Sidra Naseem¹, Rashid Jahangir^1,*, Nazik Alturki², Faheem Shehzad³, Muhammad Sami Ullah⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575 - 26 November 2025

Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

Diyar Wirya Omar Ameenulhakeem^*, Osman Nuri Uçan

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5671-5702, 2025, DOI:10.32604/cmc.2025.070422 - 23 October 2025

Abstract Face antispoofing has received a lot of attention because it plays a role in strengthening the security of face recognition systems. Face recognition is commonly used for authentication in surveillance applications. However, attackers try to compromise these systems by using spoofing techniques such as using photos or videos of users to gain access to services or information. Many existing methods for face spoofing face difficulties when dealing with new scenarios, especially when there are variations in background, lighting, and other environmental factors. Recent advancements in deep learning with multi-modality methods have shown their effectiveness in… More >

Ming Chen^1,*, Yanfei Niu², Ping Qi¹, Fucheng Wang¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5015-5035, 2025, DOI:10.32604/cmc.2025.067951 - 23 October 2025

Abstract This research addresses the critical challenge of enhancing satellite images captured under low-light conditions, which suffer from severely degraded quality, including a lack of detail, poor contrast, and low usability. Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks (e.g., spacecraft on-orbit connection, spacecraft surface repair, space debris capture) that rely on clear visual information. Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions: (1) an improved U-Net (IU-Net) generator with multi-scale feature fusion in the contracting path for richer semantic feature… More >

Huayu Li^1,2, Xinxin Chen^1,2, Lizhuang Tan^3,4,*, Konstantin I. Kostromitin^5,6, Athanasios V. Vasilakos⁷, Peiying Zhang^1,2

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4133-4153, 2025, DOI:10.32604/cmc.2025.069690 - 23 September 2025

Abstract To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising from modal heterogeneity during fusion, while also capturing shared information across modalities, this paper proposes a Multi-modal Pre-synergistic Entity Alignment model based on Cross-modal Mutual Information Strategy Optimization (MPSEA). The model first employs independent encoders to process multi-modal features, including text, images, and numerical values. Next, a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information. This pre-fusion strategy enables unified perception of heterogeneous modalities at the More >

Gang Li^1,#, Ru Wang^1,#, Yang Zhang^2,*, Chuanyun Xu², Xinyu Fan¹, Zheng Zhou¹, Pengfei Lv¹, Zihan Ruan¹

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3267-3288, 2025, DOI:10.32604/cmc.2025.067763 - 23 September 2025

Abstract Currently, challenges such as small object size and occlusion lead to a lack of accuracy and robustness in small object detection. Since small objects occupy only a few pixels in an image, the extracted features are limited, and mainstream downsampling convolution operations further exacerbate feature loss. Additionally, due to the occlusion-prone nature of small objects and their higher sensitivity to localization deviations, conventional Intersection over Union (IoU) loss functions struggle to achieve stable convergence. To address these limitations, LR-Net is proposed for small object detection. Specifically, the proposed Lossless Feature Fusion (LFF) method transfers spatial… More >

Zihan Jia, Bowen Ma, Dongyue Chen^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2873-2891, 2025, DOI:10.32604/cmc.2025.067460 - 23 September 2025

Abstract In real-world scenarios, dual-view X-ray machines have outnumbered single-view X-ray machines due to their ability to provide comprehensive internal information about the baggage, which is important for identifying prohibited items that are not visible in one view due to rotation or overlap. However, existing work still focuses mainly on single-view, and the limited dual-view based work only performs simple information fusion at the feature or decision level and lacks effective utilization of the complementary information hidden in dual view. To this end, this paper proposes an end-to-end dual-view prohibited item detection method, the core of… More >

Zehui Qi, Sixing Wu^*, Jianbin Li

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3739-3766, 2025, DOI:10.32604/cmc.2025.067180 - 23 September 2025

Abstract False Data Injection Attacks (FDIAs) pose a critical security threat to modern power grids, corrupting state estimation and enabling malicious control actions that can lead to severe consequences, including cascading failures, large-scale blackouts, and significant economic losses. While detecting attacks is important, accurately localizing compromised nodes or measurements is even more critical, as it enables timely mitigation, targeted response, and enhanced system resilience beyond what detection alone can offer. Existing research typically models topological features using fixed structures, which can introduce irrelevant information and affect the effectiveness of feature extraction. To address this limitation, this… More >

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025

Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >

Shuo Zhang, Hanwu He, Yueming Wu^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 801-826, 2025, DOI:10.32604/cmc.2025.066540 - 29 August 2025

Abstract Virtual maintenance, as an important means of industrial training and education, places strict requirements on the accuracy of participant pose perception and assessment of motion standardization. However, existing research mainly focuses on human pose estimation in general scenarios, lacking specialized solutions for maintenance scenarios. This paper proposes a virtual maintenance human pose estimation method based on multi-scale feature enhancement (VMHPE), which integrates adaptive input feature enhancement, multi-scale feature correction for improved expression of fine movements and complex poses, and multi-scale feature fusion to enhance keypoint localization accuracy. Meanwhile, this study constructs the first virtual maintenance-specific… More >

Chuanchuan Wang^1,2, Ahmad Sufril Azlan Mohamed^2,*, Xiao Yang ², Hao Zhang ², Xiang Li¹, Mohd Halim Bin Mohd Noor ²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 855-874, 2025, DOI:10.32604/cmc.2025.066343 - 29 August 2025

Abstract Classroom behavior recognition is a hot research topic, which plays a vital role in assessing and improving the quality of classroom teaching. However, existing classroom behavior recognition methods have challenges for high recognition accuracy with datasets with problems such as scenes with blurred pictures, and inconsistent objects. To address this challenge, we proposed an effective, lightweight object detector method called the RFNet model (YOLO-FR). The YOLO-FR is a lightweight and effective model. Specifically, for efficient multi-scale feature extraction, effective feature pyramid shared convolutional (FPSC) was designed to improve the feature extract performance by leveraging convolutional… More >