Soufiane Khedairia¹, Akram Bennour^2,*, Mouaaz Nahas³, Aida Chefrour¹, Rashiq Rafiq Marie⁴, Mohammed Al-Sarem⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1267-1285, 2025, DOI:10.32604/cmc.2025.066601 - 29 August 2025

Abstract These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes… More >

Lang Ding, Qinmu Wu^*, Jiaheng Li, Tao Hong, Linqing Bian

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1999-2020, 2025, DOI:10.32604/cmc.2025.066423 - 29 August 2025

Abstract Trajectory prediction is a critical task in autonomous driving systems. It enables vehicles to anticipate the future movements of surrounding traffic participants, which facilitates safe and human-like decision-making in the planning and control layers. However, most existing approaches rely on end-to-end deep learning architectures that overlook the influence of driving style on trajectory prediction. These methods often lack explicit modeling of semantic driving behavior and effective interaction mechanisms, leading to potentially unrealistic predictions. To address these limitations, we propose the Driving Style Guided Trajectory Prediction framework (DSG-TP), which incorporates a probabilistic representation of driving style… More >

Yuanqing Ding^1,2, Hanming Zhai¹, Qiming Ma¹, Liang Zhang¹, Lei Shao², Fanliang Bu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 905-923, 2025, DOI:10.32604/cmc.2025.066307 - 29 August 2025

Abstract As the use of deepfake facial videos proliferate, the associated threats to social security and integrity cannot be overstated. Effective methods for detecting forged facial videos are thus urgently needed. While many deep learning-based facial forgery detection approaches show promise, they often fail to delve deeply into the complex relationships between image features and forgery indicators, limiting their effectiveness to specific forgery techniques. To address this challenge, we propose a dual-branch collaborative deepfake detection network. The network processes video frame images as input, where a specialized noise extraction module initially extracts the noise feature maps.… More >

Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Yong Liu^1,2, Tianning Sun^3,*, Daofu Gong^1,4, Li Di⁵, Xu Zhao¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1161-1184, 2025, DOI:10.32604/cmc.2025.062330 - 29 August 2025

Abstract To address the high-quality forged videos, traditional approaches typically have low recognition accuracy and tend to be easily misclassified. This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content (AIGC) video authenticity detection with a multimodal information fusion approach. First, a high-quality multimodal video dataset is collected and normalized, including resolution correction and frame rate unification. Next, feature extraction techniques are employed to draw out features from visual, audio, and text modalities. Subsequently, these features are fused into a multilayer perceptron and attention mechanisms-based More >

Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

Journal on Artificial Intelligence, Vol.7, pp. 255-274, 2025, DOI:10.32604/jai.2025.069008 - 29 August 2025

Abstract Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% More >

Yao-Tien Chen¹, Nisar Ahmad^1,*, Khursheed Aurangzeb²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1197-1224, 2025, DOI:10.32604/cmes.2025.066580 - 31 July 2025

Abstract Accurate and efficient brain tumor segmentation is essential for early diagnosis, treatment planning, and clinical decision-making. However, the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection. While U-Net-based architectures have demonstrated strong performance in medical image segmentation, there remains room for improvement in feature extraction and localization accuracy. In this study, we propose a novel hybrid model designed to enhance 3D brain tumor segmentation. The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder. Additionally, to… More > Graphic Abstract

Haiyan Chen^*, Rong Li

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5693-5712, 2025, DOI:10.32604/cmc.2025.065339 - 30 July 2025

Abstract The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations. In natural environments, the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels, and their distinguishing features are often subtle, complicating the target detection process. To effectively extract the characteristics of these small targets, a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features, ultimately improving detection accuracy. However, the top-down… More >

Zongzhe Xu, Ming Yu^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2805-2826, 2025, DOI:10.32604/cmc.2025.066244 - 03 July 2025

Abstract As the group-buying model shows significant progress in attracting new users, enhancing user engagement, and increasing platform profitability, providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems. This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning, termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation (MAMGBR) model, specifically designed to optimize group-buying recommendations on e-commerce platforms. The core dataset of this study comes from the Chinese maternal and infant e-commerce platform “Beibei,” encompassing approximately 430,000 successful group-buying actions and… More >

Zhunruo Feng¹, Ruomeng Shi², Yuhan Jiang³, Yiming Han¹, Zeyang Ma¹, Yuheng Ren^4,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3559-3575, 2025, DOI:10.32604/cmc.2025.065152 - 03 July 2025

Abstract In this study, we propose Space-to-Depth and You Only Look Once Version 7 (SPD-YOLOv7), an accurate and efficient method for detecting pests in maize crops, addressing challenges such as small pest sizes, blurred images, low resolution, and significant species variation across different growth stages. To improve the model’s ability to generalize and its robustness, we incorporate target background analysis, data augmentation, and processing techniques like Gaussian noise and brightness adjustment. In target detection, increasing the depth of the neural network can lead to the loss of small target information. To overcome this, we introduce the… More >