Yafei Liu^1,2, Yuling Chen^1,2,*, Xuewei Wang³, Yuxiang Yang², Chaoyue Tan²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069152 - 10 November 2025

Abstract As blockchain technology rapidly evolves, smart contracts have seen widespread adoption in financial transactions and beyond. However, the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems. Although numerous detection techniques have been proposed, existing methods suffer from significant limitations, such as class imbalance and insufficient modeling of transaction-related semantic features. To address these challenges, this paper proposes an oversampling-based detection framework for Ponzi smart contracts. We enhance the Adaptive Synthetic Sampling (ADASYN) algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions. This enhancement facilitates the… More >

Zheng Zhang^1,2, Jie Hao², Liquan Chen^1,*, Tianhao Hou², Yanan Liu²

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068372 - 10 November 2025

Abstract With the increasing severity of network security threats, Network Intrusion Detection (NID) has become a key technology to ensure network security. To address the problem of low detection rate of traditional intrusion detection models, this paper proposes a Dual-Attention model for NID, which combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to design two modules: the FocusConV and the TempoNet module. The FocusConV module, which automatically adjusts and weights CNN extracted local features, focuses on local features that are more important for intrusion detection. The TempoNet module focuses on global information, identifies… More >

Lei Han^1,*, Wanyu Ye¹, Chunfang Liu², Shihua Huang¹, Chun Chen³, Luxin Zhan³, Siyuan Liang³

Energy Engineering, Vol.122, No.12, pp. 4947-4969, 2025, DOI:10.32604/ee.2025.069040 - 27 November 2025

Abstract In contemporary medium-voltage distribution networks heavily penetrated by distributed energy resources (DERs), the harmonic components injected by power-electronic interfacing converters, together with the inherently intermittent output of renewable generation, distort the zero-sequence current and continuously reshape its frequency spectrum. As a result, single-line-to-ground (SLG) faults exhibit a pronounced, strongly non-stationary behaviour that varies with operating point, load mix and DER dispatch. Under such circumstances the performance of traditional rule-based algorithms—or methods that rely solely on steady-state frequency-domain indicators—degrades sharply, and they no longer satisfy the accuracy and universality required by practical protection systems. To overcome… More >

Xiaofang Jin, Yiran Li^*, Yuying Yang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5309-5326, 2025, DOI:10.32604/cmc.2025.067812 - 23 October 2025

Abstract Given the importance of sentiment analysis in diverse environments, various methods are used for image sentiment analysis, including contextual sentiment analysis that utilizes character and scene relationships. However, most existing works employ character faces in conjunction with context, yet lack the capacity to analyze the emotions of characters in unconstrained environments, such as when their faces are obscured or blurred. Accordingly, this article presents the Adaptive Multi-Channel Sentiment Analysis Network (AMSA), a contextual image sentiment analysis framework, which consists of three channels: body, face, and context. AMSA employs Multi-task Cascaded Convolutional Networks (MTCNN) to detect More >

Fatma S. Alrayes¹, Mohammed Zakariah^2,*, Mohammed K. Alzaylaee³, Syed Umar Amin⁴, Zafar Iqbal Khan⁴

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3457-3484, 2025, DOI:10.32604/cmc.2025.068599 - 23 September 2025

Abstract Healthcare networks prove to be an urgent issue in terms of intrusion detection due to the critical consequences of cyber threats and the extreme sensitivity of medical information. The proposed Auto-Stack ID in the study is a stacked ensemble of encoder-enhanced auctions that can be used to improve intrusion detection in healthcare networks. The WUSTL-EHMS 2020 dataset trains and evaluates the model, constituting an imbalanced class distribution (87.46% normal traffic and 12.53% intrusion attacks). To address this imbalance, the study balances the effect of training Bias through Stratified K-fold cross-validation (K = 5), so that… More >

Lulu Zhang, Xuehui Du^*, Wenjuan Wang, Yu Cao, Xiangyu Wu, Shihao Wang

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1901-1919, 2025, DOI:10.32604/cmc.2025.064270 - 09 June 2025

Abstract To address the limitations of existing abnormal traffic detection methods, such as insufficient temporal and spatial feature extraction, high false positive rate (FPR), poor generalization, and class imbalance, this study proposed an intelligent detection method that combines a Stacked Convolutional Network (SCN), Bidirectional Long Short-Term Memory (BiLSTM) network, and Equalization Loss v2 (EQL v2). This method was divided into two components: a feature extraction model and a classification and detection model. First, SCN was constructed by combining a Convolutional Neural Network (CNN) with a Depthwise Separable Convolution (DSC) network to capture the abstract spatial features More >

I Made Putrama^1,2,*, Péter Martinek¹

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5699-5727, 2025, DOI:10.32604/cmc.2025.063465 - 19 May 2025

Abstract Imbalanced multiclass datasets pose challenges for machine learning algorithms. They often contain minority classes that are important for accurate predictions. However, when the data is sparsely distributed and overlaps with data points from other classes, it introduces noise. As a result, existing resampling methods may fail to preserve the original data patterns, further disrupting data quality and reducing model performance. This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling (NDESO), a hybrid method that integrates a data displacement strategy with a resampling technique to achieve data balance. It begins by computing the average distance of noisy… More >

Yuanqing Li¹, Mengyao Dai¹, Sanfeng Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 309-333, 2025, DOI:10.32604/cmc.2025.060455 - 26 March 2025

Abstract Social media has significantly accelerated the rapid dissemination of information, but it also boosts propagation of fake news, posing serious challenges to public awareness and social stability. In real-world contexts, the volume of trustable information far exceeds that of rumors, resulting in a class imbalance that leads models to prioritize the majority class during training. This focus diminishes the model’s ability to recognize minority class samples. Furthermore, models may experience overfitting when encountering these minority samples, further compromising their generalization capabilities. Unlike node-level classification tasks, fake news detection in social networks operates on graph-level samples,… 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 >

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