Informatics Publishing Limited

P. Thamilselvan ¹, J. G. R. Sathiaseelan ¹

Affiliations
1 Department of Computer Science, Bishop Heber College, Tiruchirappalli - 620017, Tamil Nadu, IN

Abstract

Objectives: To detect and classify the malignant cancer tissues and benign cancer tissues in MR lung cancer images by using k nearest neighbor mining algorithm. Methods/Statistical Analysis: In this paper, the Enhanced K Nearest Neighbor (EKNN) algorithm is executed to identify the lung cancer images. The k nearest neighbor technique is an important method of data mining algorithms. Findings: This work implicates four stages such as pre-processing, feature extraction, classification and detection of cancer tissues. In preprocessing stage, morphological process is used to filter the irrelevant noisy data in images. In the second phase, statistical and discriminator algorithm is used to extract the images. In the last stage, the improved k Nearest Neighbor (EKNN) method has been used to classify and identify the cancerous tissues in MRI images. The detection of cancer tissues and classification is done by executing four steps of Enhanced k Nearest Neighbor method which are measuring the Euclidean distance value, determining the k value, calculating the minimum distance and detecting the cancerous cells. Improvements/Applications: The experimental study with enhanced k nearest neighbor method shows better and promising classification result for classifying benign and malignant tissues.

Keywords

Geometrical and Statistical Properties, Image Classification, Image Mining, MRI Images, k Nearest Neighbor, Morphological Method.

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B. Ramesh ¹, J. G. R. Sathiaseelan ¹

Affiliations
1 Department of Computer Science, Bishop Heber College, IN

Abstract

Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find ischolar_main word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.

Keywords

Automatic Text Classification, ECAS Stemmer, Efficient Instance Selection and Pre-Computed Kernel Support Vector Machine.

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G. Srinaganya ¹, J. G. R. Sathiaseelan ¹

Affiliations
1 Department of Computer Science, Bishop Heber College – 620017, Tiruchirappalli, Tamil Nadu, IN

Abstract

Support Vector Machines (SVM) along with Hilltop associated techniques contain publicized headed for erect precise models other than the erudition chore usually requires a quadratic brainwashing, consequently the erudition chore for hefty datasets necessitates gigantic recall competence along with an elongated time. A up-to-the-minute augmentation, hilltop technique sloping SVM algorithm with linear or non-linear techniques in the proposition exertion in this article, which aims at classifying very hefty datasets on standard Page Rank for a website. The recent finite hilltop classifiers for building an incremental has extended based on SVM algorithm. The proposed algorithm has seemed to be very fast and could knob very hefty datasets in linear and non-linear classification chores. A case in point of the success is prearranged with the linear taxonomy into two modules of two million information positions in 50-measuremental contribution breathing space in various seconds with ECTRAA.

Keywords

Enhanced Candidate Transaction Rank Accuracy Algorithm, Hilltop, Page Rank, SVM Algorithm, Web Mining

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