Informatics Publishing Limited

D. Rajasekhara Reddy ¹, V. S. S. Prasad ¹, V. Malathi ¹, K. S. N. Reddy ¹, D. D. Varma ¹

Affiliations
1 Department of Geology, Andhra University, Visakhapatnam - 530 003, IN

Abstract

The economic potentiality of the heavy minerals in the beaches between Baruva and Bavanapadu extending for about 45 km was examined. In the sub-surface sediments, the heavy minerals were studied at an interval of 1 m up to a maximum depth of 5.8m. In general the concentration of heavy minerals is high in dunes followed by backshore and foreshore regions. Heavy mineral content increases from surface to sub-surface in dunes, decreases in foreshore and does not vary much in backshore. The heavy minerals include mainly ilmenite, garnet, sillimanite and ortho-pyroxenes with minor amounts of amphiboles, zircon, monazite, rutile etc. Majority of the heavies such as ilmenite, monazite, zircon etc. are concentrated in finer fractions while some of the heavies like garnet and sillimanite are concentrated in coarser fractions. The inferred reserves estimated for the area indicate its economical potential.

Keywords

Heavy Minerals, Dunes, Backshore, Foreshore, Economic Potential.

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J. Suganthi ¹, V. Malathi ²

Affiliations
1 Department of CSE, Raja College of Engineering and Technology, Madurai - 625020, Tamil Nadu, IN
2 Anna University Regional Centre, Madurai - 625020, Tamil Nadu, IN

Abstract

Objectives: A reliable and precise classification of tumor types is of great importance and essential for successful Diagnosis and Drug Discovery. Methods: Gene expression profiling has shown a great prospective in the outcome prediction of different types of Cancers, with the innovation of Microarray Technology. Microarray cancer data, which have been organized as samples versus genes fashion, are being exploited for the tissue sample classification. They are also useful for identifying potential gene markers in each subtype of cancer, that helps to analyze a particular cancer type in a successful manner. Findings: In this paper a new method for classification based on Fuzzy Rough-Set Feature Selection Approach through Transductive SVM Technique, which is called as FFS+TSVM is proposed. Moreover recently, for Cancer Pattern Classification, a combined Consistency based Feature Selection Approach through Transductive Support Vector Machine (CBFS+TSVM) has proposed and its prediction accuracy has encouraged than that of existing other Classifiers. However, from the literature survey, it is revealed that the performance of the existing scheme can be improved if Fuzzy Rough Set Approach for Gene Feature Selection with Transductive Support Vector Machine is employed. The present work is implemented with the help of Bio Weka and studied thoroughly in terms of Computational Cost, Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy. Applications/Improvements: The proposed work outperforms the existing Transductive Support Vector Machine (TSVM) in terms of Dimensionality Reduction, Threshold, Classification Error and Classification Accuracy for various Cancer Patterns.

Keywords

Fuzzy Rough Sets, Gene Selection, Information Measures, Low-Density Separation (LDS), Microarray Analysis, Semisupervised Classification, Support Vector Machines (SVM)

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