Zhenfu Zhang¹, Haiyan Yin², Liudong Zuo³, Pan Lai^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 919-936, 2025, DOI:10.32604/cmc.2025.062980 - 09 June 2025

Abstract The knapsack problem is a classical combinatorial optimization problem widely encountered in areas such as logistics, resource allocation, and portfolio optimization. Traditional methods, including dynamic programming (DP) and greedy algorithms, have been effective in solving small problem instances but often struggle with scalability and efficiency as the problem size increases. DP, for instance, has exponential time complexity and can become computationally prohibitive for large problem instances. On the other hand, greedy algorithms offer faster solutions but may not always yield the optimal results, especially when the problem involves complex constraints or large numbers of items.… More >

Hui Luo, Wenqing Li^*, Wei Zeng

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1567-1580, 2025, DOI:10.32604/cmc.2025.062949 - 09 June 2025

Abstract Rail surface damage is a critical component of high-speed railway infrastructure, directly affecting train operational stability and safety. Existing methods face limitations in accuracy and speed for small-sample, multi-category, and multi-scale target segmentation tasks. To address these challenges, this paper proposes Pyramid-MixNet, an intelligent segmentation model for high-speed rail surface damage, leveraging dataset construction and expansion alongside a feature pyramid-based encoder-decoder network with multi-attention mechanisms. The encoding network integrates Spatial Reduction Masked Multi-Head Attention (SRMMHA) to enhance global feature extraction while reducing trainable parameters. The decoding network incorporates Mix-Attention (MA), enabling multi-scale structural understanding and More >

Junfeng Lin¹, Jialin Ma^1,*, Wei Chen^1,2, Hao Wang¹, Weiguo Ding¹, Mingyao Tang¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1013-1029, 2025, DOI:10.32604/cmc.2025.062895 - 09 June 2025

Abstract The cross-modal person re-identification task aims to match visible and infrared images of the same individual. The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods. Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities. However, these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features, resulting in limited correspondence accuracy and suboptimal matching performance. To address this issue, we propose a quadratic graph matching method… More >

Lingyun Xiang¹, Hang Fu¹, Chunfang Yang^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 325-345, 2025, DOI:10.32604/cmc.2025.062834 - 09 June 2025

Abstract In the Visual Place Recognition (VPR) task, existing research has leveraged large-scale pre-trained models to improve the performance of place recognition. However, when there are significant environmental differences between query images and reference images, a large number of ineffective local features will interfere with the extraction of key landmark features, leading to the retrieval of visually similar but geographically different images. To address this perceptual aliasing problem caused by environmental condition changes, we propose a novel Visual Place Recognition method with Cross-Environment Robust Feature Enhancement (CerfeVPR). This method uses the GAN network to generate similar… More >

Yinuo Chen¹, Xu Wu¹, Jiaxin Fan¹, Guangyu Zhu^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1597-1613, 2025, DOI:10.32604/cmc.2025.062779 - 09 June 2025

Abstract The consultation intention of emergency decision-makers in urban rail transit (URT) is input into the emergency knowledge base in the form of domain questions to obtain emergency decision support services. This approach facilitates the rapid collection of complete knowledge and rules to form effective decisions. However, the current structured degree of the URT emergency knowledge base remains low, and the domain questions lack labeled datasets, resulting in a large deviation between the consultation outcomes and the intended objectives. To address this issue, this paper proposes a question intention recognition model for the URT emergency domain,… More >

Yan Cao^#, Zhizhuang Zhou^#, Yan Zhuang^*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1545-1565, 2025, DOI:10.32604/cmc.2025.062743 - 09 June 2025

Abstract Code obfuscation is a crucial technique for protecting software against reverse engineering and security attacks. Among various obfuscation methods, opaque predicates, which are recognized as flexible and promising, are widely used to increase control-flow complexity. However, traditional opaque predicates are increasingly vulnerable to Dynamic Symbolic Execution (DSE) attacks, which can efficiently identify and eliminate them. To address this issue, this paper proposes a novel approach for anti-DSE opaque predicates that effectively resists symbolic execution-based deobfuscation. Our method introduces two key techniques: single-way function opaque predicates, which leverage hash functions and logarithmic transformations to prevent constraint More >

Qing Xie^*, Ruiyun Yu

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1123-1144, 2025, DOI:10.32604/cmc.2025.062712 - 09 June 2025

Abstract Although existing style transfer techniques have made significant progress in the field of image generation, there are still some challenges in the field of exhibition hall design. The existing style transfer methods mainly focus on the transformation of single dimensional features, but ignore the deep integration of content and style features in exhibition hall design. In addition, existing methods are deficient in detail retention, especially in accurately capturing and reproducing local textures and details while preserving the content image structure. In addition, point-based attention mechanisms tend to ignore the complexity and diversity of image features… More >

Ronglei Hu¹, Chuce He^1,2, Sihui Liu¹, Dong Yao¹, Xiuying Li¹, Xiaoyi Duan^1,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 937-956, 2025, DOI:10.32604/cmc.2025.062542 - 09 June 2025

Abstract Ciphertext data retrieval in cloud databases suffers from some critical limitations, such as inadequate security measures, disorganized key management practices, and insufficient retrieval access control capabilities. To address these problems, this paper proposes an enhanced Fully Homomorphic Encryption (FHE) algorithm based on an improved DGHV algorithm, coupled with an optimized ciphertext retrieval scheme. Our specific contributions are outlined as follows: First, we employ an authorization code to verify the user’s retrieval authority and perform hierarchical access control on cloud storage data. Second, a triple-key encryption mechanism, which separates the data encryption key, retrieval authorization key, More >

Anand Deva Durai Chelladurai¹, Theena Jemima Jebaseeli², Omar Alqahtani¹, Prasanalakshmi Balaji^1,*, Jeniffer John Simon Christopher³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1101-1122, 2025, DOI:10.32604/cmc.2025.062278 - 09 June 2025

Abstract Optical Coherence Tomography (OCT) provides cross-sectional and three-dimensional reconstructions of the target tissue, allowing precise imaging and quantitative analysis of individual retinal layers. These images, based on optical inhomogeneities, reveal intricate cellular structures and are vital for tasks like retinal segmentation. The proposed study uses OCT images to identify significant differences in peripapillary retinal nerve fiber layer thickness. Incorporating spectral-domain analysis of OCT images significantly enhances the evaluation of Purtcher Retinopathy. To streamline this process, the study introduces a Context Encoding Deep Neural Network (CEDNN), which eliminates the time-consuming manual segmentation process while improving the… More >

Hussam Qushtom, Ahmad Hasasneh^*, Sari Masri

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1379-1395, 2025, DOI:10.32604/cmc.2025.061995 - 09 June 2025

Abstract This study presents an enhanced convolutional neural network (CNN) model integrated with Explainable Artificial Intelligence (XAI) techniques for accurate prediction and interpretation of wheat crop diseases. The aim is to streamline the detection process while offering transparent insights into the model’s decision-making to support effective disease management. To evaluate the model, a dataset was collected from wheat fields in Kotli, Azad Kashmir, Pakistan, and tested across multiple data splits. The proposed model demonstrates improved stability, faster convergence, and higher classification accuracy. The results show significant improvements in prediction accuracy and stability compared to prior works,… More >