Open Access
ARTICLE
Sagvan Y. Musa1,2, Baravan A. Asaad3,4,*, Hanan Alohali5, Zanyar A. Ameen6, Mesfer H. Alqahtani7
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062524
（This article belongs to the Special Issue: Algorithms, Models, and Applications of Fuzzy Optimization and Decision Making)
Abstract This paper introduces fuzzy N-bipolar soft (FN-BS) sets, a novel mathematical framework designed to enhance multi-criteria decision-making (MCDM) processes under uncertainty. The study addresses a significant limitation in existing models by unifying fuzzy logic, the consideration of bipolarity, and the ability to evaluate attributes on a multinary scale. The specific contributions of the FN-BS framework include: (1) a formal definition and set-theoretic foundation, (2) the development of two innovative algorithms for solving decision-making (DM) problems, and (3) a comparative analysis demonstrating its superiority over established models. The proposed framework is applied to a real-world case More >
Open Access
ARTICLE
Magdy Nagy*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061865
Abstract In this present work, we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution. These estimates have been obtained using gamma priors based on various loss functions such as squared error, entropy, weighted balance, and minimum expected loss functions. An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators. The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of More >
Open Access
ARTICLE
Mourad Benmalek1,*,#,*, Abdessamed Seddiki2,#, Kamel-Dine Haouam1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062841
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract The Internet of Medical Things (IoMT) connects healthcare devices and sensors to the Internet, driving transformative advancements in healthcare delivery. However, expanding IoMT infrastructures face growing security threats, necessitating robust Intrusion Detection Systems (IDS). Maintaining the confidentiality of patient data is critical in AI-driven healthcare systems, especially when securing interconnected medical devices. This paper introduces SNN-IoMT (Stacked Neural Network Ensemble for IoMT Security), an AI-driven IDS framework designed to secure dynamic IoMT environments. Leveraging a stacked deep learning architecture combining Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), the model optimizes data management More >
Open Access
ARTICLE
Saleh Albahli*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.058821
（This article belongs to the Special Issue: Exploring the Impact of Artificial Intelligence on Healthcare: Insights into Data Management, Integration, and Ethical Considerations)
Abstract Predicting hospital readmission and length of stay (LOS) for diabetic patients is critical for improving healthcare quality, optimizing resource utilization, and reducing costs. This study leverages machine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade. A comprehensive preprocessing pipeline, including feature selection, data transformation, and class balancing, was implemented to ensure data quality and enhance model performance. Exploratory analysis revealed key patterns, such as the influence of age and the number of diagnoses on readmission rates, guiding the More >
Open Access
ARTICLE
Noveela Iftikhar1, Mujeeb Ur Rehman1, Mumtaz Ali Shah2, Mohammed J. F. Alenazi3, Jehad Ali4,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062788
（This article belongs to the Special Issue: Leveraging AI and ML for QoS Improvement in Intelligent Programmable Networks)
Abstract Intrusion attempts against Internet of Things (IoT) devices have significantly increased in the last few years. These devices are now easy targets for hackers because of their built-in security flaws. Combining a Self-Organizing Map (SOM) hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting (XGBoost) for multi-class classification can improve network traffic intrusion detection. The proposed model is evaluated on the NSL-KDD dataset. The hybrid approach outperforms the baseline line models, Multilayer perceptron model, and SOM-KNN (k-nearest neighbors) model in precision, recall, and F1-score, highlighting the proposed More >
Open Access
ARTICLE
Jahanzaib Latif 1, Ahsan Wajahat1, Alishba Tahir2, Anas Bilal3,*, Mohammed Zakariah4, Abeer Alnuaim4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062301
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >
Open Access
ARTICLE
Yu-Cheng Liao1, Fu-I Chou1,*, Po-Yuan Yang2, Jyh-Horng Chou3
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062228
Abstract This paper presents an allowable-tolerance-based group search optimization (AT-GSO), which combines the robust GSO (R-GSO) and the external quality design planning of the Taguchi method. AT-GSO algorithm is used to optimize the heat transfer area of the heat exchanger system. The R-GSO algorithm integrates the GSO algorithm with the Taguchi method, utilizing the Taguchi method to determine the optimal producer in each iteration of the GSO algorithm to strengthen the robustness of the search process and the ability to find the global optima. In conventional parameter design optimization, it is typically assumed that the designed… More >
Open Access
ARTICLE
Muhammad Usama1, Arshad Aziz2, Suliman A. Alsuhibany3,*, Imtiaz Hassan2, Farrukh Yuldashev4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061787
（This article belongs to the Special Issue: Emerging Technologies in Information Security )
