Browsing by Author "Kumari, R."

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Study of wear behaviour of ductile iron subjected to two step austempering
(2010) Kumari, R.; Prasad Rao, P.P.
An investigation was carried out to examine the influence of two step austempering on microstructural parameters and the wear behaviour of austempered ductile iron. Ductile iron was austenitised at 900 °C for 30 min, and then austempered successively at two different temperatures. It was first austempered at 300 °C for different durations from 2 min to 30 min and subsequently austempered at 400°C for 2 h, after which it was quenched to room temperature. Resulting microstructures were characterised through optical microscopy and X-ray diffraction. Mechanical properties were studied through hardness measurement and tensile testing. Wear studies were carried out using a pin-on-disc machine. Wear rate was found to decrease with increasing time at the first step temperature of 300 °C. At short austempering times at 300 °C, the amount of austenite was instrumental in improving the wear resistance through formation of deformation induced martensite. Wear rate was found to depend on yield strength, austenite content and its carbon content. © Carl Hanser Verlag GmbH & Co. KG.
Time series with sentiment analysis for stock price prediction
(2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.
Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. � 2019 IEEE.
Time series with sentiment analysis for stock price prediction
(Institute of Electrical and Electronics Engineers Inc., 2019) Sharma, V.; Khemnar, R.; Kumari, R.; Mohan, B.R.
Stock price prediction has been a major area of research for many years. Accurate predictions can help investors take correct decisions about the selling/purchase of stocks. This paper aims to predict and gauge stock costs and patterns, utilizing the power of machine learning, content examination and fundamental analysis, to give traders a hands-on tool for keen speculations particularly for the volatile Indian Stock Market. We propose a technique to analyze and predict the stock price with the help of sentiment analysis and decomposable time series model along with multivariate-linear regression. Â© 2019 IEEE.