Songsong Zhang¹, Huazhong Jin^1,2,*, Zhiwei Ye^1,2, Jia Yang^1,2, Jixin Zhang^1,2, Dongfang Wu^1,2, Xiao Zheng^1,2, Dingfeng Song¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-19, 2026, DOI:10.32604/cmc.2025.068044 - 10 November 2025

Abstract Multi-label feature selection (MFS) is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels. However, traditional centralized methods face significant challenges in privacy-sensitive and distributed settings, often neglecting label dependencies and suffering from low computational efficiency. To address these issues, we introduce a novel framework, Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization (DHBCPSO-MSR). Leveraging the federated learning paradigm, Fed-MFSDHBCPSO allows clients to perform local feature selection (FS) using DHBCPSO-MSR. Locally selected feature subsets are encrypted with differential privacy (DP) and transmitted… More >

Candan Tumer¹, Erdal Guvenoglu², Volkan Tunali^3,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5135-5158, 2025, DOI:10.32604/cmc.2025.067840 - 23 October 2025

Abstract Automated cartoon character recognition is crucial for applications in content indexing, filtering, and copyright protection, yet it faces a significant challenge in animated media due to high intra-class visual variability, where characters frequently alter their appearance. To address this problem, we introduce the novel Kral Sakir dataset, a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions. This paper conducts a comprehensive benchmark study, evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks (CNNs), including DenseNet, ResNet, and VGG, against a custom baseline model trained More >

Wei Ou^1,2,3, Shuai Liu^1,*, Mengxue Pang¹, Jianqiang Ma¹, Qiuling Yue¹, Wenbao Han¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 463-490, 2025, DOI:10.32604/cmc.2025.063862 - 09 June 2025

Abstract TarGuess-I is a leading model utilizing Personally Identifiable Information for online targeted password guessing. Due to its remarkable guessing performance, the model has drawn considerable attention in password security research. However, through an analysis of the vulnerable behavior of users when constructing passwords by combining popular passwords with their Personally Identifiable Information, we identified that the model fails to consider popular passwords and frequent substrings, and it uses overly broad personal information categories, with extensive duplicate statistics. To address these issues, we propose an improved password guessing model, TGI-FPR, which incorporates three semantic methods: (1) More >

Chih-Ta Yen^1,*, Jung-Ren Wong², Chia-Hsang Chang²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 347-363, 2025, DOI:10.32604/cmc.2025.063389 - 09 June 2025

Abstract In its 2023 global health statistics, the World Health Organization noted that noncommunicable diseases (NCDs) remain the leading cause of disease burden worldwide, with cardiovascular diseases (CVDs) resulting in more deaths than the three other major NCDs combined. In this study, we developed a method that can comprehensively detect which CVDs are present in a patient. Specifically, we propose a multi-label classification method that utilizes photoplethysmography (PPG) signals and physiological characteristics from public datasets to classify four types of CVDs and related conditions: hypertension, diabetes, cerebral infarction, and cerebrovascular disease. Our approach to multi-disease classification… More >

Faheem Shaukat¹, Naveed Ejaz^1,2, Rashid Kamal^3,4, Tamim Alkhalifah^5,*, Sheraz Aslam^6,7,*, Mu Mu⁴

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5595-5622, 2025, DOI:10.32604/cmc.2025.061702 - 19 May 2025

Abstract Automated and accurate movie genre classification is crucial for content organization, recommendation systems, and audience targeting in the film industry. Although most existing approaches focus on audiovisual features such as trailers and posters, the text-based classification remains underexplored despite its accessibility and semantic richness. This paper introduces the Genre Attention Model (GAM), a deep learning architecture that integrates transformer models with a hierarchical attention mechanism to extract and leverage contextual information from movie plots for multi-label genre classification. In order to assess its effectiveness, we assess multiple transformer-based models, including Bidirectional Encoder Representations from Transformers… More >

Jian Luo¹, Yaling Li¹, Xueyu Li¹, Xuliang Hu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 877-896, 2025, DOI:10.32604/cmc.2025.061401 - 26 March 2025

Abstract Sarcasm detection in Natural Language Processing (NLP) has become increasingly important, particularly with the rise of social media and non-textual emotional expressions, such as images. Existing methods often rely on separate image and text modalities, which may not fully utilize the information available from both sources. To address this limitation, we propose a novel multimodal large model, i.e., the PKME-MLM (Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection). The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images, which is then combined… More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >

Mingzhu Tang¹, Yujie Huang¹, Dongxu Ji², Hao Yu^2,*

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 95-129, 2025, DOI:10.32604/fhmt.2025.061143 - 26 February 2025

Abstract Load deviations between the output of ultra-supercritical (USC) coal-fired power units and automatic generation control (AGC) commands can adversely affect the safe and stable operation of these units and grid load dispatching. Data-driven diagnostic methods often fail to account for the imbalanced distribution of data samples, leading to reduced classification performance in diagnosing load deviations in USC units. To address the class imbalance issue in USC load deviation datasets, this study proposes a diagnostic method based on the multi-label natural neighbor boundary oversampling technique (MLNaNBDOS). The method is articulated in three phases. Initially, the traditional… More > Graphic Abstract

Ting Cai¹, Chun Ye¹, Zhiwei Ye^1,*, Ziyuan Chen¹, Mengqing Mei¹, Haichao Zhang¹, Wanfang Bai², Peng Zhang³

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1157-1175, 2024, DOI:10.32604/cmc.2024.055080 - 15 October 2024

Abstract The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging. Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features. The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection, because of its simplicity, efficiency, and similarity to reinforcement learning. Nevertheless, existing methods do not consider crucial correlation information, such as dynamic redundancy and label correlation. To tackle these concerns, the paper proposes a More >

P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373 - 13 September 2024

Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >