Xuewen Mu^*, Bingcong Zhao

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2143-2159, 2025, DOI:10.32604/cmc.2025.060739 - 16 April 2025

Abstract When dealing with imbalanced datasets, the traditional support vector machine (SVM) tends to produce a classification hyperplane that is biased towards the majority class, which exhibits poor robustness. This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets. The proposed method first uses a biased second-order cone programming support vector machine (B-SOCP-SVM) to identify the support vectors (SVs) and non-support vectors (NSVs) in the imbalanced data. Then, it applies the synthetic minority over-sampling technique (SV-SMOTE) to oversample the support vectors of the minority class and uses the random under-sampling technique (NSV-RUS) multiple times More >

Jiming Lan¹, Bo Zeng^1,*, Suiqun Li¹, Weihan Zhang¹, Xinyi Shi²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2865-2888, 2025, DOI:10.32604/cmc.2025.060260 - 16 April 2025

Abstract The Quadric Error Metrics (QEM) algorithm is a widely used method for mesh simplification; however, it often struggles to preserve high-frequency geometric details, leading to the loss of salient features. To address this limitation, we propose the Salient Feature Sampling Points-based QEM (SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler (SFSP). This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification. Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details. Specifically, for general models… More >

Sumaiya Noor¹, Salman A. AlQahtani², Salman Khan^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1435-1450, 2025, DOI:10.32604/cmc.2025.061700 - 26 March 2025

Abstract The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion, metabolism, detoxification, and immunity. Liver diseases result from factors such as viral infections, obesity, alcohol consumption, injuries, or genetic predispositions. Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates. Traditionally, diagnosing liver diseases relied heavily on clinical expertise, often leading to subjective, challenging, and time-intensive processes. However, early detection is essential for effective intervention, and advancements in machine learning (ML) have demonstrated remarkable success in predicting various conditions, including Chronic Obstructive… More >

Xianfeng Zhou^1,2,*, Ruiju Sun¹, Zhaojie Zhang¹, Yuanyuan Song¹, Lijiao Jin¹, Lin Yuan³

Phyton-International Journal of Experimental Botany, Vol.94, No.2, pp. 503-519, 2025, DOI:10.32604/phyton.2025.060827 - 06 March 2025

Abstract An accurate and robust estimation of leaf chlorophyll content (LCC) is very important to better know the process of material and energy exchange between plants and the environment. Compared with traditional remote sensing methods, abundant research has made progress in agronomic parameter retrieval using different CNN frameworks. Nevertheless, limited reports have paid attention to the problems, i.e., limited measured data, hyperspectral redundancy, and model convergence issues, when concerning CNN models for parameter estimation. Therefore, the present study tried to analyze the effects of synthetic data size expansion employing a Gaussian process regression (GPR) model for… 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

Yuanjun Qu, Xiameng Si^*, Haiyan Kang, Hanlin Zhou

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3111-3130, 2025, DOI:10.32604/cmc.2024.057368 - 17 February 2025

Abstract With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq^2,*, Saifur Rahman³, Saad Arif⁴, Salim Nasar Faraj Mursal³, Muhammad Irfan³, Haris Aziz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >

Nada Mohammed Murad¹, Adnan Yousif Dawod², Saadaldeen Rashid Ahmed^3,4,*, Ravi Sekhar⁵, Pritesh Shah⁵

Intelligent Automation & Soft Computing, Vol.40, pp. 1-27, 2025, DOI:10.32604/iasc.2024.058624 - 10 January 2025

Abstract This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity, focusing on network intrusion detection systems (NIDS). The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing a more flexible and robust security architecture. We use seven unique machine learning models to improve detection skills, emphasizing data quality, traceability, and transparency, facilitated by a blockchain layer that safeguards against data modification and ensures auditability. Our technique employs the Synthetic Minority Oversampling Technique (SMOTE) to equilibrate the dataset, therefore mitigating prevalent class imbalance difficulties… More >

Dazhi E¹, Jiale Liu², Ming Zhang^1,*, Huiyuan Jiang², Keming Mao²

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3853-3880, 2024, DOI:10.32604/cmc.2024.057538 - 19 December 2024

Abstract Imbalance is a distinctive feature of many datasets, and how to make the dataset balanced become a hot topic in the machine learning field. The Synthetic Minority Oversampling Technique (SMOTE) is the classical method to solve this problem. Although much research has been conducted on SMOTE, there is still the problem of synthetic sample singularity. To solve the issues of class imbalance and diversity of generated samples, this paper proposes a hybrid resampling method for binary imbalanced data sets, RE-SMOTE, which is designed based on the improvements of two oversampling methods parameter-free SMOTE (PF-SMOTE) and… More >

Hongsheng Su, Shilin Song^*, Xingsheng Wang

Energy Engineering, Vol.121, No.11, pp. 3289-3303, 2024, DOI:10.32604/ee.2024.054643 - 21 October 2024

Abstract This paper introduces the Particle Swarm Optimization (PSO) algorithm to enhance the Latin Hypercube Sampling (LHS) process. The key objective is to mitigate the issues of lengthy computation times and low computational accuracy typically encountered when applying Monte Carlo Simulation (MCS) to LHS for probabilistic trend calculations. The PSO method optimizes sample distribution, enhances global search capabilities, and significantly boosts computational efficiency. To validate its effectiveness, the proposed method was applied to IEEE34 and IEEE-118 node systems containing wind power. The performance was then compared with Latin Hypercubic Important Sampling (LHIS), which integrates significant sampling More >