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 >

Ze Wang, Jin Qin, Chuhua Huang^*, Yongjun Zhang^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5811-5829, 2025, DOI:10.32604/cmc.2025.064537 - 30 July 2025

Abstract Semantic segmentation has made significant breakthroughs in various application fields, but achieving both accurate and efficient segmentation with limited computational resources remains a major challenge. To this end, we propose CGMISeg, an efficient semantic segmentation architecture based on a context-guided multi-scale interaction strategy, aiming to significantly reduce computational overhead while maintaining segmentation accuracy. CGMISeg consists of three core components: context-aware attention modulation, feature reconstruction, and cross-information fusion. Context-aware attention modulation is carefully designed to capture key contextual information through channel and spatial attention mechanisms. The feature reconstruction module reconstructs contextual information from different scales, modeling… More >

Xinyi Wang^*, Wanru Song, Chuanyan Hao, Feng Liu

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3351-3368, 2025, DOI:10.32604/cmc.2025.062922 - 16 April 2025

Abstract As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection… More >

Sarah M. Kamel^1,*, Mai A. Fadel², Lamiaa Elrefaei^1,3, Shimaa I. Hassan^1,4

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 373-411, 2025, DOI:10.32604/cmes.2025.062837 - 11 April 2025

Abstract Visual question answering (VQA) is a multimodal task, involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer. In this paper, we propose a VQA system intended to answer yes/no questions about real-world images, in Arabic. To support a robust VQA system, we work in two directions: (1) Using deep neural networks to semantically represent the given image and question in a fine-grained manner, namely ResNet-152 and Gated Recurrent Units (GRU). (2) Studying the role of the utilized multimodal bilinear… More >

Chaozhi Cai, Yuqi Ren, Yingfang Xue^*, Jianhua Ren

Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

Shi Luo, Sheng Zheng^*, Yuxin Zhao

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3881-3901, 2024, DOI:10.32604/cmc.2024.056773 - 19 December 2024

Abstract In the age of smart technology, the widespread use of small LCD (Liquid Crystal Display) necessitates pre-market defect detection to ensure quality and reduce the incidence of defective products. Manual inspection is both time-consuming and labor-intensive. Existing methods struggle with accurately detecting small targets, such as point defects, and handling defects with significant scale variations, such as line defects, especially in complex background conditions. To address these challenges, this paper presents the YOLO-DEI (Deep Enhancement Information) model, which integrates DCNv2 (Deformable convolution) into the backbone network to enhance feature extraction under geometric transformations. The model More >

Amal Al-Rasheed¹, Rahim Khan^2,3,*, Tahani Alsaed⁴, Mahwish Kundi^2,5, Mohamad Hanif Md. Saad⁶, Mahidur R. Sarker^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1305-1323, 2024, DOI:10.32604/cmc.2024.049215 - 18 July 2024

Abstract Due to the overwhelming characteristics of the Internet of Things (IoT) and its adoption in approximately every aspect of our lives, the concept of individual devices’ privacy has gained prominent attention from both customers, i.e., people, and industries as wearable devices collect sensitive information about patients (both admitted and outdoor) in smart healthcare infrastructures. In addition to privacy, outliers or noise are among the crucial issues, which are directly correlated with IoT infrastructures, as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing, i.e.,… More >

Jinlin Xu^1,2, Wansu Pan^1,*, Longle Cheng^1,2, Haibo Tan^1,2, Munan Yuan^1,*, Xiaofeng Li^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1399-1418, 2024, DOI:10.32604/cmc.2024.049622 - 25 April 2024

Abstract The existing multipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is a lack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptation of service requirements and network resources. To address these issues, we propose a multi-constraint path optimization scheme based on information fusion in SDN. The proposed scheme collects network topology and network state information on the network side and computes disjoint paths between end hosts. It uses the Fuzzy Analytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters… More >

Meiyan Zhang¹, Boqi Zhao¹, Jipu Li², Qisong Wang^1,*, Dan Liu¹, Jinwei Sun¹, Jingxiao Liao^1,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1039-1061, 2024, DOI:10.32604/cmc.2024.048643 - 25 April 2024

Abstract Driving fatigue is a physiological phenomenon that often occurs during driving. After the driver enters a fatigued state, the attention is lax, the response is slow, and the ability to deal with emergencies is significantly reduced, which can easily cause traffic accidents. Therefore, studying driver fatigue detection methods is significant in ensuring safe driving. However, the fatigue state of actual drivers is easily interfered with by the external environment (glasses and light), which leads to many problems, such as weak reliability of fatigue driving detection. Moreover, fatigue is a slow process, first manifested in physiological… More >

Kuosheng Jiang¹, Chengrui Han¹, Yasheng Chang^2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3329-3352, 2024, DOI:10.32604/cmes.2024.046729 - 11 March 2024

Abstract Angular contact ball bearings have been widely used in machine tool spindles, and the bearing preload plays an important role in the performance of the spindle. In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties, a roller bearing preload test method based on the improved D-S evidence theory multi-sensor fusion method was proposed. First, a novel controllable preload system is proposed and evaluated. Subsequently, multiple sensors are employed to collect data on the bearing parameters during preload application. Finally, a multisensor fusion algorithm is used More >