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Li Jiangping

Bio: Li Jiangping is an academic researcher from Guangdong University of Technology. The author has contributed to research in topics: Algorithm design & Rough set. The author has an hindex of 2, co-authored 8 publications receiving 9 citations.

Papers
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Proceedings ArticleDOI
04 Jul 2009
TL;DR: The algorithm avoided segmenting image excessively and speeded up segmentation velocity by fuzzy grid dividing and has been put into use in tongue image segmentation of Traditional Chinese Medicine (TCM).
Abstract: The paper studied a new theory of fuzzy rough sets, and presented a method to segment tongue image. It presented an algorithm of Fuzzy Rough Clustering Based on Grid by the theory. The algorithm extracts condensation points by the theory of fuzzy rough sets, and quarters the data space layer by layer, and softens the edge of the dense block by drawing condensation points in the borders. The algorithm has been put into use in tongue image segmentation of Traditional Chinese Medicine (TCM). The application result indicated: The algorithm avoided segmenting image excessively and speeded up segmentation velocity by fuzzy grid dividing.

4 citations

Proceedings ArticleDOI
19 May 2009
TL;DR: The algorithm improved speed, reliability and accuracy of TCM tongue diagnosis, also met the requirements of intellectualization and digitization.
Abstract: The paper studied a new theory of fuzzy rough sets, and presented a method to approximately estimate objects in a range. It presented an algorithm of Fuzzy Rough Clustering Based on Grid by the theory. The algorithm extracts condensation points by the theory of fuzzy rough sets, and quarters the data space layer by layer, and softens the edge of the dense block by drawing condensation points in the borders. The tongue diagnosis system is a big, complex one, its data is of great amount and the data cluster has uncertainty. The algorithm has been put into use in rules mining of Traditional Chinese Medicine (TCM) tongue diagnosis system. The application result indicated: The algorithm speeded up cluster by fuzzy grid dividing, saved a lot of time than traditional fuzzy cluster algorithm. The algorithm improved speed, reliability and accuracy of TCM tongue diagnosis, also met the requirements of intellectualization and digitization.

2 citations

Proceedings ArticleDOI
16 Jul 2008
TL;DR: The results of complex TCM diagnosis and inference process show that the intelligent inference model studied in this paper can preferably be applied to TCM differentiation of symptoms and signs.
Abstract: This paper studies an intelligent inference model embedded parallel competitive neural network, and implements the simulation of traditional Chinese medicine (TCM) experts in diagnosis and inference process, by integrating fuzzy neural networks (FNN) technology, image processing and recognition technology, data mining technology and extension thought. The results of complex TCM diagnosis and inference process show that the intelligent inference model studied in this paper can preferably be applied to TCM differentiation of symptoms and signs.

1 citations

Proceedings ArticleDOI
02 Jul 2008
TL;DR: The application result of the algorithm on complex traditional Chinese medicine (TCM) indicates: the algorithm can be applied well to process perplexing TCM clinical data.
Abstract: In this paper, the author introduces an expandable algorithm. This algorithm bases on the analysis of object and its datapsilas characteristics: such as similarity, complexity, invisibility (or visibility), degree of association, rate of appearance, dominant and recessive. By combine with fuzzy neural network and expandable thinking, this article analyses the incomplete information, complex data intelligent inference expandable algorithm. Also by embedding competitive neural networkpsilas computing model, the algorithm can be realized. The application result of the algorithm on complex traditional Chinese medicine (TCM) indicates: the algorithm can be applied well to process perplexing TCM clinical data.

1 citations

Proceedings ArticleDOI
14 Aug 2009
TL;DR: Improved FP-growth mining algorithm was presented, which was usable to extract the diagnosis rules in the Traditional Chinese Medicine tongue diagnosis, and applied it to the case database successfully.
Abstract: At the beginning, the paper analyzed the characteristics of clinical diseases database in the Traditional Chinese Medicine (TCM) tongue diagnosis. Secondly, the paper presented improved FP-growth mining algorithm, which was usable to extract the diagnosis rules. The algorithm was realized by using VC++ 7.0 and SQL Server 2000 as the development tools, and applied it to the case database successfully. The application result make clear that the algorithm can be accurate more and fitter.

1 citations


Cited by
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Journal ArticleDOI
01 Jul 2014
TL;DR: In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly to provide a stable and better framework forimage segmentation.
Abstract: In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

77 citations

Book ChapterDOI
01 Jan 2015
TL;DR: This chapter describes the state-of-the-art in the combinations of fuzzy and rough sets dividing into three parts.
Abstract: Fuzzy sets and rough sets are known as uncertainty models. They are proposed to treat different aspects of uncertainty. Therefore, it is natural to combine them to build more powerful mathematical tools for treating problems under uncertainty. In this chapter, we describe the state-of-the-art in the combinations of fuzzy and rough sets dividing into three parts.

16 citations

Journal ArticleDOI
TL;DR: A novel method is suggested, which applies multiobjective greedy rules and makes fusion of color and space information in order to extract tongue image accurately.
Abstract: Tongue image with coating is of important clinical diagnostic meaning, but traditional tongue image extraction method is not competent for extraction of tongue image with thick coating. In this paper, a novel method is suggested, which applies multiobjective greedy rules and makes fusion of color and space information in order to extract tongue image accurately. A comparative study of several contemporary tongue image extraction methods is also made from the aspects of accuracy and efficiency. As the experimental results show, geodesic active contour is quite slow and not accurate, the other 3 methods achieve fairly good segmentation results except in the case of the tongue with thick coating, our method achieves ideal segmentation results whatever types of tongue images are, and efficiency of our method is acceptable for the application of quantitative check of tongue image.

9 citations

Posted ContentDOI
03 Jul 2015-viXra
TL;DR: This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.
Abstract: Rough set theory is a new method that deals with vagueness and uncertainty emphasized in decision making. The theory provides a practical approach for extraction of valid rules fromdata.This paper discusses about rough sets and fuzzy rough sets with its applications in data mining that can handle uncertain and vague data so as to reach at meaningful conclusions.

8 citations