emerging as an innovative phenomenon concerned with the development of numerous vital applications. With the development of IoT devices, huge amounts of information, including users’ private data, are generated. IoT systems face major security and data privacy challenges owing to their integral features such as scalability, resource constraints, and heterogeneity. These challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data, creating an attractive opportunity for cyberattacks. To address these challenges, artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL),… More >
Open Access
REVIEW
Sheng Dong*, Feihu Fan, Yinuo Chen, Shangpeng Guo, Jiayu Liu
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062113
（This article belongs to the Special Issue: Environment Modeling for Applications of Mobile Robots)
Abstract Legged robots have always been a focal point of research for scholars domestically and internationally. Compared to other types of robots, quadruped robots exhibit superior balance and stability, enabling them to adapt effectively to diverse environments and traverse rugged terrains. This makes them well-suited for applications such as search and rescue, exploration, and transportation, with strong environmental adaptability, high flexibility, and broad application prospects. This paper discusses the current state of research on quadruped robots in terms of development status, gait trajectory planning methods, motion control strategies, reinforcement learning applications, and control algorithm integration. It More >
Open Access
ARTICLE
Murat Cikan*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061699
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract This study examines various issues arising in three-phase unbalanced power distribution networks (PDNs) using a comprehensive optimization approach. With the integration of renewable energy sources, increasing energy demands, and the adoption of smart grid technologies, power systems are undergoing a rapid transformation, making the need for efficient, reliable, and sustainable distribution networks increasingly critical. In this paper, the reconfiguration problem in a 37-bus unbalanced PDN test system is solved using five different popular metaheuristic algorithms. Among these advanced search algorithms, the Bonobo Optimizer (BO) has demonstrated superior performance in handling the complexities of unbalanced power… More >
Open Access
ARTICLE
Salwa A. Mohamed1, Mohamed A. Eltaher2,3,*, Nazira Mohamed1, Rasha Abo-bakr4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061897
（This article belongs to the Special Issue: Theoretical and Computational Modeling of Advanced Materials and Structures-II)
Abstract The nonlinear post-buckling response of functionally graded (FG) copper matrix plates enforced by graphene origami auxetic metamaterials (GOAMs) is investigated in the current work. The auxetic material properties of the plate are controlled by graphene content and the degree of origami folding, which are graded across the thickness of the plate. The material properties of the GOAM plate are evaluated using genetic micro-mechanical models. Governing nonlinear eigenvalue problems for the post-buckling response of the GOAM composite plate are derived using the virtual work principle and a four-variable nonlinear shear deformation theory. A novel differential quadrature More >
Open Access
ARTICLE
Iman S. Al-Mahdi1, Saad M. Darwish1,*, Magda M. Madbouly2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061623
（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 Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart… More >
Open Access
ARTICLE
Amlak Abaza1, Ragab A. El-Sehiemy1,6, Mona Gafar2,3,*, Ahmed Bayoumi4,5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.059625
（This article belongs to the Special Issue: Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications-II)
Abstract Promoting the high penetration of renewable energies like photovoltaic (PV) systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution grids. This study measures the effectiveness of the Puma optimizer (PO) algorithm in parameter estimation of PSC (perovskite solar cells) dynamic models with hysteresis consideration considering the electric field effects on operation. The models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs accurately. The PO optimizes the proposed modified triple diode model (TDM) with a variable voltage capacitor… More >
Open Access
ARTICLE
Jifeng Liang1, Qiang Wang2, Leibao Wang1, Ziwei Zhang3, Yonghui Sun3,*, Hongzhu Tao4, Xiaofei Li5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062315
（This article belongs to the Special Issue: Advances in Deep Learning for Time Series Forecasting: Research and Applications)
Abstract Wind power generation is subjected to complex and variable meteorological conditions, resulting in intermittent and volatile power generation. Accurate wind power prediction plays a crucial role in enabling the power grid dispatching departments to rationally plan power transmission and energy storage operations. This enhances the efficiency of wind power integration into the grid. It allows grid operators to anticipate and mitigate the impact of wind power fluctuations, significantly improving the resilience of wind farms and the overall power grid. Furthermore, it assists wind farm operators in optimizing the management of power generation facilities and reducing… More >
Graphic Abstract
Open Access
ARTICLE
Nguyen Minh Trieu, Nguyen Truong Thinh*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061339
（This article belongs to the Special Issue: Applied Artificial Intelligence: Advanced Solutions for Engineering Real-World Challenges)
Abstract In robotics and human-robot interaction, a robot’s capacity to express and react correctly to human
