Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Jiajun Sun, Shunshun Ji, Chao Zhang^*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068668 - 09 December 2025

Abstract Asparagus stem blight is a devastating crop disease, and the early detection of its pathogenic spores is essential for effective disease control and prevention. However, spore detection is still hindered by complex backgrounds, small target sizes, and high annotation costs, which limit its practical application and widespread adoption. To address these issues, a semi-supervised spore detection framework is proposed for use under complex background conditions. Firstly, a difficulty perception scoring function is designed to quantify the detection difficulty of each image region. For regions with higher difficulty scores, a masking strategy is applied, while the… More >

Duong Tien Dung^1,2,3, Ha Hai Nam⁴, Nguyen Long Giang³, Luong Thi Hong Lan^5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5625-5642, 2025, DOI:10.32604/cmc.2025.069636 - 23 October 2025

Abstract Semi-supervised clustering techniques attempt to improve clustering accuracy by utilizing a limited number of labeled data for guidance. This method effectively integrates prior knowledge using pre-labeled data. While semi-supervised fuzzy clustering (SSFC) methods leverage limited labeled data to enhance accuracy, they remain highly susceptible to inappropriate or mislabeled prior knowledge, especially in noisy or overlapping datasets where cluster boundaries are ambiguous. To enhance the effectiveness of clustering algorithms, it is essential to leverage labeled data while ensuring the safety of the previous knowledge. Existing solutions, such as the Trusted Safe Semi-Supervised Fuzzy Clustering Method (TS3FCM),… More >

Duong Tien Dung^1,2,3, Ha Hai Nam⁴, Nguyen Long Giang³, Luong Thi Hong Lan^5,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5341-5357, 2025, DOI:10.32604/cmc.2025.068299 - 23 October 2025

Abstract Active semi-supervised fuzzy clustering integrates fuzzy clustering techniques with limited labeled data, guided by active learning, to enhance classification accuracy, particularly in complex and ambiguous datasets. Although several active semi-supervised fuzzy clustering methods have been developed previously, they typically face significant limitations, including high computational complexity, sensitivity to initial cluster centroids, and difficulties in accurately managing boundary clusters where data points often overlap among multiple clusters. This study introduces a novel Active Semi-Supervised Fuzzy Clustering algorithm specifically designed to identify, analyze, and correct misclassified boundary elements. By strategically utilizing labeled data through active learning, our More >

Jialong Zhang¹, Meijuan Yin^2,*, Yang Pei², Fenlin Liu², Chenyu Wang²

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2095-2115, 2025, DOI:10.32604/cmc.2025.065679 - 29 August 2025

Abstract Identifying influential users in social networks is of great significance in areas such as public opinion monitoring and commercial promotion. Existing identification methods based on Graph Neural Networks (GNNs) often lead to yield inaccurate features of influential users due to neighborhood aggregation, and require a large substantial amount of labeled data for training, making them difficult and challenging to apply in practice. To address this issue, we propose a semi-supervised contrastive learning method for identifying influential users. First, the proposed method constructs positive and negative samples for contrastive learning based on multiple node centrality metrics… More >

Habib Hadj-Mabrouk^*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4399-4430, 2025, DOI:10.32604/cmc.2025.067143 - 30 July 2025

Abstract A literature review on AI applications in the field of railway safety shows that the implemented approaches mainly concern the operational, maintenance, and feedback phases following railway incidents or accidents. These approaches exploit railway safety data once the transport system has received authorization for commissioning. However, railway standards and regulations require the development of a safety management system (SMS) from the specification and design phases of the railway system. This article proposes a new AI approach for analyzing and assessing safety from the specification and design phases of the railway system with a view to… More >

Won-Yang Cho, Sangjun Lee^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2847-2863, 2025, DOI:10.32604/cmc.2025.066373 - 03 July 2025

Abstract The classification of respiratory sounds is crucial in diagnosing and monitoring respiratory diseases. However, auscultation is highly subjective, making it challenging to analyze respiratory sounds accurately. Although deep learning has been increasingly applied to this task, most existing approaches have primarily relied on supervised learning. Since supervised learning requires large amounts of labeled data, recent studies have explored self-supervised and semi-supervised methods to overcome this limitation. However, these approaches have largely assumed a closed-set setting, where the classes present in the unlabeled data are considered identical to those in the labeled data. In contrast, this… More >

Mingen Zhong¹, Kaibo Yang^1,*, Ziji Xiao¹, Jiawei Tan², Kang Fan², Zhiying Deng¹, Mengli Zhou¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1055-1071, 2025, DOI:10.32604/cmc.2025.063383 - 09 June 2025

Abstract With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video… More >

Di Wu^*, Liming Feng, Xiaoyu Wang

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 977-999, 2025, DOI:10.32604/cmc.2025.060319 - 26 March 2025

Abstract Semi-supervised new intent discovery is a significant research focus in natural language understanding. To address the limitations of current semi-supervised training data and the underutilization of implicit information, a Semi-supervised New Intent Discovery for Elastic Neighborhood Syntactic Elimination and Fusion model (SNID-ENSEF) is proposed. Syntactic elimination contrast learning leverages verb-dominant syntactic features, systematically replacing specific words to enhance data diversity. The radius of the positive sample neighborhood is elastically adjusted to eliminate invalid samples and improve training efficiency. A neighborhood sample fusion strategy, based on sample distribution patterns, dynamically adjusts neighborhood size and fuses sample More >

Kun Liu¹, Chen Bao^1,*, Sidong Liu²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025

Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >