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  • Open Access

    ARTICLE

    Multi-Scale Feature Fusion Network for Accurate Detection of Cervical Abnormal Cells

    Chuanyun Xu1,#, Die Hu1,#, Yang Zhang1,*, Shuaiye Huang1, Yisha Sun1, Gang Li2

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 559-574, 2025, DOI:10.32604/cmc.2025.061579 - 26 March 2025

    Abstract Detecting abnormal cervical cells is crucial for early identification and timely treatment of cervical cancer. However, this task is challenging due to the morphological similarities between abnormal and normal cells and the significant variations in cell size. Pathologists often refer to surrounding cells to identify abnormalities. To emulate this slide examination behavior, this study proposes a Multi-Scale Feature Fusion Network (MSFF-Net) for detecting cervical abnormal cells. MSFF-Net employs a Cross-Scale Pooling Model (CSPM) to effectively capture diverse features and contextual information, ranging from local details to the overall structure. Additionally, a Multi-Scale Fusion Attention (MSFA)… More >

  • Open Access

    ARTICLE

    A Generative Image Steganography Based on Disentangled Attribute Feature Transformation and Invertible Mapping Rule

    Xiang Zhang1,2,*, Shenyan Han1,2, Wenbin Huang1,2, Daoyong Fu1,2

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1149-1171, 2025, DOI:10.32604/cmc.2025.060876 - 26 March 2025

    Abstract Generative image steganography is a technique that directly generates stego images from secret information. Unlike traditional methods, it theoretically resists steganalysis because there is no cover image. Currently, the existing generative image steganography methods generally have good steganography performance, but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction. Therefore, this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule. Firstly, the reference image is disentangled by a content and an attribute encoder to obtain content features… More >

  • Open Access

    ARTICLE

    Amalgamation of Classical and Large Language Models for Duplicate Bug Detection: A Comparative Study

    Sai Venkata Akhil Ammu1, Sukhjit Singh Sehra1,*, Sumeet Kaur Sehra2, Jaiteg Singh3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 435-453, 2025, DOI:10.32604/cmc.2025.057792 - 26 March 2025

    Abstract Duplicate bug reporting is a critical problem in the software repositories’ mining area. Duplicate bug reports can lead to redundant efforts, wasted resources, and delayed software releases. Thus, their accurate identification is essential for streamlining the bug triage process mining area. Several researchers have explored classical information retrieval, natural language processing, text and data mining, and machine learning approaches. The emergence of large language models (LLMs) (ChatGPT and Huggingface) has presented a new line of models for semantic textual similarity (STS). Although LLMs have shown remarkable advancements, there remains a need for longitudinal studies to… More >

  • Open Access

    ARTICLE

    Efficient Spatiotemporal Information Utilization for Video Camouflaged Object Detection

    Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4319-4338, 2025, DOI:10.32604/cmc.2025.060653 - 06 March 2025

    Abstract Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time More >

  • Open Access

    ARTICLE

    DIEONet: Domain-Invariant Information Extraction and Optimization Network for Visual Place Recognition

    Shaoqi Hou1,2,3,*, Zebang Qin2, Chenyu Wu2, Guangqiang Yin2, Xinzhong Wang1, Zhiguo Wang2,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5019-5033, 2025, DOI:10.32604/cmc.2025.058233 - 06 March 2025

    Abstract Visual Place Recognition (VPR) technology aims to use visual information to judge the location of agents, which plays an irreplaceable role in tasks such as loop closure detection and relocation. It is well known that previous VPR algorithms emphasize the extraction and integration of general image features, while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks. To this end, this paper proposes a Domain-invariant Information Extraction and Optimization Network (DIEONet) for VPR. The core of the algorithm is a newly designed Domain-invariant Information Mining Module (DIMM)… More >

  • Open Access

    ARTICLE

    kProtoClust: Towards Adaptive k-Prototype Clustering without Known k

    Yuan Ping1,2,*, Huina Li1, Chun Guo3, Bin Hao4

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4949-4976, 2025, DOI:10.32604/cmc.2025.057693 - 06 March 2025