emotions is essential. A signiffcant aspect of the capability involves controlling the robotic facial skin actuators in a
way that resonates with human emotions. This research focuses on human anthropometric theories to design and
control robotic facial actuators, addressing the limitations of existing approaches in expressing emotions naturally and
accurately. The facial landmarks are extracted to determine the anthropometric indicators for designing the robot head
and is employed to the displacement of these points to calculate emotional values using Fuzzy… More >
Open Access
ARTICLE
Hyunwoo Park1,2, Jaedong Lee1,3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061178
（This article belongs to the Special Issue: Machine learning and Blockchain for AIoT: Robustness, Privacy, Trust and Security)
Abstract Integrating Artificial Intelligence of Things (AIoT) in healthcare offers transformative potential for real-time diagnostics and collaborative learning but presents critical challenges, including privacy preservation, computational efficiency, and regulatory compliance. Traditional approaches, such as differential privacy, homomorphic encryption, and secure multi-party computation, often fail to balance performance and privacy, rendering them unsuitable for resource-constrained healthcare AIoT environments. This paper introduces LMSA (Lightweight Multi-Key Secure Aggregation), a novel framework designed to address these challenges and enable efficient, secure federated learning across distributed healthcare institutions. LMSA incorporates three key innovations: (1) a lightweight multi-key management system leveraging Diffie-Hellman… More >
Open Access
ARTICLE
Kaku Haribabu1,*, Prasath R1, Praveen Joe IR2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.059406
Abstract Thyroid nodules, a common disorder in the endocrine system, require accurate segmentation in ultrasound images for effective diagnosis and treatment. However, achieving precise segmentation remains a challenge due to various factors, including scattering noise, low contrast, and limited resolution in ultrasound images. Although existing segmentation models have made progress, they still suffer from several limitations, such as high error rates, low generalizability, overfitting, limited feature learning capability, etc. To address these challenges, this paper proposes a Multi-level Relation Transformer-based U-Net (MLRT-UNet) to improve thyroid nodule segmentation. The MLRT-UNet leverages a novel Relation Transformer, which processes… More >
Open Access
ARTICLE
Muhammad Zubair Mustafa1, Sumera Dero1, Liaquat Ali Lund2, Mehboob Ul Hassan3, Umair Khan4,5,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061804
（This article belongs to the Special Issue: Innovative Computational Methods and Applications of Nanofluids in Engineering)
Abstract This paper discusses the model of the boundary layer (BL) flow and the heat transfer characteristics of hybrid nanofluid (HNF) over shrinking/stretching disks. In addition, the thermal radiation and the impact of velocity and thermal slip boundary conditions are also examined. The considered hybrid nano-fluid contains silver (Ag) and iron oxide (Fe3O4) nanoparticles dispersed in the water to prepare the Ag-Fe3O4/water-based hybrid nanofluid. The requisite posited partial differential equations model is converted to ordinary differential equations using similarity transformations. For a numerical solution, the shooting method in Maple is employed. Moreover, the duality in solutions is… More >
Graphic Abstract
Open Access
ARTICLE
Kuo Li1,*, Aimin Wang1, Limin Wang1, Yuetan Zhao1, Xinyu Zhu2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061242
（This article belongs to the Special Issue: Incomplete Data Test, Analysis and Fusion Under Complex Environments)
Abstract A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values. This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies. In the experiment of game network reconstruction, when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%, the minimum data required is… More >
Open Access
ARTICLE
Zijian Liu1, Yong Shi2, Chuanqi Li1, Xiliang Zhang3,*, Jian Zhou1, Manoj Khandelwal4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062426
Abstract Given the growing concern over global warming and the critical role of carbon dioxide (CO2) in this phenomenon, the study of CO2-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration. A large number of experiments have proved that CO2 interaction time (T), saturation pressure (P) and other parameters have significant effects on coal strength. However, accurate evaluation of CO2-induced alterations in coal strength is still a difficult problem, so it is particularly important to establish accurate and efficient prediction models. This study explored the application of advanced machine learning (ML)… More >
Open Access
ARTICLE
Umar Shafique1, Ali Raza2,7,*, Dumitru Baleanu3, Khadija Nasir4, Muhammad Naveed5, Abu Bakar Siddique1, Emad Fadhal6,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061635
（This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)
Abstract Streptococcus suis (S. suis) is a major disease impacting pig farming globally. It can also be transferred to humans by eating raw pork. A comprehensive study was recently carried out to determine the indices through multiple geographic regions in China. Methods: The well-posed theorems were employed to conduct a thorough analysis of the model’s feasible features, including positivity, boundedness equilibria, reproduction number, and parameter sensitivity. Stochastic Euler, Runge Kutta, and Euler Maruyama are some of the numerical techniques used to replicate the behavior of the streptococcus suis infection in the pig population. However, the dynamic… More >
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