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    Author 2
    ORCID
    0000-0002-5884-8480
    Amin Sharafian
    Department of Mechatronics and Control Engineering, Shenzhen University, China
    Summary
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    2
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    Free Access | Review Article | 04 January 2025
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    by
    Hafiz Muhammad Zeeshan
    ,
    Inam Ullah
    ,
    Furkan Yousaf
    ,
    Amin Sharafian
    ,
    Md Belal Bin Heyat
    ,
    Shazia Saqib
    ,
    Atta Ur Rahman
    IECE Transactions on Intelligent Systematics | Volume 2, Issue 1: 38-48, 2025 | DOI:10.62762/TIS.2024.564569
    Abstract
    In the modern world, disseminating false information is a problem that must be addressed, and algorithms based on machine learning are used to spot and stop the spread of incorrect information. Due to the current unregulated development of false news fabrication and dissemination, democracy is continuously under threat. Fake news may mislead individuals while influencing them because of its persuasiveness and life sciences. Using data from the Web of Science, this study undertakes a bibliometric analysis of research on the application of machine learning for fake news identification. The research underscores the need for a streamlined approach to analyze data exclusively from the Web of Scie... More >
    Graphical Abstract
    A Machine Learning-Based Scientometric Evaluation for Fake News Detection
    Free Access | Research Article | 26 December 2024
    Adaptive Fuzzy Controller for Chaos Suppression in Nonlinear Fractional Order Systems
    by
    Amin Sharafian
    ,
    Islam Md Monirul
    ,
    Mohammad Jafar Mokarram
    ,
    Inam Ullah
    IECE Transactions on Emerging Trends in Network Systems | Volume 1, Issue 1: 5-18, 2024 | DOI:10.62762/TETNS.2024.318686
    Abstract
    This paper introduces a novel method for controlling a class of nonlinear non-affine systems with fractional-order dynamics, using an adaptive fuzzy technique. By incorporating a novel fractional update law in the design procedure, the controller can effectively suppress chaotic behaviour and smoothly track desired trajectories. The proposed method offers key advantages such as robustness against uncertainties, fast error convergence to the neighbourhood of zero, and satisfactory disturbance rejection performance. To demonstrate the capabilities of the proposed fractional controller, simulation results were conducted using Python on a fractional order Arneodo chaotic system. The results high... More >
    Graphical Abstract
    Adaptive Fuzzy Controller for Chaos Suppression in Nonlinear Fractional Order Systems