Fang Yuan, Xueyao Gao^*, Chunxiang Zhang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5037-5055, 2025, DOI:10.32604/cmc.2025.067760 - 23 October 2025

Abstract 3D model classification has emerged as a significant research focus in computer vision. However, traditional convolutional neural networks (CNNs) often struggle to capture global dependencies across both height and width dimensions simultaneously, leading to limited feature representation capabilities when handling complex visual tasks. To address this challenge, we propose a novel 3D model classification network named ViT-GE (Vision Transformer with Global and Efficient Attention), which integrates Global Grouped Coordinate Attention (GGCA) and Efficient Channel Attention (ECA) mechanisms. Specifically, the Vision Transformer (ViT) is employed to extract comprehensive global features from multi-view inputs using its self-attention More >

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 >

Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025

Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5159-5181, 2025, DOI:10.32604/cmc.2025.067241 - 23 October 2025

Abstract Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random… More >

Sudhakar Tummala^1,2,*, Sajjad Hussain Chauhdary³, Vikash Singh⁴, Roshan Kumar⁵, Seifedine Kadry⁶, Jungeun Kim^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3585-3605, 2025, DOI:10.32604/cmes.2025.069731 - 30 September 2025

Abstract Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets:… More >

Myoung-oh Choi¹, Mincheol Shin¹, Hyonjun Kang¹, Ka Lok Man², Mucheol Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3191-3212, 2025, DOI:10.32604/cmes.2025.068723 - 30 September 2025

Abstract The escalating complexity and heterogeneity of modern energy systems—particularly in smart grid and distributed energy infrastructures—has intensified the need for intelligent and scalable security vulnerability classification. To address this challenge, we propose Vulnerability2Vec, a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience. Vulnerability2Vec converts Common Vulnerabilities and Exposures (CVE) text explanations to semantic graphs, where nodes represent CVE IDs and key terms (nouns, verbs, and adjectives), and edges capture co-occurrence relationships. Then, it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram More >

Mian Muhammad Kamal^1,*, Syed Zain Ul Abideen², M. A. Al-Khasawneh^3,4, Alaa M. Momani⁴, Hala Mostafa⁵, Mohammed Salem Atoum⁶, Saeed Ullah⁷, Jamil Abedalrahim Jamil Alsayaydeh^8,*, Mohd Faizal Bin Yusof⁹, Suhaila Binti Mohd Najib⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3053-3083, 2025, DOI:10.32604/cmes.2025.067430 - 30 September 2025

Abstract Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue. However, low resolution, occlusion, and background interference make small object detection a complex and demanding task. One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities. This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations, such as Hyperspectral–Multispectral (HS-MS), Hyperspectral–Synthetic Aperture Radar (HS-SAR), and HS-SAR–Digital Surface Model (HS-SAR-DSM). The detection process is done by the proposed Jaccard Deep Q-Net (JDQN), which More >

Xiao-Xiao Luo, Bi Peng, Jian-Hua Wang, Guang-Yuan Hu, Xiang-Lin Yuan, Guo-Xian Long^*

Oncology Research, Vol.33, No.10, pp. 2903-2921, 2025, DOI:10.32604/or.2025.066308 - 26 September 2025

Abstract Objective: The World Health Organization (WHO) grading based on histopathology cannot always accurately predict tumor behavior of meningiomas. To overcome the limitations of the WHO grading, the study aims to propose a novel oxidative stress-based molecular classification for WHO grade 2/3 meningiomas. Methods: Differentially expressed oxidative stress-related genes were analyzed between 86 WHO grade 1 (low grade) meningiomas and 99 grade 2/3 (high grade) meningiomas. An oxidative stress-based molecular classification was developed in high-grade meningiomas through consensus clustering analysis. Immune microenvironment features, responses to immunotherapy and chemotherapy, and targeted drugs were evaluated. Three machine learning… More >

Hoang-Dieu Vu^1,2, Duc-Nghia Tran³, Quang-Tu Pham¹, Ngoc-Linh Nguyen^4,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2819-2835, 2025, DOI:10.32604/cmc.2025.068355 - 23 September 2025

Abstract This study presents CGB-Net, a novel deep learning architecture specifically developed for classifying twelve distinct sleep positions using a single abdominal accelerometer, with direct applicability to gastroesophageal reflux disease (GERD) monitoring. Unlike conventional approaches limited to four basic postures, CGB-Net enables fine-grained classification of twelve clinically relevant sleep positions, providing enhanced resolution for personalized health assessment. The architecture introduces a unique integration of three complementary components: 1D Convolutional Neural Networks (1D-CNN) for efficient local spatial feature extraction, Gated Recurrent Units (GRU) to capture short-term temporal dependencies with reduced computational complexity, and Bidirectional Long Short-Term Memory… More >

Saad Sadiq¹, Kashif Sultan¹, Muhammad Sheraz², Teong Chee Chuah^2,*, Muhammad Usman Hashmi³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3945-3963, 2025, DOI:10.32604/cmc.2025.067538 - 23 September 2025

Abstract Vehicle recognition plays a vital role in intelligent transportation systems, law enforcement, access control, and security operations—domains that are becoming increasingly dynamic and complex. Despite advancements, most existing solutions remain siloed, addressing individual tasks such as vehicle make and model recognition (VMMR), automatic number plate recognition (ANPR), and color classification separately. This fragmented approach limits real-world efficiency, leading to slower processing, reduced accuracy, and increased operational costs, particularly in traffic monitoring and surveillance scenarios. To address these limitations, we present a unified framework that consolidates all three recognition tasks into a single, lightweight system. The More >