Nour El Houda Kaiber¹, Tahar Mekhaznia¹, Akram Bennour^1,*, Mohammed Al-Sarem^2,3,*, Zakaria Lakhdara⁴, Fahad Ghaban², Mohammad Nassef^5,6

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070337 - 12 January 2026

Abstract The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness. Deep learning has emerged as a promising solution, offering new avenues for improvements. However, building models from scratch is computationally expensive and requires large datasets. This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image. The core idea is to fine-tune a pre-trained model on specific object categories using new, unseen data, resulting in specialized versions of the model that are better adapted to reconstruct particular objects. The proposed approach utilizes a… More >

Stellar Choi¹, HeeAe Ko², KyungRok Bae³, HyunSook Lee², HaeJong Joo⁴, Woong Choi^5,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 335-355, 2025, DOI:10.32604/cmc.2025.061243 - 26 March 2025

Abstract Previous research utilizing Cartoon Generative Adversarial Network (CartoonGAN) has encountered limitations in managing intricate outlines and accurately representing lighting effects, particularly in complex scenes requiring detailed shading and contrast. This paper presents a novel Enhanced Pixel Integration (EPI) technique designed to improve the visual quality of images generated by CartoonGAN. Rather than modifying the core model, the EPI approach employs post-processing adjustments that enhance images without significant computational overhead. In this method, images produced by CartoonGAN are converted from Red-Green-Blue (RGB) to Hue-Saturation-Value (HSV) format, allowing for precise adjustments in hue, saturation, and brightness, thereby… More >

S. Kalyani^*, K. Venkata Rao, A. Mary Sowjanya

Structural Durability & Health Monitoring, Vol.15, No.4, pp. 317-334, 2021, DOI:10.32604/sdhm.2021.016975 - 23 November 2021

Abstract Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure.… More >