ANALYZING CONTINUOUS FUNCTIONS WITH FUZZY-BASED FOURIER TRANSFORM METHODS
DOI:
https://doi.org/10.29121/shodhkosh.v4.i1.2023.3626Keywords:
Fuzzy-based Fourier Transform, Traditional Fourier Transform, Noise Suppression, Uncertainty Handling, Signal Processing, Image Analysis, Pattern Recognition, Fuzzy Membership Functions, Computational Efficiency, Machine Learning, Artificial Intelligence, Real-World ApplicationsAbstract [English]
Five to six months for a problem to sleep and wake up but in the sequence of set and reset without any need only energy creation. This paper presents the fuzzy-based Fourier Transform (FFFT) to point out its superiority over the traditional Fourier Transform (TFT) for treatment of fuzzy data. And computational experiments in signal processing, image analysis, and pattern recognition confirm the robustness of the FFFT. Experimental results show notable enhancements on the noise suppression, edge sharpening, and features extraction performances, reflecting its versatility in areas like enhanced medical imaging, audio signal processing, and handwriting recognition. Even though alternative approaches face troubles of data analysis pain for computational overhead or with defining membership function for every attribute, be it FFFT proposes a manageable and reliable backdrop for studying uncertain datasets. Future works will focus on enhancing the membership functions, utilizing sophisticated mathematical formulations, and using FFFT in machine learning and AI tasks. Ultimately, this study demonstrates that FFFT is a potential powerful analytical tool in contemporary data analysis, as it combines fuzzy mathematics with Fourier analysis.
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