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Search Results (49)
  • Open Access

    ARTICLE

    Linguistic Steganography Based on Sentence Attribute Encoding

    Lingyun Xiang*, Xu He, Xi Zhang, Chengfu Ou

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2375-2389, 2025, DOI:10.32604/cmc.2025.065804 - 03 July 2025

    Abstract Linguistic steganography (LS) aims to embed secret information into normal natural text for covert communication. It includes modification-based (MLS) and generation-based (GLS) methods. MLS often relies on limited manual rules, resulting in low embedding capacity, while GLS achieves higher embedding capacity through automatic text generation but typically ignores extraction efficiency. To address this, we propose a sentence attribute encoding-based MLS method that enhances extraction efficiency while maintaining strong performance. The proposed method designs a lightweight semantic attribute analyzer to encode sentence attributes for embedding secret information. When the attribute values of the cover sentence differ… More >

  • Open Access

    ARTICLE

    Context Encoding Deep Neural Network Driven Spectral Domain 3D-Optical Coherence Tomography Imaging in Purtscher Retinopathy Diagnosis

    Anand Deva Durai Chelladurai1, Theena Jemima Jebaseeli2, Omar Alqahtani1, Prasanalakshmi Balaji1,*, 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 >

  • Open Access

    ARTICLE

    Multi-Scale Vision Transformer with Dynamic Multi-Loss Function for Medical Image Retrieval and Classification

    Omar Alqahtani, Mohamed Ghouse*, Asfia Sabahath, Omer Bin Hussain, Arshiya Begum

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2221-2244, 2025, DOI:10.32604/cmc.2025.061977 - 16 April 2025

    Abstract This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer (ViT) architectures and a dynamic multi-loss function. The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features, while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance. Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets, yielding notable improvements. Specifically, on the ISIC-2018 dataset, our method achieves an F1-Score improvement of +4.84% compared to the standard ViT, with a precision increase of +5.46% More >

  • Open Access

    ARTICLE

    GENOME: Genetic Encoding for Novel Optimization of Malware Detection and Classification in Edge Computing

    Sang-Hoon Choi1, Ki-Woong Park2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4021-4039, 2025, DOI:10.32604/cmc.2025.061267 - 06 March 2025

    Abstract The proliferation of Internet of Things (IoT) devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing. Nevertheless, the distributed nature of edge computing presents substantial security challenges, rendering it a prominent target for sophisticated malware attacks. Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources. This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation (GENOME) framework, a novel solution that is intended to improve the performance of malware detection and… More >

  • Open Access

    ARTICLE

    Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism

    Wei Liu1, Sen Liu2,3,*, Yinchao He2, Jiaojiao Wang1, Yu Gu1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4863-4880, 2025, DOI:10.32604/cmc.2025.059295 - 06 March 2025

    Abstract To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping… More >

  • Open Access

    ARTICLE

    Joint Feature Encoding and Task Alignment Mechanism for Emotion-Cause Pair Extraction

    Shi Li, Didi Sun*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1069-1086, 2025, DOI:10.32604/cmc.2024.057349 - 03 January 2025

    Abstract With the rapid expansion of social media, analyzing emotions and their causes in texts has gained significant importance. Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text, facilitating a deeper understanding of expressed sentiments and their underlying reasons. This comprehension is crucial for making informed strategic decisions in various business and societal contexts. However, recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneously model extracted features and their interactions, or inconsistencies in label prediction between emotion-cause pair extraction… More >

  • Open Access

    ARTICLE

    Efficient OpenMP Based Z-curve Encoding and Decoding Algorithms

    Zicheng Zhou1, Shaowen Sun2, Teng Liang3, Mengjuan Li4,*, Fengling Xia5,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1313-1327, 2025, DOI:10.32604/cmc.2024.056880 - 03 January 2025

    Abstract Z-curve’s encoding and decoding algorithms are primely important in many Z-curve-based applications. The bit interleaving algorithm is the current state-of-the-art algorithm for encoding and decoding Z-curve. Although simple, its efficiency is hindered by the step-by-step coordinate shifting and bitwise operations. To tackle this problem, we first propose the efficient encoding algorithm LTFe and the corresponding decoding algorithm LTFd, which adopt two optimization methods to boost the algorithm’s efficiency: 1) we design efficient lookup tables (LT) that convert encoding and decoding operations into table-lookup operations; 2) we design a bit detection mechanism that skips partial order More >

  • Open Access

    ARTICLE

    An Improved Distraction Behavior Detection Algorithm Based on YOLOv5

    Keke Zhou, Guoqiang Zheng*, Huihui Zhai, Xiangshuai Lv, Weizhen Zhang

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2571-2585, 2024, DOI:10.32604/cmc.2024.056863 - 18 November 2024

    Abstract Distracted driving remains a primary factor in traffic accidents and poses a significant obstacle to advancing driver assistance technologies. Improving the accuracy of distracted driving can greatly reduce the occurrence of traffic accidents, thereby providing a guarantee for the safety of drivers. However, detecting distracted driving behaviors remains challenging in real-world scenarios with complex backgrounds, varying target scales, and different resolutions. Addressing the low detection accuracy of existing vehicle distraction detection algorithms and considering practical application scenarios, this paper proposes an improved vehicle distraction detection algorithm based on YOLOv5. The algorithm integrates Attention-based Intra-scale Feature… More >

  • Open Access

    ARTICLE

    Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data

    Fahim Nasir1, Abdulghani Ali Ahmed1,*, Mehmet Sabir Kiraz1, Iryna Yevseyeva1, Mubarak Saif2

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1703-1728, 2024, DOI:10.32604/cmc.2024.055192 - 15 October 2024

    Abstract Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making. However, imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics, limiting their overall effectiveness. This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers (SLCs) and evaluates their performance in data-driven decision-making. The evaluation uses various metrics, with a particular focus on the Harmonic Mean Score (F-1 score) on an imbalanced real-world bank target marketing dataset. The findings indicate… More >

  • Open Access

    ARTICLE

    Cu Stress-Induced Transcriptome Alterations in Sorghum and Expression Analysis of the Transcription Factor-Encoding Gene SbWRKY24

    Mingchuan Yang1, Jia Zheng2, Wenhui Yu1, Yanghua Li2, Yali Wang1, Zilu Zhang1, Zhenhui Kang1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.7, pp. 1503-1521, 2024, DOI:10.32604/phyton.2024.051718 - 30 July 2024

    Abstract Sorghum is not only an important bio-energy crop but also a vital raw material for brewing. Exogenous copper affects the growth and metabolism of crops in specific ways. This study identified 8475 differentially expressed genes (DEGs) by high-throughput transcriptome sequencing in the sorghum cultivar ‘Jinnuoliang 2’ after 24 h of treatment with 10 mM CuSO. Using GO analysis, 476 genes were functionally annotated, which were mainly related to catabolism and biosynthetic processes. Additionally, 90 pathways were annotated by employing the KEGG analysis. Among them, glutathione metabolism and peroxisome were induced, while photosynthesis, photosynthesis-antenna protein, and… More >

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