Osnamir E. Bru-Cordero¹, Cecilia Castro², Víctor Leiva^3,*, 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 >

Namal Rathnayake¹, Jeevani Jayasinghe^2,3, Rashmi Semasinghe², Upaka Rathnayake^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464 - 30 May 2025

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 >

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 >

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

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 >

Umit Cigdem Turhal¹, Yasemin Onal^1,*, 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

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

Nguyen Khac Toan¹, Ho Nguyen Anh Tuan², Nguyen Truong Thinh^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1273-1295, 2025, DOI:10.32604/cmes.2025.064218 - 30 May 2025

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

Yongli Liu^1,2, Weihao Li^1,2,*, Haitao Wang^1,2,3, 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

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 >

Priyadharsini Selvaraj^1,*, Senthil Kumar Jagatheesaperumal², Karthiga Marimuthu¹, Oviya Saravanan¹, Bader Fahad Alkhamees³, Mohammad Mehedi Hassan^3,*

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 >

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025

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 >

Lord Amoah^1,2, Jinwei Wang^1,2,3,*, Bernard-Marie Onzo^1,2

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 >