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Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
TLDR
Fuzzy rules and defuzzification: rules based on experience learning from examples decision tree approach neural network approach minimization of fuzzy rulesdefuzzification and optimization applications concluding remarks.
Tensor Decomposition of Gait Dynamics in Parkinson's Disease
  • T. Pham, Hong Yan
  • Medicine
    IEEE Transactions on Biomedical Engineering
  • 1 August 2018
TLDR
Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease and effective features for machine learning that can provide early prediction of the disease progression.
Spectral estimation techniques for DNA sequence and microarray data analysis
TLDR
The advantages of the autoregressive (AR) model for the identification of protein coding regions and the detection of DNA repeats are demonstrated.
Map image segmentation based on thresholding and fuzzy rules
TLDR
A technique for map image segmentation based on thresholding and fuzzy rules is presented that outperforms the commonly used adaptive thresholding method.
Blood cell image segmentation based on the Hough transform and fuzzy curve tracing
TLDR
A method based on the Hough transform and fuzzy curve tracing is proposed that reduces the effects of noise and the uneven brightness within cells effectively and can even separate slightly overlapping cells.
Image Threshold Processing Based on Simulated Annealing and OTSU Method
This chapter analyzes Maximum between-Cluster Variance method to conduct image threshold, coming up with an optimizing searching method of image segmentation with simulated annealing optimization
ID3-derived fuzzy rules and optimized defuzzification for handwritten numeral recognition
  • Z. Chi, Hong Yan
  • Computer Science
    IEEE Trans. Fuzzy Syst.
  • 1 February 1996
TLDR
The technique overcomes the difficulties in a conventional syntactic approach to handwritten character recognition, including problems of choosing a starting or reference point, scaling, and learning by machines, and out-performs the straight forward ID3 approach.
A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy
TLDR
A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy is presented in this paper and experimental results show that the proposed method spends less time to reach the better thresholds in edge similarity than existing multileVEL thresholding methods.
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