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 >

Anees Ara¹, Muhammad Mujahid¹, Amal Al-Rasheed^2,*, Shaha Al-Otaibi², Tanzila Saba¹

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2717-2731, 2025, DOI:10.32604/cmc.2025.065566 - 03 July 2025

Abstract DeepSeek Chinese artificial intelligence (AI) open-source model, has gained a lot of attention due to its economical training and efficient inference. DeepSeek, a model trained on large-scale reinforcement learning without supervised fine-tuning as a preliminary step, demonstrates remarkable reasoning capabilities of performing a wide range of tasks. DeepSeek is a prominent AI-driven chatbot that assists individuals in learning and enhances responses by generating insightful solutions to inquiries. Users possess divergent viewpoints regarding advanced models like DeepSeek, posting both their merits and shortcomings across several social media platforms. This research presents a new framework for predicting… 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 >

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

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 >

Jiqiang Chen, Ranran Han, Dongqing Zhang, Litao Ma^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5067-5082, 2024, DOI:10.32604/cmc.2024.051494 - 20 June 2024

Abstract Imbalanced datasets are common in practical applications, and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes. However, the creation of fuzzy rules typically depends on expert knowledge, which may not fully leverage the label information in training data and may be subjective. To address this issue, a novel fuzzy rule oversampling approach is developed based on the learning vector quantization (LVQ) algorithm. In this method, the label information of the training data is utilized to determine the antecedent… More >

S M Hasan Mahmud^1,2, Md Mamun Ali³, Mohammad Fahim Shahriar¹, Fahad Ahmed Al-Zahrani⁴, Kawsar Ahmed^5,6,*, Dip Nandi¹, Francis M. Bui⁵

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3933-3948, 2023, DOI:10.32604/cmc.2023.039020 - 08 October 2023

Abstract Alzheimer’s disease (AD) is a neurodevelopmental impairment that results in a person’s behavior, thinking, and memory loss. The most common symptoms of AD are losing memory and early aging. In addition to these, there are several serious impacts of AD. However, the impact of AD can be mitigated by early-stage detection though it cannot be cured permanently. Early-stage detection is the most challenging task for controlling and mitigating the impact of AD. The study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to… More >

Mengxiao Wang^1,2, Jing Huang^1,2,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1023-1042, 2023, DOI:10.32604/csse.2023.039569 - 26 May 2023

Abstract Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis… More >

Mamoona Humayun^1,*, Danish Javed², Nz Jhanjhi², Maram Fahaad Almufareh¹, Saleh Naif Almuayqil¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 575-591, 2023, DOI:10.32604/csse.2023.038765 - 26 May 2023

Abstract Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as… More >