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  • Open Access

    ARTICLE

    Comprehensive Analysis of Gender Classification Accuracy across Varied Geographic Regions through the Application of Deep Learning Algorithms to Speech Signals

    Abhishek Singhal*, Devendra Kumar Sharma

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 609-625, 2024, DOI:10.32604/csse.2023.046730

    Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference… More >

  • Open Access

    REVIEW

    Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease: Application and Development

    Yang Wang#, Xun Yang#, Mingtang Ye, Yuhang Zhao, Runsen Chen, Min Da, Zhiqi Wang, Xuming Mo, Jirong Qi*

    Congenital Heart Disease, Vol.19, No.2, pp. 219-231, 2024, DOI:10.32604/chd.2024.048314

    Abstract Congenital heart disease (CHD), the most prevalent congenital ailment, has seen advancements in the “dual indicator” screening program. This facilitates the early-stage diagnosis and treatment of children with CHD, subsequently enhancing their survival rates. While cardiac auscultation offers an objective reflection of cardiac abnormalities and function, its evaluation is significantly influenced by personal experience and external factors, rendering it susceptible to misdiagnosis and omission. In recent years, continuous progress in artificial intelligence (AI) has enabled the digital acquisition, storage, and analysis of heart sound signals, paving the way for intelligent CHD auscultation-assisted diagnostic technology. Although More > Graphic Abstract

    Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease: Application and Development

  • Open Access

    REVIEW

    A Review of NILM Applications with Machine Learning Approaches

    Maheesha Dhashantha Silva*, Qi Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2971-2989, 2024, DOI:10.32604/cmc.2024.051289

    Abstract In recent years, Non-Intrusive Load Monitoring (NILM) has become an emerging approach that provides affordable energy management solutions using aggregated load obtained from a single smart meter in the power grid. Furthermore, by integrating Machine Learning (ML), NILM can efficiently use electrical energy and offer less of a burden for the energy monitoring process. However, conducted research works have limitations for real-time implementation due to the practical issues. This paper aims to identify the contribution of ML approaches to developing a reliable Energy Management (EM) solution with NILM. Firstly, phases of the NILM are discussed,… More >

  • Open Access

    REVIEW

    Federated Learning on Internet of Things: Extensive and Systematic Review

    Meenakshi Aggarwal1, Vikas Khullar1, Sunita Rani2, Thomas André Prola3,4,5, Shyama Barna Bhattacharjee6, Sarowar Morshed Shawon7, Nitin Goyal8,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1795-1834, 2024, DOI:10.32604/cmc.2024.049846

    Abstract The proliferation of IoT devices requires innovative approaches to gaining insights while preserving privacy and resources amid unprecedented data generation. However, FL development for IoT is still in its infancy and needs to be explored in various areas to understand the key challenges for deployment in real-world scenarios. The paper systematically reviewed the available literature using the PRISMA guiding principle. The study aims to provide a detailed overview of the increasing use of FL in IoT networks, including the architecture and challenges. A systematic review approach is used to collect, categorize and analyze FL-IoT-based articles.… More >

  • Open Access

    ARTICLE

    Curve Classification Based on Mean-Variance Feature Weighting and Its Application

    Zewen Zhang1, Sheng Zhou1, Chunzheng Cao1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2465-2480, 2024, DOI:10.32604/cmc.2024.049605

    Abstract The classification of functional data has drawn much attention in recent years. The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy. In this paper, we propose a mean-variance-based (MV) feature weighting method for classifying functional data or functional curves. In the feature extraction stage, each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis. After that, a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the… More >

  • Open Access

    ARTICLE

    Design Pattern and Challenges of Federated Learning with Applications in Industrial Control System

    Hina Batool1, Jiuyun Xu1,*, Ateeq Ur Rehman2, Habib Hamam3,4,5,6

    Journal on Artificial Intelligence, Vol.6, pp. 105-128, 2024, DOI:10.32604/jai.2024.049912

    Abstract Federated Learning (FL) appeared as an encouraging approach for handling decentralized data. Creating a FL system needs both machine learning (ML) knowledge and thinking about how to design system software. Researchers have focused a lot on the ML side of FL, but have not paid enough attention to designing the software architecture. So, in this survey, a set of design patterns is described to tackle the design issues. Design patterns are like reusable solutions for common problems that come up when designing software architecture. This paper focuses on (1) design patterns such as architectures, frameworks,… More >

  • Open Access

    ARTICLE

    Validity, Reliability, and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

    Kamolthip Ruckwongpatr1,#, Chirawat Paratthakonkun2,#, Usanut Sangtongdee3,4,*, Iqbal Pramukti5, Ira Nurmala6, Kanokwan Angkasith7, Weena Thanachaisakul7, Jatuphum Ketchatturat8, Mark D. Griffiths9, Yi-Kai Kao10,*, Chung-Ying Lin1,5,11,12

    International Journal of Mental Health Promotion, Vol.26, No.4, pp. 293-302, 2024, DOI:10.32604/ijmhp.2024.047023

    Abstract Background: In recent years, there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs. However, there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand. The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale (SABAS) and Bergen Social Media Addiction Scale (BSMAS). Method: A total of 801 Thai university students participated in an online survey from January 2022 to More >

  • Open Access

    ARTICLE

    On Multi-Granulation Rough Sets with Its Applications

    Radwan Abu-Gdairi1, R. Mareay2,*, M. Badr3

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1025-1038, 2024, DOI:10.32604/cmc.2024.048647

    Abstract Recently, much interest has been given to multi-granulation rough sets (MGRS), and various types of MGRS models have been developed from different viewpoints. In this paper, we introduce two techniques for the classification of MGRS. Firstly, we generate multi-topologies from multi-relations defined in the universe. Hence, a novel approximation space is established by leveraging the underlying topological structure. The characteristics of the newly proposed approximation space are discussed. We introduce an algorithm for the reduction of multi-relations. Secondly, a new approach for the classification of MGRS based on neighborhood concepts is introduced. Finally, a real-life More >

  • Open Access

    ARTICLE

    A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

    Naglaa F. Soliman1, Fatma E. Fadl-Allah2, Walid El-Shafai3,4,*, Mahmoud I. Aly2, Maali Alabdulhafith1, Fathi E. Abd El-Samie1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 201-241, 2024, DOI:10.32604/cmc.2024.046757

    Abstract The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away from unauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and… More >

  • Open Access

    ARTICLE

    Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

    Xukai Ren1,2,*, Huanwei Yu2, Xianfeng Chen2, Yantong Tang2, Guobiao Wang1,*, Xiyong Du2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 647-663, 2024, DOI:10.32604/cmes.2024.048782

    Abstract Stirred reactors are key equipment in production, and unpredictable failures will result in significant economic losses and safety issues. Therefore, it is necessary to monitor its health state. To achieve this goal, in this study, five states of the stirred reactor were firstly preset: normal, shaft bending, blade eccentricity, bearing wear, and bolt looseness. Vibration signals along x, y and z axes were collected and analyzed in both the time domain and frequency domain. Secondly, 93 statistical features were extracted and evaluated by ReliefF, Maximal Information Coefficient (MIC) and XGBoost. The above evaluation results were More >

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