Efficient Subject-Oriented Evaluating and Mining Methods for Data with Schema Uncertainty

THE MODEL FOR THE ANALYSIS OF PHARMACOEPIDEMIOLOGICAL DATA BASED ON DATA MINING METHODS

The Journal of scientific articles Health and Education millennium ◽

10.26787/nydha-2226-7425-2017-19-10-289-298 ◽

2017 ◽

Vol 19 (10) ◽

pp. 289-298

Author(s):

I.M. Burykin ◽

◽

G.N. Aleeva ◽

R.Kh. Khafizianova ◽

◽

...

Keyword(s):

Data Mining ◽

Mining Methods

Download Full-text

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180322130242 ◽

2019 ◽

Vol 15 (8) ◽

pp. 769-776

Author(s):

Kalaivani Subramani ◽

Shantharajah Periyasamy ◽

Padma Theagarajan

Keyword(s):

Pattern Matching ◽

Error Rate ◽

Median Filter ◽

Identification System ◽

Double Line ◽

Cultivation System ◽

Matching Algorithm ◽

Non Local ◽

Mining Methods ◽

Blight Disease

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.

Download Full-text