Yu-Shiuan Tsai^*, Zhen-Rong Wu, Jian-Zhi Liu

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3431-3457, 2025, DOI:10.32604/cmc.2025.066509 - 03 July 2025

Abstract Few-shot learning has emerged as a crucial technique for coral species classification, addressing the challenge of limited labeled data in underwater environments. This study introduces an optimized few-shot learning model that enhances classification accuracy while minimizing reliance on extensive data collection. The proposed model integrates a hybrid similarity measure combining Euclidean distance and cosine similarity, effectively capturing both feature magnitude and directional relationships. This approach achieves a notable accuracy of 71.8% under a 5-way 5-shot evaluation, outperforming state-of-the-art models such as Prototypical Networks, FEAT, and ESPT by up to 10%. Notably, the model demonstrates high… More >

Pooja Bidwai^1,2, Shilpa Gite^1,3, Biswajeet Pradhan^4,*, Abdullah Almari⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

Amani Baazeem¹, Muhammad Shoaib Arif^2,*, Yasir Nawaz³, Kamaleldin Abodayeh²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3463-3489, 2025, DOI:10.32604/cmes.2025.066299 - 30 June 2025

Abstract The numerical approximation of stochastic partial differential equations (SPDEs), particularly those including q-diffusion, poses considerable challenges due to the requirements for high-order precision, stability amongst random perturbations, and processing efficiency. Because of their simplicity, conventional numerical techniques like the Euler-Maruyama method are frequently employed to solve stochastic differential equations; nonetheless, they may have low-order accuracy and lower stability in stiff or high-resolution situations. This study proposes a novel computational scheme for solving SPDEs arising from a stochastic SEIR model with q-diffusion and a general incidence rate function. A proposed computational scheme can be used to… More >

Kirubavathi G.^1,*, Arjun Pulliyasseri¹, Aswathi Rajesh¹, Amal Ajayan¹, Sultan Alfarhood^2,*, Mejdl Safran², Meshal Alfarhood², Jungpil Shin³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3005-3031, 2025, DOI:10.32604/cmes.2025.065698 - 30 June 2025

Abstract The exponential expansion of the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Transportation Management of Things (TMoT) produces vast amounts of real-time streaming data. Ensuring system dependability, operational efficiency, and security depends on the identification of anomalies in these dynamic and resource-constrained systems. Due to their high computational requirements and inability to efficiently process continuous data streams, traditional anomaly detection techniques often fail in IoT systems. This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems. Extensive experiments were carried out on multiple real-world datasets, achieving… More >

Tawfeeq Shawly^1,2, Ahmed A. Alsheikhy^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3033-3064, 2025, DOI:10.32604/cmes.2025.065264 - 30 June 2025

Abstract Epilepsy is a long-term neurological condition marked by recurrent seizures, which result from abnormal electrical activity in the brain that disrupts its normal functioning. Traditional methods for detecting epilepsy through machine learning typically utilize discrete-time models, which inadequately represent the continuous dynamics of electroencephalogram (EEG) signals. To overcome this limitation, we introduce an innovative approach that employs Neural Ordinary Differential Equations (NODEs) to model EEG signals as continuous-time systems. This allows for effective management of irregular sampling and intricate temporal patterns. In contrast to conventional techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural… More >

Jong-Yih Kuo¹, Ping-Feng Wang^2,*, Ti-Feng Hsieh^1,*, Cheng-Hsuan Kuo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3133-3166, 2025, DOI:10.32604/cmes.2025.064104 - 30 June 2025

Abstract In recent years, cyber threats have escalated across diverse sectors, with cybercrime syndicates increasingly exploiting system vulnerabilities. Traditional passive defense mechanisms have proven insufficient, particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets. According to Trend Micro, there has been a substantial increase in Linux-targeted malware, with ransomware attacks on Linux surpassing those on macOS. This alarming trend underscores the need for detection strategies specifically designed for Linux environments. To address this challenge, this study proposes a comprehensive malware detection framework tailored for Linux systems, integrating dynamic behavioral analysis with the… More >

Sahbi Boubaker^1,*, Sameer Al-Dahidi², Faisal S. Alsubaei³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3583-3614, 2025, DOI:10.32604/cmes.2025.063957 - 30 June 2025

Abstract The transportation and logistics sectors are major contributors to Greenhouse Gase (GHG) emissions. Carbon dioxide (CO₂) from Light-Duty Vehicles (LDVs) is posing serious risks to air quality and public health. Understanding the extent of LDVs’ impact on climate change and human well-being is crucial for informed decision-making and effective mitigation strategies. This study investigates the predictability of CO₂ emissions from LDVs using a comprehensive dataset that includes vehicles from various manufacturers, their CO₂ emission levels, and key influencing factors. Specifically, six Machine Learning (ML) algorithms, ranging from simple linear models to complex non-linear models, were applied under… More >

Trung-Hieu Tran^*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 869-886, 2025, DOI:10.32604/sdhm.2025.064902 - 30 June 2025

Abstract The design principles for conventional reinforced concrete structures have gradually transitioned to seismic-resistant design since the 1970s. However, until recently, the implementation of strength capacity and ductility design has not been rigorously enforced in many developing countries that are prone to seismic risks. Numerous studies have evaluated the effectiveness of joint behavior based on both ductile and non-ductile designs under cyclic loading. Previous research has demonstrated that enhancing joint regions with Ultra-High Performance Steel Fiber Reinforced Concrete (UHPSFRC) significantly improves the seismic resistance of structural components. This paper presents a detailed analysis of the considerable… More >

Mahmoud Bady¹, Fitrian Imaduddin^1,2, Iskander Tlili^1,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1339-1352, 2025, DOI:10.32604/fdmp.2025.065295 - 30 June 2025

Abstract This study explores the bioconvective behavior of a Reiner-Rivlin nanofluid, accounting for spatially varying thermal properties. The flow is considered over a porous, stretching surface with mass suction effects incorporated into the transport analysis. The Reiner-Rivlin nanofluid model includes variable thermal conductivity, mass diffusivity, and motile microorganism density to accurately reflect realistic biological conditions. Radiative heat transfer and internal heat generation are considered in the thermal energy equation, while the Cattaneo-Christov theory is employed to model non-Fourier heat and mass fluxes. The governing equations are non-dimensionalized to reduce complexity, and a numerical solution is obtained More >

Ibrahim T. Teke^1,2, Ahmet H. Ertas^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 243-264, 2025, DOI:10.32604/cmc.2025.066086 - 09 June 2025

Abstract This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting, runner system optimization, and structural analysis to significantly enhance the performance of injection-molded parts. At its core, the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles, leading to improvements in both mechanical strength and material efficiency. The design optimization is validated through a series of rigorous experimental tests, including three-point bending and torsion tests performed on key-socket frames, ensuring that the optimized designs meet practical performance requirements. A critical innovation of… More >