Hongyang Wang^1,2, Yichen Shi³, Jun Feng^1,2,*, Zitong Yu⁴, Zhuofu Tao⁵

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3097-3112, 2025, DOI:10.32604/cmc.2025.061019 - 16 April 2025

Abstract Face Presentation Attack Detection (fPAD) plays a vital role in securing face recognition systems against various presentation attacks. While supervised learning-based methods demonstrate effectiveness, they are prone to overfitting to known attack types and struggle to generalize to novel attack scenarios. Recent studies have explored formulating fPAD as an anomaly detection problem or one-class classification task, enabling the training of generalized models for unknown attack detection. However, conventional anomaly detection approaches encounter difficulties in precisely delineating the boundary between bonafide samples and unknown attacks. To address this challenge, we propose a novel framework focusing on… More >

Haitao Xie, Yuliang Chen, Yunjie Zeng, Lingyu Yan, Zhizhi Wang, Zhiwei Ye^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3389-3410, 2025, DOI:10.32604/cmc.2025.060783 - 16 April 2025

Abstract With the rapid development of intelligent video surveillance technology, pedestrian re-identification has become increasingly important in multi-camera surveillance systems. This technology plays a critical role in enhancing public safety. However, traditional methods typically process images and text separately, applying upstream models directly to downstream tasks. This approach significantly increases the complexity of model training and computational costs. Furthermore, the common class imbalance in existing training datasets limits model performance improvement. To address these challenges, we propose an innovative framework named Person Re-ID Network Based on Visual Prompt Technology and Multi-Instance Negative Pooling (VPM-Net). First, we… More >

Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Martina Rinaldi^1,2, Stefano Valvano^1,*, Francesco Tornabene², Rossana Dimitri²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2403-2428, 2025, DOI:10.32604/cmc.2025.060672 - 16 April 2025

Abstract This study conducts a thorough examination of honeycomb sandwich panels with a lattice core, adopting advanced computational techniques for their modeling. The research extends its analysis to investigate the natural frequency behavior of sandwich panels, encompassing the comprehensive assessment of the entire panel structure. At its core, the research applies the Representative Volume Element (RVE) theory to establish the equivalent material properties, thereby enhancing the predictive capabilities of lattice structure simulations. The methodology applies these properties in the core of infinite panels, which are modeled using double periodic boundary conditions to explore their natural frequencies.… More >

Min Yao^1,*, Guofeng Liu², Yaozu Zhang³, Guangjie Hu¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2189-2205, 2025, DOI:10.32604/cmc.2025.060609 - 16 April 2025

Abstract Knowledge distillation (KD) is an emerging model compression technique for learning compact object detector models. Previous KD often focused solely on distilling from the logits layer or the feature intermediate layers, which may limit the comprehensive learning of the student network. Additionally, the imbalance between the foreground and background also affects the performance of the model. To address these issues, this paper employs feature-based distillation to enhance the detection performance of the bounding box localization part, and logit-based distillation to improve the detection performance of the category prediction part. Specifically, for the intermediate layer feature… More >

Jiming Lan¹, Bo Zeng^1,*, Suiqun Li¹, Weihan Zhang¹, Xinyi Shi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2865-2888, 2025, DOI:10.32604/cmc.2025.060260 - 16 April 2025

Abstract The Quadric Error Metrics (QEM) algorithm is a widely used method for mesh simplification; however, it often struggles to preserve high-frequency geometric details, leading to the loss of salient features. To address this limitation, we propose the Salient Feature Sampling Points-based QEM (SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler (SFSP). This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification. Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details. Specifically, for general models… More >

Xiaotong Han^1,2, Yunqi Jiang^2,3, Haitao Wang^1,2, Yuan Tian^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1951-1971, 2025, DOI:10.32604/cmc.2025.060134 - 16 April 2025

Abstract Knowledge graphs (KGs), which organize real-world knowledge in triples, often suffer from issues of incompleteness. To address this, multi-hop knowledge graph reasoning (KGR) methods have been proposed for interpretable knowledge graph completion. The primary approaches to KGR can be broadly classified into two categories: reinforcement learning (RL)-based methods and sequence-to-sequence (seq2seq)-based methods. While each method has its own distinct advantages, they also come with inherent limitations. To leverage the strengths of each method while addressing their weaknesses, we propose a cyclical training method that alternates for several loops between the seq2seq training phase and the… More >

Mohammed Abdelmajeed^1,*, Jiangbin Zheng¹, Ahmed Murtadha¹, Youcef Nafa¹, Mohammed Abaker², Muhammad Pervez Akhter³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3471-3491, 2025, DOI:10.32604/cmc.2025.059870 - 16 April 2025

Abstract Arabic Dialect Identification (DID) is a task in Natural Language Processing (NLP) that involves determining the dialect of a given piece of text in Arabic. The state-of-the-art solutions for DID are built on various deep neural networks that commonly learn the representation of sentences in response to a given dialect. Despite the effectiveness of these solutions, the performance heavily relies on the amount of labeled examples, which is labor-intensive to attain and may not be readily available in real-world scenarios. To alleviate the burden of labeling data, this paper introduces a novel solution that leverages… More >

Abdel Aziz Osseni¹, Hubert Olivier Dossou-Yovo^2,*, Apollon D.M.T. Hegbe³, Muhammad Nauman Khan⁴, Brice Sinsin²

Revue Internationale de Géomatique, Vol.34, pp. 209-234, 2025, DOI:10.32604/rig.2025.061448 - 14 April 2025

Abstract Today, environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent sustainable decision tools. The Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs, habitats for wildlife conservation lack substantial investigations on the vegetation dynamics. Despite the protection measures they benefit from, these reserves remain dependent on climatic hazards that can influence their stability. The present study is innovative since it applied remote sensing techniques combined with climate records from the last thirty years to… More >

Majdi Rawashdeh^1,2,*, Muath A. Obaidat³, Meryem Abouali⁴, Dhia Eddine Salhi⁵, Kutub Thakur⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1129-1155, 2025, DOI:10.32604/cmes.2025.063765 - 11 April 2025

Abstract Cancer is a formidable and multifaceted disease driven by genetic aberrations and metabolic disruptions. Around 19% of cancer-related deaths worldwide are attributable to lung and colon cancer, which is also the top cause of death worldwide. The malignancy has a terrible 5-year survival rate of 19%. Early diagnosis is critical for improving treatment outcomes and survival rates. The study aims to create a computer-aided diagnosis (CAD) that accurately diagnoses lung disease by classifying histopathological images. It uses a publicly accessible dataset that includes 15,000 images of benign, malignant, and squamous cell carcinomas in the lung.… More >