Abstract Data compression plays a vital role in data management and information theory by reducing redundancy. However, it lacks built-in security features such as secret keys or password-based access control, leaving sensitive data vulnerable to unauthorized access and misuse. With the exponential growth of digital data, robust security measures are essential. Data encryption, a widely used approach, ensures data confidentiality by making it unreadable and unalterable through secret key control. Despite their individual benefits, both require significant computational resources. Additionally, performing them separately for the same data increases complexity and processing time. Recognizing the need for More >
Open Access
ARTICLE
Yunhan Ling1, Huajun Peng2, Yiqing Shi1,*, Chao Xu1, Jingzhen Yan1, Jingjing Wang1, Hui Ma3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061763
（This article belongs to the Special Issue: Machine Learning-Assisted Structural Integrity Assessment and Design Optimization under Uncertainty)
Abstract The traditional first-order reliability method (FORM) often encounters challenges with non-convergence of results or excessive calculation when analyzing complex engineering problems. To improve the global convergence speed of structural reliability analysis, an improved coati optimization algorithm (COA) is proposed in this paper. In this study, the social learning strategy is used to improve the coati optimization algorithm (SL-COA), which improves the convergence speed and robustness of the new heuristic optimization algorithm. Then, the SL-COA is compared with the latest heuristic optimization algorithms such as the original COA, whale optimization algorithm (WOA), and osprey optimization algorithm… More >
Open Access
REVIEW
Yoshihiro Kanno1,*, Izuru Takewaki2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.061551
Abstract Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization. This paper provides a review on optimization-based methods for uncertainty analysis, with focusing attention on specific properties of adopted numerical optimization approaches. We collect and discuss the methods based on nonlinear programming, semidefinite programming, mixed-integer programming, mathematical programming with complementarity constraints, difference-of-convex programming, optimization methods using surrogate models and machine learning techniques, and metaheuristics. As a closely related topic, we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling. We conclude the paper by drawing several remarks through More >
Open Access
ARTICLE
Shaher Momani1,2, Rabha W. Ibrahim3,*, Yeliz Karaca4
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.059700
（This article belongs to the Special Issue: Analytical and Numerical Solution of the Fractional Differential Equation)
Abstract Fractional discrete systems can enable the modeling and control of the complicated processes more adaptable through the concept of versatility by providing system dynamics’ descriptions with more degrees of freedom. Numerical approaches have become necessary and sufficient to be addressed and employed for benefiting from the adaptability of such systems for varied applications. A variety of fractional Layla and Majnun model (LMM) system kinds has been proposed in the current work where some of these systems’ key behaviors are addressed. In addition, the necessary and sufficient conditions for the stability and asymptotic stability of the… More >
Open Access
ARTICLE
Xiangyang Cao1,2, Yaojie Zheng1,2, Hanbin Xiao1,2,*, Min Xiao2,3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063954
Abstract This study investigates the Maximum Power Point Tracking (MPPT) control method of offshore wind-photovoltaic hybrid power generation system with offshore crane-assisted. A new algorithm of Global Fast Integral Sliding Mode Control (GFISMC) is proposed based on the tip speed ratio method and sliding mode control. The algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low jitter. An offshore wind power generation system model is presented to verify the algorithm effect. An offshore More >
Open Access
ARTICLE
Nour Elhouda Chalabi1, Abdelouahab Attia2, Abdulaziz S. Almazyad3, Ali Wagdy Mohamed4,5, Frank Werner6, Pradeep Jangir7, Mohammad Shokouhifar8,9,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062332
（This article belongs to the Special Issue: Swarm and Metaheuristic Optimization for Applied Engineering Application)
Abstract Multi-objective optimization is critical for problem-solving in engineering, economics, and AI. This study introduces the Multi-Objective Chef-Based Optimization Algorithm (MOCBOA), an upgraded version of the Chef-Based Optimization Algorithm (CBOA) that addresses distinct objectives. Our approach is unique in systematically examining four dominance relations—Pareto, Epsilon, Cone-epsilon, and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front. Our comparison investigation, which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering, mechanical design, and power systems, reveals that the dominance approach More >
Open Access
ARTICLE
Mohammad Barr*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.062264
（This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)
Abstract Recent years have seen a surge in interest in object detection on remote sensing images for applications such as surveillance and management. However, challenges like small object detection, scale variation, and the presence of closely packed objects in these images hinder accurate detection. Additionally, the motion blur effect further complicates the identification of such objects. To address these issues, we propose enhanced YOLOv9 with a transformer head (YOLOv9-TH). The model introduces an additional prediction head for detecting objects of varying sizes and swaps the original prediction heads for transformer heads to leverage self-attention mechanisms. We… More >
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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