Open Access
ARTICLE
Wencheng Wang1,2,*, Baoxin Yin1,2, Lei Li2,*, Lun Li1, Hongtao Liu1
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063595
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 Access
ARTICLE
Umit Cigdem Turhal1, Yasemin Onal1,*, Kutalmis Turhal2
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.064269
（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
Open Access
ARTICLE
Gebrail Bekdaş1, Yaren Aydın1, Celal Cakiroglu2, Umit Işıkdağ3,*
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.063909
（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 Access
ARTICLE
Smita Mahajan1, Shilpa Gite1,2, Biswajeet Pradhan3,*, Abdullah Alamri4, Shaunak Inamdar5, Deva Shriyansh5, Akshat Ashish Shah5, Shruti Agarwal5
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.058456
（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 >
Login
Register
Submit a Paper
Propose a Special lssue
