Soft computing and intelligent systems design: theory, tools and applications, F. O. Karry and C. De Silva, Pearson, Addison‐Wesley, New York, NY, 2004

Naidu, D. Subbaram

doi:10.1002/rnc.1072

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Fuzzy knowledge base or rule base in fuzzy inference system is a set of linguistic descriptions [ 31 ]. Fuzzy rule base plays a key role in fuzzy inference system as it makes conclusions related to either classifying an input or stabilizing and adjusting the output.…”

Section: Proposed Methodologymentioning

confidence: 99%

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

et al. 2015

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Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.

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Section: Proposed Methodologymentioning

confidence: 99%

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

et al. 2015

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Comparative evaluation of quality properties of chickpea seed (Cicer arietinum L.) grown under different irrigation conditions by using machine learning

Varol

Çetin

Kırnak

2022

J Agr Sci-Tarim Bili

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Chickpea is an important edible legume consumed worldwide because of rich nutrient composition. The physical parameters of chickpea are crucial attributes for design of processing and classification systems. In this study, effects of seven different irrigation treatments on size, shape, mass, and color properties of chickpea seeds were investigated, and machine learning algorithms were used to estimate mass and color attributes of chickpea seeds. The results showed that Multilayer Perceptron (MLP) had the greatest correlation coefficients for mass (0.9997) and chroma (0.9997). The MLP yielded better outcomes than Random Forest for both mass and color estimation. In terms of physical attributes, the best results were obtained in I1 (rainfed) and I5 (irrigation at 50% flowering and 50% pod fill) irrigation treatments. Additionally, single or couple irrigations at different physiological stages instead of full irrigation treatment might be sufficient to improve the physical attributes of chickpea.

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Soft computing and intelligent systems design: theory, tools and applications, F. O. Karry and C. De Silva, Pearson, Addison‐Wesley, New York, NY, 2004

Cited by 2 publications

References 18 publications

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

Comparative evaluation of quality properties of chickpea seed (Cicer arietinum L.) grown under different irrigation conditions by using machine learning

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