Bappa Muktar^*, Vincent Fono, Adama Nouboukpo

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4705-4727, 2025, DOI:10.32604/cmc.2025.067733 - 23 October 2025

Abstract Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization,… More >

Peng Sun^1,2, Dechen Yao^1,2,*, Jianwei Yang^1,2, Quanyu Long^1,2

Structural Durability & Health Monitoring, Vol.19, No.5, pp. 1221-1239, 2025, DOI:10.32604/sdhm.2025.066098 - 05 September 2025

Abstract Supervised learning-based rail fastener anomaly detection models are limited by the scarcity of anomaly samples and perform poorly under data imbalance conditions. However, unsupervised anomaly detection methods based on diffusion models reduce the dependence on the number of anomalous samples but suffer from too many iterations and excessive smoothing of reconstructed images. In this work, we have established a rail fastener anomaly detection framework called Diff-Fastener, the diffusion model is introduced into the fastener detection task, half of the normal samples are converted into anomaly samples online in the model training stage, and One-Step denoising… More >

Vijaya Choudhary^1,2,3,*, Paramita Guha^1,2, Giovanni Pau⁴

Journal on Big Data, Vol.7, pp. 1-14, 2025, DOI:10.32604/jbd.2025.065122 - 12 August 2025

Abstract Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits, supporting improved crop management, breeding programs, and yield optimization. However, cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes, complex vein structures, and variations caused by environmental conditions. This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks. Given the limited availability of annotated datasets, data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics. Various image processing More >

Habib Hadj-Mabrouk^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4399-4430, 2025, DOI:10.32604/cmc.2025.067143 - 30 July 2025

Abstract A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to… More >

Qingyang Zhou¹, Guofeng Lu², Yunfan Ye^3,*, Zhiping Cai¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2411-2427, 2025, DOI:10.32604/cmc.2025.066810 - 03 July 2025

Abstract Computed Tomography (CT) reconstruction is essential in medical imaging and other engineering fields. However, blurring of the projection during CT imaging can lead to artifacts in the reconstructed images. Projection blur combines factors such as larger ray sources, scattering and imaging system vibration. To address the problem, we propose DeblurTomo, a novel self-supervised learning-based deblurring and reconstruction algorithm that efficiently reconstructs sharp CT images from blurry input without needing external data and blur measurement. Specifically, we constructed a coordinate-based implicit neural representation reconstruction network, which can map the coordinates to the attenuation coefficient in the… More >

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 >

Won-Yang Cho, Sangjun Lee^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2847-2863, 2025, DOI:10.32604/cmc.2025.066373 - 03 July 2025

Abstract The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases. However, auscultation is highly subjective, making it challenging to analyze respiratory sounds accurately. Although deep learning has been increasingly applied to this task, most existing approaches have primarily relied on supervised learning. Since supervised learning requires large amounts of labeled data, recent studies have explored self-supervised and semi-supervised methods to overcome this limitation. However, these approaches have largely assumed a closed-set setting, where the classes present in the unlabeled data are considered identical to those in the labeled data. In contrast, this… More >

Peicheng Shi^1,*, Yueyue Tang¹, Yi Li¹, Xinlong Dong¹, Yu Sun², Aixi Yang³

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3321-3343, 2025, DOI:10.32604/cmc.2025.065297 - 03 July 2025

Abstract In the dynamic scene of autonomous vehicles, the depth estimation of monocular cameras often faces the problem of inaccurate edge depth estimation. To solve this problem, we propose an unsupervised monocular depth estimation model based on edge enhancement, which is specifically aimed at the depth perception challenge in dynamic scenes. The model consists of two core networks: a deep prediction network and a motion estimation network, both of which adopt an encoder-decoder architecture. The depth prediction network is based on the U-Net structure of ResNet18, which is responsible for generating the depth map of the… More >

Nan Li^1,2,*, Jingxiong Dong², Liang Tao³, Liang Huang³

Energy Engineering, Vol.122, No.7, pp. 2957-2975, 2025, DOI:10.32604/ee.2025.062667 - 27 June 2025

Abstract With the continuous expansion and increasing complexity of power system scales, the binary classification for transient stability assessment in power systems can no longer meet the safety requirements of power system control and regulation. Therefore, this paper proposes a multi-class transient stability assessment model based on an improved Transformer. The model is designed with a dual-tower encoder structure: one encoder focuses on the time dependency of data, while the other focuses on the dynamic correlations between variables. Feature extraction is conducted from both time and variable perspectives to ensure the completeness of the feature extraction… More > Graphic Abstract

Jinhai Wang¹, Junwei Xue¹, Hongyan Zhang², Hui Xiao^3,4, Huiling Wei^3,4, Mingyou Chen^3,4, Jiang Liao², Lufeng Luo^3,4,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1839-1861, 2025, DOI:10.32604/cmc.2025.064489 - 09 June 2025

Abstract Defect detection based on computer vision is a critical component in ensuring the quality of industrial products. However, existing detection methods encounter several challenges in practical applications, including the scarcity of labeled samples, limited adaptability of pre-trained models, and the data heterogeneity in distributed environments. To address these issues, this research proposes an unsupervised defect detection method, FLAME (Federated Learning with Adaptive Multi-Model Embeddings). The method comprises three stages: (1) Feature learning stage: this work proposes FADE (Feature-Adaptive Domain-Specific Embeddings), a framework employs Gaussian noise injection to simulate defective patterns and implements a feature discriminator… More >