Browsing by Author "Deepalakshmi R"

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    INFLUENCE OF SOIL FUNGI ON CORROSION OF MILD STEEL PLATES
    (NACE International, 2018) Dharani R; Deepalakshmi R; Padma Devi S N; NithyaMeenakshi S; Nalini D
    Metal corrosion is an electrochemical reaction between the environment and a metal, in which microbes are thought to play a very important role. These microorganisms do not only cause corrosion, but they can also inhibit or protect against corrosion. Fungi are the most dessicant–resistant microorganisms and are ubiquitous in atmospheric environments. About five fungal organisms were isolated using Starkey media from the soil of corroded pipeline tank. The influence of these fungal isolates on both rusted and non–rusted mild steel plates were studied for a period of 25 days. Among the five fungal isolates, Non–rusted Isolate (NR)–1 and Rusted Isolate (R)–3 showed minimum corrosion reaction on mild steel plates, based on the results of weight loss and dissolved iron content. The results revealed that the two isolates showed minimum rate of corrosion on mild steel plates due to the passive mechanism of …
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    MACHINE LEARNING APPROACH FOR TAXATION ANALYSIS USING CLASSIFICATION TECHNIQUES.
    (International Journal of Computer Applications, 2011-01) Deepalakshmi R; Radha N
    Data mining process discovers useful information from the hidden data, which can be used for future prediction. Machine learning provides methods, techniques and tools, which help to learn automatically and to make accurate predictions based on past observations. The data are retrieved from the real time environmental setup. Machine learning techniques can help in the integration of computer-based systems in predicting the dataset and to improve the efficiency of the system. The main purpose of this paper is to provide a comparison of some commonly employed classification algorithms under same conditions. Such comparison helps to provide the accurate result in algorithms. Hence comparing the algorithms for such a classifier is a tedious task, for real time dataset. The classification models were experimented by using 365 datasets with 24 attributes. The predicted values for the classifiers were evaluated and the results were compared