Advances in Information Mining

1-4

Authors: Reddy P.V.N., Satya Prasad K

In this paper a retrieval study for natural image learning environment is proposed. The content of this paper is a result of projects called Image content-based retrieval on a natural image database. The objective of this project is to develop an image content-based search engine, which can perform identity check of a natural image. It is well known that conventional natural image databases can only be retrieved by text-based query. In this paper we use the shape, color, and other features extracted from a captured natural image to search the natural image database. The developed technique is able to perform scale, translation, and rotation invariant matching between natural images. Currently, the database contains several hundreds of natural images. In future, we shall enhance the capability of the search engine to deal with more than 30,000 natural image species, which is the total amount of natural image species along the coast.

11-13

Authors: Ajmire P.E., Warkhede S.E

The work in this paper is deals with the recognition of handwritten Marathi vowels. Marathi is an Indo-Aryan language spoken by about 71 million people mainly in the Indian state of Maharashtra and neighbouring states. Marathi is also spoken in Israel and Mauritius. Marathi is thought to be a descendent of Maharashtri, one of the Prakrit languages which developed from Sanskrit. This work is based on invariant moments for recognition of isolated Marathi Handwritten Characters and their divisions. The proposed technique is independent of size, slant, orientation, translation and other variations in handwritten characters. Handwritten Marathi Characters/Numerals are more complex for recognition than corresponding English characters due to many possible variations in order, number, direction and shape of the constituent strokes. The work treats isolated Characters as an image of 40X40 pixel size. Seven invariant central moments of the image and two additional feature sets are evaluated. 10 samples of each number from 20 different writers have been sampled and prepared a database has been made. Seven central invariant moments are evaluated for each character and its parts by dividing it by two different ways. In all, there are 14 features corresponding to each character. The Gaussian Distribution Function has been adopted for classification. The average success rate of some vowels is compatible.

1-7

Authors: Lohgaonkar M.H., Bajaj V.H., Jadhav V.A., Patwari M.B

The aim of this paper is to present a fuzzy multi-objective multi-index transportation problem and develop multi-objective multi-index fuzzy programming model. This model cannot only satisfy more of the actual requirements of the integral system but is also more flexible than conventional transportation problems. Furthermore, it can offer more information to the decision maker (DM) for reference, and then it can raise the quality for decision-making. This paper, we use a special type of linear and non-linear membership functions to solve the multi-objective multi-index transportation problem. It gives an optimal compromise solution.

13-17

Authors: Sankar K., Krishnamoorthy K

Ant-based techniques, in the computer sciences, are designed those who take biological inspirations on the behavior of these social insects. Data clustering techniques are classification algorithms that have a wide range of applications, from Biology to Image processing and Data presentation. Since real life ants do perform clustering and sorting of objects among their many activities, we expect that an study of ant colonies can provide new insights for clustering techniques. The aim of clustering is to separate a set of data points into self-similar groups such that the points that belong to the same group are more similar than the points belonging to different groups. Each group is called a cluster. Data may be clustered using an iterative version of the Fuzzy C means (FCM) algorithm, but the draw back of FCM algorithm is that it is very sensitive to cluster center initialization because the search is based on the hill climbing heuristic. The ant based algorithm provides a relevant partition of data without any knowledge of the initial cluster centers. In the past researchers have used ant based algorithms which are based on stochastic principles coupled with the k-means algorithm. The proposed system in this work use the Fuzzy C means algorithm as the deterministic algorithm for ant optimization. The proposed model is used after reformulation and the partitions obtained from the ant based algorithm were better optimized than those from randomly initialized hard C Means. The proposed technique executes the ant fuzzy in parallel for multiple clusters. This would enhance the speed and accuracy of cluster formation for the required system problem.