TY - GEN
T1 - Python-based fuzzy classifier for cashew kernels
AU - Tomar, Snehal Singh
AU - Narendra, V. G.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85058993230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058993230&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-1592-3_28
DO - 10.1007/978-981-13-1592-3_28
M3 - Conference contribution
AN - SCOPUS:85058993230
SN - 9789811315916
T3 - Advances in Intelligent Systems and Computing
SP - 365
EP - 374
BT - Soft Computing for Problem Solving - SocProS 2017
A2 - Bansal, Jagdish Chand
A2 - Nagar, Atulya
A2 - Ojha, Akshay Kumar
A2 - Das, Kedar Nath
A2 - Deep, Kusum
PB - Springer Verlag
T2 - 7th International Conference on Soft Computing for Problem Solving, SocProS 2017
Y2 - 23 December 2017 through 24 December 2017
ER -