Fuzzy classification for strawberry diseases-infection using machine vision and soft-computing techniques

Author(s):  
Hamit Altıparmak ◽  
Mohamad Al Shahadat ◽  
Ehsan Kiani ◽  
Kamil Dimililer
2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Toly Chen ◽  
Richard Romanowski

This study proposes a slack-diversifying fuzzy-neural rule to improve job dispatching in a wafer fabrication factory. Several soft computing techniques, including fuzzy classification and artificial neural network prediction, have been applied in the proposed methodology. A highly effective fuzzy-neural approach is applied to estimate the remaining cycle time of a job. This research presents empirical evidence of the relationship between the estimation accuracy and the scheduling performance. Because dynamic maximization of the standard deviation of schedule slack has been shown to improve performance, this work applies such maximization to a slack-diversifying fuzzy-neural rule derived from a two-factor tailored nonlinear fluctuation smoothing rule for mean cycle time (2f-TNFSMCT). The effectiveness of the proposed rule was checked with a simulated case, which provided evidence of the rule’s effectiveness. The findings in this research point to several directions that can be exploited in the future.


2015 ◽  
Vol 81 (5-8) ◽  
pp. 771-778 ◽  
Author(s):  
Pascual Noradino Montes Dorantes ◽  
Marco Aurelio Jiménez Gómez ◽  
Gerardo Maximiliano Méndez ◽  
Juan Pablo Nieto González ◽  
Jesús de la Rosa Elizondo

Author(s):  
Binoy B Nair ◽  
S Silamparasu ◽  
R Mohnish ◽  
T S Deepak ◽  
M Rahul

Author(s):  
Mohammad K. Ayoubloo ◽  
Hazi Md. Azamathulla ◽  
Zulfequar Ahmad ◽  
Aminuddin Ab. Ghani ◽  
Javad Mahjoobi ◽  
...  

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