Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Nanjiang Zhong^*, Xinchen Jiang, Yuan Yao

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4741-4757, 2025, DOI:10.32604/cmc.2025.059536 - 06 March 2025

Abstract The proliferation of rumors on social media has caused serious harm to society. Although previous research has attempted to use deep learning methods for rumor detection, they did not simultaneously consider the two key features of temporal and spatial domains. More importantly, these methods struggle to automatically generate convincing explanations for the detection results, which is crucial for preventing the further spread of rumors. To address these limitations, this paper proposes a novel method that integrates both temporal and spatial features while leveraging Large Language Models (LLMs) to automatically generate explanations for the detection results.… More >

Gang Hao, Peng Liang^*, Ziyuan Li, Huimin Zhao, Hong Zhang

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5221-5238, 2025, DOI:10.32604/cmc.2025.055739 - 06 March 2025

Abstract In recent years, the detection of image copy-move forgery (CMFD) has become a critical challenge in verifying the authenticity of digital images, particularly as image manipulation techniques evolve rapidly. While deep convolutional neural networks (DCNNs) have been widely employed for CMFD tasks, they are often hindered by a notable limitation: the progressive reduction in spatial resolution during the encoding process, which leads to the loss of critical image details. These details are essential for the accurate detection and localization of image copy-move forgery. To overcome the limitations of existing methods, this paper proposes a Transformer-based… More >

Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

Eliel Martins¹, Javier Bermejo Higuera^2,*, Ricardo Sant’Ana¹, Juan Ramón Bermejo Higuera², Juan Antonio Sicilia Montalvo², Diego Piedrahita Castillo³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >

Gaoshuai Su^1,2, Hongyang Li^1,*, Huafeng Chen^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2527-2545, 2025, DOI:10.32604/cmes.2025.059452 - 03 March 2025

Abstract Brain tumors, one of the most lethal diseases with low survival rates, require early detection and accurate diagnosis to enable effective treatment planning. While deep learning architectures, particularly Convolutional Neural Networks (CNNs), have shown significant performance improvements over traditional methods, they struggle to capture the subtle pathological variations between different brain tumor types. Recent attention-based models have attempted to address this by focusing on global features, but they come with high computational costs. To address these challenges, this paper introduces a novel parallel architecture, ParMamba, which uniquely integrates Convolutional Attention Patch Embedding (CAPE) and the… More >

Yahia Said^1,2,*, Yahya Alassaf³, Refka Ghodhbani⁴, Taoufik Saidani⁴, Olfa Ben Rhaiem⁵

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

Xin Fan^1,2, Zhenlei Fu^1,2,*, Jian Shu^1,2, Zuxiong Shen^1,2, Yun Ge^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2583-2607, 2025, DOI:10.32604/cmc.2024.057695 - 17 February 2025

Abstract Spectrum-based fault localization (SBFL) generates a ranked list of suspicious elements by using the program execution spectrum, but the excessive number of elements ranked in parallel results in low localization accuracy. Most researchers consider intra-class dependencies to improve localization accuracy. However, some studies show that inter-class method call type faults account for more than 20%, which means such methods still have certain limitations. To solve the above problems, this paper proposes a two-phase software fault localization based on relational graph convolutional neural networks (Two-RGCNFL). Firstly, in Phase 1, the method call dependence graph (MCDG) of… More >

Mustafa Youldash¹, Atta Rahman^2,*, Manar Alsayed¹, Abrar Sebiany¹, Joury Alzayat¹, Noor Aljishi¹, Ghaida Alshammari¹, Mona Alqahtani¹

Intelligent Automation & Soft Computing, Vol.40, pp. 125-143, 2025, DOI:10.32604/iasc.2025.058509 - 23 January 2025

Abstract Diabetic retinopathy (DR) is a complication of diabetes that can lead to reduced vision or even blindness if left untreated. Therefore, early and accurate detection of this disease is crucial for diabetic patients to prevent vision loss. This study aims to develop a deep-learning approach for the early and precise diagnosis of DR, as manual detection can be time-consuming, costly, and prone to human error. The classification task is divided into two groups for binary classification: patients with DR (diagnoses 1–4) and those without DR (diagnosis 0). For multi-class classification, the categories are no DR,… More >

Syed Tasnimul Karim Ayon^1,#, Farhan Md. Siraj^1,#, Jia Uddin^2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 499-520, 2025, DOI:10.32604/cmc.2025.058361 - 03 January 2025

Abstract This study investigates the application of Learnable Memory Vision Transformers (LMViT) for detecting metal surface flaws, comparing their performance with traditional CNNs, specifically ResNet18 and ResNet50, as well as other transformer-based models including Token to Token ViT, ViT without memory, and Parallel ViT. Leveraging a widely-used steel surface defect dataset, the research applies data augmentation and t-distributed stochastic neighbor embedding (t-SNE) to enhance feature extraction and understanding. These techniques mitigated overfitting, stabilized training, and improved generalization capabilities. The LMViT model achieved a test accuracy of 97.22%, significantly outperforming ResNet18 (88.89%) and ResNet50 (88.90%), as well… More >