Abstract
Fuzzy logic is a well-known branch of mathematics which provides a quantitative framework to discuss uncertain events and hence make logical estimations for uncertain outcomes. In this work, the key objective is to explore and illustrate the tools and techniques required to perform fuzzy operations and hence realize a basic fuzzy classifier in Python and assert its applicability over other conventional fuzzy logic tools such as the fuzzy logic toolbox in MATLAB. The above-mentioned classifier took real-world data of physical parameters such as length, width and thickness of white wholes cashew kernels which had highly overlapping data ranges as input and classified them into suitable categories. The observed computation time for successful (crisp) classification of the kernels into WW-320, WW-240, WW-210 and WW-180 categories using the said classifier was 0.43, 0.43, 0.42 and 0.46 s, respectively, whereas the fuzzy logic toolbox in MATLAB took minimum 0.58 s only to obtain a fuzzy output on the same computing system.
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Tomar, S.S., Narendra, V.G. (2019). Python-Based Fuzzy Classifier for Cashew Kernels. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_28
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DOI: https://doi.org/10.1007/978-981-13-1592-3_28
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