Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

Sandeerah Choudhary¹, Qaisar Abbas², Tallha Akram^3,*, Irshad Qureshi⁴, Mutlaq B. Aldajani², Hammad Salahuddin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1755-1787, 2025, DOI:10.32604/cmes.2025.072200 - 26 November 2025

Abstract The increasing demand for sustainable construction practices has led to growing interest in recycled aggregate concrete (RAC) as an eco-friendly alternative to conventional concrete. However, predicting its compressive strength remains a challenge due to the variability in recycled materials and mix design parameters. This study presents a robust machine learning framework for predicting the compressive strength of recycled aggregate concrete using feedforward neural networks (FFNN), Random Forest (RF), and XGBoost. A literature-derived dataset of 502 samples was enriched via interpolation-based data augmentation and modeled using five distinct optimization techniques within MATLAB’s Neural Net Fitting module:… More >

Canlin Cui¹, Junyu Yao^1,*, Heng Xia^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4275-4306, 2025, DOI:10.32604/cmc.2025.069097 - 23 October 2025

Abstract High-quality data is essential for the success of data-driven learning tasks. The characteristics, precision, and completeness of the datasets critically determine the reliability, interpretability, and effectiveness of subsequent analyzes and applications, such as fault detection, predictive maintenance, and process optimization. However, for many industrial processes, obtaining sufficient high-quality data remains a significant challenge due to high costs, safety concerns, and practical constraints. To overcome these challenges, data augmentation has emerged as a rapidly growing research area, attracting considerable attention across both academia and industry. By expanding datasets, data augmentation techniques improve greater generalization and more… More >

Alex Varghese¹, Achin Jain², Mohammed Inamur Rahman³, Mudassir Khan^4,*, Arun Kumar Dubey², Iqrar Ahmad⁵, Yash Prakash Narayan¹, Arvind Panwar⁶, Anurag Choubey⁷, Saurav Mallik^8,9,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1767-1791, 2025, DOI:10.32604/cmes.2025.067999 - 31 August 2025

Abstract Skin cancer remains a significant global health challenge, and early detection is crucial to improving patient outcomes. This study presents a novel deep learning framework that combines Convolutional Neural Networks (CNNs), Transformers, and Gated Recurrent Units (GRUs) for robust skin cancer classification. To address data set imbalance, we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques. The hybrid architecture effectively captures both local and global dependencies in dermoscopic images, while the GRU component models sequential patterns. Evaluated on the HAM10000 dataset, the proposed model achieves an accuracy of 90.61%, outperforming baseline architectures such as VGG16 More >

Huichao Cao^1,*, Honghe Du¹, Dongnian Jiang¹, Wei Li¹, Lei Du¹, Jianfeng Yang²

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1305-1325, 2025, DOI:10.32604/sdhm.2025.066002 - 05 September 2025

Abstract In the production processes of modern industry, accurate assessment of the system’s health state and traceability non-optimal factors are key to ensuring “safe, stable, long-term, full load and optimal” operation of the production process. The benzene-to-ethylene ratio control system is a complex system based on an MPC-PID double-layer architecture. Taking into consideration the interaction between levels, coupling between loops and conditions of incomplete operation data, this paper proposes a health assessment method for the dual-layer control system by comprehensively utilizing deep learning technology. Firstly, according to the results of the pre-assessment of the system layers… More >

Huagang Jin, Yu Zhou^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 751-768, 2025, DOI:10.32604/cmc.2025.067867 - 29 August 2025

Abstract Recently, ship detection technology has been applied extensively in the marine security monitoring field. However, achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales, slightly occluded objects, uneven illumination, and sea clutter. To address these issues, we propose a novel ship detection approach, i.e., the Twin Feature Pyramid Network and Data Augmentation (TFPN-DA), which mainly consists of three modules. First, to eliminate the negative effects of slightly occluded objects and uneven illumination, we propose the Spatial Attention within the Twin Feature Pyramid Network (SA-TFPN) method, which is based More >

Yu-Xiang Zhao^*, Yi Lee

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 791-810, 2025, DOI:10.32604/cmc.2025.065048 - 09 June 2025

Abstract Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research. Additionally, performing edge computing on low-level devices using small neural networks can be an important research direction. In this paper, we use the EfficientNetV2B0 model for bird species classification, applying transfer learning on a dataset of 525 bird species. We also employ the BiRefNet model to remove backgrounds from images in the training set. The generated background-removed images are mixed with the original training set as a form of data augmentation.… More >

Rongwei Yu¹, Jie Yin^1,*, Jingyi Xiang¹, Qiyun Shao², Lina Wang¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 365-391, 2025, DOI:10.32604/cmc.2025.064438 - 09 June 2025

Abstract With the emergence of new attack techniques, traffic classifiers usually fail to maintain the expected performance in real-world network environments. In order to have sufficient generalizability to deal with unknown malicious samples, they require a large number of new samples for retraining. Considering the cost of data collection and labeling, data augmentation is an ideal solution. We propose an optimized noise-based traffic data augmentation system, ONTDAS. The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise. The noise is injected into the original samples for data augmentation. Then, an More >

Tehreem Fatima¹, Kewen Xia^1,*, Wenbiao Yang², Qurat Ul Ain¹, Poornima Lankani Perera¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 811-826, 2025, DOI:10.32604/cmc.2025.063686 - 09 June 2025

Abstract The rising prevalence of diabetes in modern society underscores the urgent need for precise and efficient diagnostic tools to support early intervention and treatment. However, the inherent limitations of existing datasets, including significant class imbalances and inadequate sample diversity, pose challenges to the accurate prediction and classification of diabetes. Addressing these issues, this study proposes an innovative diabetes prediction framework that integrates a hybrid Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model for classification with Adaptive Synthetic Sampling (ADASYN) for data augmentation. ADASYN was employed to generate synthetic yet representative data samples, effectively mitigating class… More >

Tengyue Li^1,*, Shuangli Song¹, Jiaming Zhou², Simon Fong^2,3, Geyue Li⁴, Qun Song³, Sabah Mohammed⁵, Weiwei Lin⁶, Juntao Gao⁷

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1711-1730, 2025, DOI:10.32604/cmc.2025.062489 - 09 June 2025

Abstract Hematoxylin and Eosin (H&E) images, popularly used in the field of digital pathology, often pose challenges due to their limited color richness, hindering the differentiation of subtle cell features crucial for accurate classification. Enhancing the visibility of these elusive cell features helps train robust deep-learning models. However, the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community. To address this challenge, we introduce Salient Features Guided Augmentation (SFGA), an approach that strategically integrates machine learning and image processing. SFGA utilizes machine learning algorithms to identify… More >