Informatics Publishing Limited

B. Nagarajan ¹, P. Balasubramanie ²

Affiliations
1 Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Kongu Engineering College, Perundurai, Tamilnadu, IN

Abstract

Object classification under partially cluttered background is a difficult task in still images. The challenging task in this problem is the classification of objects invariant to size and pose with partially cluttered environment containing natural scenes. This paper addresses the issues to classify sample objects from caltech-101 standard database containing airplanes and motorbikes. The threshold technique with background subtraction is used to segment the background region to extract the object of interest. The background segmented image with region of interest is divided into equal sized blocks of sub-images. The statistical features are extracted from each sub-block. The features of the objects are fed to the back-propagation neural classifier. Thus the performance of the neural classifier is compared with various categories of block size. Quantitative evaluation out perform the previous work in the literature with an improved results of 92.4% due to absence of occlusions. A critical evaluation of our approach under the proposed is presented.

Keywords

Background Segmentation, Neural Classifier, Object Classification, Statistical Features.

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E. K. Vellingiriraj ¹, P. Balasubramanie ¹

Affiliations
1 Department of C.S.E., Kongu Engineering College, Perundurai, Tamilnadu, IN

Abstract

The objective of this research is to develop computer software that can recognize the Ancient Tamil handwritten characters by using the genetic algorithm technique (RATHCPM). The system consists of 5 main modules, which are: 1) image acquisition module, 2) image preprocessing module, 3) feature extraction module, 4) character recognition module, and 5) display result module. Each module has the following details. First, the image acquisition module collects an unknown input character from a user. Second, the input image is transformed into a suitable image for the feature extraction module. Third, the system extracts character features from the image. There are 3 main features of Tamil characters which are stroke, loop and location of loop and stroke connection. Fourth, the extracted character information is kept in the form of bits string chromosome in a genetic algorithm. Finally, the system displays the best fitness chromosome for the recognition result.

Keywords

RATHCPM, Vowels and Consonant.

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S. Poorani ¹, P. Balasubramanie ², D. Vimal Kumar ³

Affiliations
1 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
2 Department of Computer Technology, Kongu Engineering College, Perundurai, IN
3 Department of Computer Science, Nehru Arts and Science College, Coimbatore, IN

Abstract

Unsupervised learning algorithms are major Data mining techniques that can be used for clinical data analysis. Asthma is a constant inflammatory disease of the respiratory channels in which the reason for its prevalence is not clear. Its dominance is rising all over the world. Clustering techniques can be used to identify the hidden disease characteristics that may assist in the treatment and to create awareness about the disease. This paper implements the k-means clustering algorithm to identify the asthma endotypes and related ischolar_main causes from the epidemiological data that was collected through questionnaire from asthma patients.

Keywords

Data Mining, Clustering, Partition Clustering, Clinical Data Analysis, Asthma, Endotypes, K-Means.

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