    Abstract Towards optimal k-prototype discovery, k-means-like algorithms give us inspirations of central samples collection, yet the unstable seed samples selection, the hypothesis of a circle-like pattern, and the unknown K are still challenges, particularly for non-predetermined data patterns. We propose an adaptive k-prototype clustering method (kProtoClust) which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging. On behalf of a group of data samples, support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes, while inner samples become the first candidates for… More >

  • Open Access

    ARTICLE

    Identifying Suitable Sites for CSP Plants Using AHP, Fuzzy AHP, and Full Consistency Method: A Case Study of CHAD

    Bernard Bayangbe1,*, Ababacar Thiam1,2, El hadji I. Cissé2, Kory Faye1

    Energy Engineering, Vol.122, No.3, pp. 943-969, 2025, DOI:10.32604/ee.2025.060273 - 07 March 2025

    Abstract Concentrating Solar Power (CSP) is one of the most promising solar technologies for sustainable power generation in countries with high solar potential, like Chad. Identifying suitable sites is of great importance for deploying solar power plants. This work focuses on the identification of potential sites for the installation of solar power plants in Chad as well as a comparative analysis using the Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy Process (FAHP), and Full Consistency Method (FUCOM). The results show that 35% of the Chadian territory, i.e., an area of 449,400 km2, is compatible with the implementation… More >

  • Open Access

    ARTICLE

    Practical Adversarial Attacks Imperceptible to Humans in Visual Recognition

    Donghyeok Park1, Sumin Yeon2, Hyeon Seo2, Seok-Jun Buu2, Suwon Lee2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2725-2737, 2025, DOI:10.32604/cmes.2025.061732 - 03 March 2025

    Abstract Recent research on adversarial attacks has primarily focused on white-box attack techniques, with limited exploration of black-box attack methods. Furthermore, in many black-box research scenarios, it is assumed that the output label and probability distribution can be observed without imposing any constraints on the number of attack attempts. Unfortunately, this disregard for the real-world practicality of attacks, particularly their potential for human detectability, has left a gap in the research landscape. Considering these limitations, our study focuses on using a similar color attack method, assuming access only to the output label, limiting the number of More >

  • Open Access

    REVIEW

    Progress on Multi-Field Coupling Simulation Methods in Deep Strata Rock Breaking Analysis

    Baoping Zou1,2, Chenhao Pei1,*, Qizhi Chen1,2, Yansheng Deng1,2, Yongguo Chen1,2, Xu Long3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2457-2485, 2025, DOI:10.32604/cmes.2025.061429 - 03 March 2025

    Abstract The utilization of multi-field coupling simulation methods has become a pivotal approach for the investigation of intricate fracture behavior and interaction mechanisms of rock masses in deep strata. The high temperatures, pressures and complex geological environments of deep strata frequently result in the coupling of multiple physical fields, including mechanical, thermal and hydraulic fields, during the fracturing of rocks. This review initially presents an overview of the coupling mechanisms of these physical fields, thereby elucidating the interaction processes of mechanical, thermal, and hydraulic fields within rock masses. Secondly, an in-depth analysis of multi-field coupling is… More >

  • Open Access

    ARTICLE

    Developing Different Models in QGIS for Determining Tourism Climate Comfort Using Remote Sensing and GIS

    Efdal Kaya*

    Revue Internationale de Géomatique, Vol.34, pp. 103-123, 2025, DOI:10.32604/rig.2025.060420 - 24 February 2025

    Abstract Global warming leads to climate change and hence effects tourism activities. Bioclimatic comfort indices are used to understand the changing climates of outdoor tourism. In this study, the models for the automatic calculation of the tourism climate index (TCI), heat index (HI), and new summer simmer index (NSSI) from bioclimatic comfort indices are used to determine the climatic conditions of outdoor tourism. The study compared the maps generated by the models with those manually created maps in ArcGIS. In order to statistically reveal how accurately the models produced maps, the relationship between the maps obtained… More >

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