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Articles by K. Premalatha
Total Records ( 3 ) for K. Premalatha
  T. Keerthika and K. Premalatha
  Worldwide heart disease forecast has been a major research over the past decade since the major reason of death is due to heart disease. Numerous researchers combined fuzzy technique with some other technique for proficient classification purpose in order to predict the heart disease, since the fuzzy is proficient only if proper fuzzy rules are specified in the rule base. At this point, we have introduced a rough-fuzzy classifier that shared rough set theory with the fuzzy set. Generally, there are three main steps taken part in the rough-fuzzy classifier such as: rule generation using rough set theory, rule optimization using Artificial Bee Colony (ABC) and prediction using fuzzy classifier. At first, the discernability matrix is framing by the given database. Reduct and core analysis is used to recognize the relevant attributes from the discernability matrix after that fuzzy rules are generated from the rough set theory. After that the set of rule is optimized. Then, with the assist of fuzzy rules and membership functions, the fuzzy system is intended so that the prediction can be carried out within the fuzzy system intended. Finally, the experimentation is carried out by means of the Cleveland, Hungarian and Switzerland datasets. From the results, we ensure that the proposed rough-fuzzy classifier outperformed the previous approach by achieving the accuracy of 87% in Hungarian and 80% in Switzerland datasets.
  M. Anidha and K. Premalatha
  MicroRNAs are small non-coding RNA molecules which are important developments in the cancer biology. miRNA microarrays are useful tools to identify potential biomarkers for variety of cancers. Due to high dimensionality of microarrays, it is very hard to identify cancer oncogenes and classify tumor samples. Feature selection is very essential task in the process of classification and identification of biomarker genes by selecting relevant genes. In this research, Entropy Based Mean Score (EBMS) is employed to identify the biomarker genes in miRNA microarrays. This is based on Fisher score which has the benefits of information gain and achieves maximum classification accuracy. The proposed research is tested on benchmark datasets with SVM and ANN for classification. The experimental results show that the EBMS method outperforms the existing methods and it is suitable for effective feature selection.
  K. Premalatha and A.M. Natarajan
  Information Retrieval (IR) is the discipline of searching for documents, for information within documents and metadata about documents. The document clustering improves the retrieval effectiveness of the IR System. If documents can be clustered together in a sensible order, then indexing and retrieval operations can be optimized. This study presents a review on document clustering.
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