Informatics Publishing Limited

V. Sivaraj ¹, S. Sukumaran ¹

Affiliations
1 Department of Computer Science, Erode Arts and Science College, Erode - 639 009, IN

Abstract

The aim of this work is to develop a new technique for medical classification problem. In diagnosing cancer, multicategory classification of cancer plays a very significant role. Nowadays, number of cancer suffering is increasing, so effective technique is required. In this work, an Extreme Learning Machine is integrated with the Successive Feature Selection technique for better classification in cancer. The extreme learning machine will rectify the problems such as improper learning rate, local minima, and low speed. This new method is evaluated for using accuracy and execution time and proves that the proposed method is very effective.

Keywords

Cancer Classification, Successive Feature Selection, Extreme Learning Machine, Gene Expression Data.

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V. Sivaraj ¹, T. Karthikeyan ²

Affiliations
1 CiiT Academic Research Lab, Coimbatore, IN
2 PSGCAS, Coimbatore, IN

Abstract

The CLUster-based rEtrieval(CLUE), groups the image based on the similarity measure, so that there is maximum similarity with in the cluster and minimum similarity between the two cluster and then retrieve the images related to the query. The cluster based retrieval of images tackles the semantic gap problem. The Content-Based Image Retrieval (CBIR) extract the feature of the images and the images with maximum similarity with that of the query is retrieved. This paper makes use of both the concept to retrieve the images. The CBIR system-using CLUE is called as Content-Based Image Clusters Retrieval (CBICR). The keyword-based retrieval along with the CBIR system retrieves the relevant images more effectively and it consumes less amount of time. The keyword based retrieval is done and the Nearest Neighbor Method is used to locate neighbor of the target image. The N-cut algorithm is used to organize the cluster.

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V. Sivaraj ¹, S. Sukumaran ¹

Affiliations
1 Department of Computer Science, Erode Arts and Science College, Erode-639009, IN

Abstract

Cancer is caused by uncontrolled and abnormal cells and it may spread through the blood stream or lymphatic system to further parts of the body. To explore the possibilities for classification of cancer, the researchers are started to perform using gene expression data. But still there are a lot of issues which is to be solved. So, this work introduced the fast PSO with ELM technique for cancer classification problems. This work implemented fast PSO method for multicategory classification of cancer cells. The PSO will provide the optimized output as the input to ELM. Evaluation is carried out for the proposed Fast PSO-ELM and the proposed approach achieves better classification accuracy.

Keywords

Cancer Classification, Gene Expression, ELM, Fast PSO-ELM.

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V. Sivaraj ¹, R. Pradheepa ¹

Affiliations
1 Sankara College of Science and Commerce, Coimbatore, IN

Abstract

One of the fundamental difficulties that arise in several fields, comprising pattern recognition, machine learning and statistics, is clustering. The basic data clustering problem might be defined as finding out groups in data or grouping related objects together. A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them. The major objective of clustering is to discover collection of comparable objects based on similarity metric. On the other hand, a similarity metric is generally specified by the user according to his requirements for obtaining better results. So far, there is no such technique available which absolutely fits for all applications. Some of the major difficulties concerning the existing available clustering approaches are that they do not concentrate on the entire needs effectively and require huge time complexity in case of clustering a great number of dimensions and bulky data sets. Efficiency of a particular clustering approach chiefly based on the definition of the distance, means that the measure of distance between the two objects in a particular cluster should be well defined using effective distance measures. Also it is necessary to know about the effect of constraints in clustering the objects. The use of constraints in clustering along with the effective distance measures will definitely provide better clustering results.

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