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  • Open Access

    ARTICLE

    Weak Fault Feature Extraction of the Rotating Machinery Using Flexible Analytic Wavelet Transform and Nonlinear Quantum Permutation Entropy

    Lili Bai1,*, Wenhui Li1, He Ren1,2, Feng Li1, Tao Yan1, Lirong Chen3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4513-4531, 2024, DOI:10.32604/cmc.2024.051348

    Abstract Addressing the challenges posed by the nonlinear and non-stationary vibrations in rotating machinery, where weak fault characteristic signals hinder accurate fault state representation, we propose a novel feature extraction method that combines the Flexible Analytic Wavelet Transform (FAWT) with Nonlinear Quantum Permutation Entropy. FAWT, leveraging fractional orders and arbitrary scaling and translation factors, exhibits superior translational invariance and adjustable fundamental oscillatory characteristics. This flexibility enables FAWT to provide well-suited wavelet shapes, effectively matching subtle fault components and avoiding performance degradation associated with fixed frequency partitioning and low-oscillation bases in detecting weak faults. In our approach,… More >

  • Open Access

    ARTICLE

    Transparent and Accountable Training Data Sharing in Decentralized Machine Learning Systems

    Siwan Noh1, Kyung-Hyune Rhee2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3805-3826, 2024, DOI:10.32604/cmc.2024.050949

    Abstract In Decentralized Machine Learning (DML) systems, system participants contribute their resources to assist others in developing machine learning solutions. Identifying malicious contributions in DML systems is challenging, which has led to the exploration of blockchain technology. Blockchain leverages its transparency and immutability to record the provenance and reliability of training data. However, storing massive datasets or implementing model evaluation processes on smart contracts incurs high computational costs. Additionally, current research on preventing malicious contributions in DML systems primarily focuses on protecting models from being exploited by workers who contribute incorrect or misleading data. However, less… More >

  • Open Access

    ARTICLE

    CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

    Fatma S. Alrayes1, Mohammed Zakariah2, Syed Umar Amin3,*, Zafar Iqbal Khan3, Jehad Saad Alqurni4

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586

    Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… More >

  • Open Access

    ARTICLE

    Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

    Yi-Chun Lai1, Shu-Yin Chiang2, Yao-Chiang Kan3, Hsueh-Chun Lin4,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3783-3803, 2024, DOI:10.32604/cmc.2024.050376

    Abstract Artificial intelligence (AI) technology has become integral in the realm of medicine and healthcare, particularly in human activity recognition (HAR) applications such as fitness and rehabilitation tracking. This study introduces a robust coupling analysis framework that integrates four AI-enabled models, combining both machine learning (ML) and deep learning (DL) approaches to evaluate their effectiveness in HAR. The analytical dataset comprises 561 features sourced from the UCI-HAR database, forming the foundation for training the models. Additionally, the MHEALTH database is employed to replicate the modeling process for comparative purposes, while inclusion of the WISDM database, renowned… More > Graphic Abstract

    Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

  • Open Access

    ARTICLE

    Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

    Jing Gao*, Mingxuan Ji, Hongjiang Wang, Zhongxiao Du

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5017-5030, 2024, DOI:10.32604/cmc.2024.050158

    Abstract With the continuous advancement of China’s “peak carbon dioxide emissions and Carbon Neutrality” process, the proportion of wind power is increasing. In the current research, aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data, a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine (IL-Bagging-DHKELM) error affinity propagation cluster analysis is proposed. The algorithm effectively combines deep hybrid kernel extreme learning machine (DHKELM) with incremental learning (IL). Firstly, an initial wind power prediction model is trained using the Bagging-DHKELM… More >

  • Open Access

    ARTICLE

    EG-STC: An Efficient Secure Two-Party Computation Scheme Based on Embedded GPU for Artificial Intelligence Systems

    Zhenjiang Dong1, Xin Ge1, Yuehua Huang1, Jiankuo Dong1, Jiang Xu2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4021-4044, 2024, DOI:10.32604/cmc.2024.049233

    Abstract This paper presents a comprehensive exploration into the integration of Internet of Things (IoT), big data analysis, cloud computing, and Artificial Intelligence (AI), which has led to an unprecedented era of connectivity. We delve into the emerging trend of machine learning on embedded devices, enabling tasks in resource-limited environments. However, the widespread adoption of machine learning raises significant privacy concerns, necessitating the development of privacy-preserving techniques. One such technique, secure multi-party computation (MPC), allows collaborative computations without exposing private inputs. Despite its potential, complex protocols and communication interactions hinder performance, especially on resource-constrained devices. Efforts… More >

  • Open Access

    ARTICLE

    Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

    Chen Chen1,2, Zhongwei Xu1, Meng Mei1,*, Kai Huang3, Siu Ming Lo2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4533-4549, 2024, DOI:10.32604/cmc.2024.048995

    Abstract Railway switch machine is essential for maintaining the safety and punctuality of train operations. A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein. Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring. Besides, a multi-sensor fusion tensor machine is designed to improve single signal data’s limitations in insufficient information. First, one-dimensional signal data is preprocessed and transformed into two-dimensional images. Afterward, the fusion feature tensor is created by More >

  • Open Access

    ARTICLE

    Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms

    Naif Al Mudawi*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967

    Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >

  • Open Access

    ARTICLE

    THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

    Monerah Alawadh*, Ahmed Barnawi

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4995-5015, 2024, DOI:10.32604/cmc.2024.048762

    Abstract Association rule learning (ARL) is a widely used technique for discovering relationships within datasets. However, it often generates excessive irrelevant or ambiguous rules. Therefore, post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors. Recently, several post-processing methods have been proposed, each with its own strengths and weaknesses. In this paper, we propose THAPE (Tunable Hybrid Associative Predictive Engine), which combines descriptive and predictive techniques. By leveraging both techniques, our aim is to enhance the quality of analyzing generated rules. This includes removing irrelevant… More >

  • Open Access

    ARTICLE

    Adaptive Cloud Intrusion Detection System Based on Pruned Exact Linear Time Technique

    Widad Elbakri1, Maheyzah Md. Siraj1,*, Bander Ali Saleh Al-rimy1, Sultan Noman Qasem2, Tawfik Al-Hadhrami3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3725-3756, 2024, DOI:10.32604/cmc.2024.048105

    Abstract Cloud computing environments, characterized by dynamic scaling, distributed architectures, and complex workloads, are increasingly targeted by malicious actors. These threats encompass unauthorized access, data breaches, denial-of-service attacks, and evolving malware variants. Traditional security solutions often struggle with the dynamic nature of cloud environments, highlighting the need for robust Adaptive Cloud Intrusion Detection Systems (CIDS). Existing adaptive CIDS solutions, while offering improved detection capabilities, often face limitations such as reliance on approximations for change point detection, hindering their precision in identifying anomalies. This can lead to missed attacks or an abundance of false alarms, impacting overall… More >

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