Browsing by Author "Prakash, B.S."

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    A comparison of linear discriminant analysis and ridge classifier on Twitter data
    (Institute of Electrical and Electronics Engineers Inc., 2017) Singh, A.; Prakash, B.S.; Chandrasekaran, K.
    This document is about the accuracy analysis of two of the most prominent classifiers present in today's academic arena. Classifiers are being used extensively in machine learning applications today and need to present a high rate of success to be considered useful. Tikhonov regularization incorporated within the Ridge Classifier is the basis for its classification. It utilises the LevenbergMarquardt algorithm for non-linear least-squares problems to classify objects. Linear Discriminant Analysis, on the other hand, utilises aspects of ANOVA[2,3] and regression analysis. LDA works by getting explicit information from the user. It needs the definition of the variables - both dependent and independent. It doesn't use any implicit assumptions in its modelling. There is no interconnection between the two variables initially. Using these two classifiers we compare their effectiveness at mapping a set of data scraped in real-time from Twitter to its corresponding generalised hashtag, and suggest why the differences, if any, arise. © 2016 IEEE.
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    A comparison of linear discriminant analysis and ridge classifier on Twitter data
    (2017) Singh, A.; Prakash, B.S.; Chandrasekaran, K.
    This document is about the accuracy analysis of two of the most prominent classifiers present in today's academic arena. Classifiers are being used extensively in machine learning applications today and need to present a high rate of success to be considered useful. Tikhonov regularization incorporated within the Ridge Classifier is the basis for its classification. It utilises the LevenbergMarquardt algorithm for non-linear least-squares problems to classify objects. Linear Discriminant Analysis, on the other hand, utilises aspects of ANOVA[2,3] and regression analysis. LDA works by getting explicit information from the user. It needs the definition of the variables - both dependent and independent. It doesn't use any implicit assumptions in its modelling. There is no interconnection between the two variables initially. Using these two classifiers we compare their effectiveness at mapping a set of data scraped in real-time from Twitter to its corresponding generalised hashtag, and suggest why the differences, if any, arise. � 2016 IEEE.
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    Review of techniques for automatic text summarization
    (Springer, 2020) Prakash, B.S.; Sanjeev, K.V.; Prakash, R.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.V.
    Summarization refers to the process of reducing the textual components such as words and sentences but conveying most of the information in the input text. Research in summarization is very prominent in the current scenario where the textual data available is enormous and contains valuable information. People have been interested in summarization since time immemorial. The methods adopted in the past relied on manually reading the text and based on one’s understanding of the text, manually generating the summary. In the current world, due to the explosion of data from Internet and social media, the manual process is very tedious and time-consuming. As a result, there is a great need to automate the process of summarization. In this paper, we summarize most of the researches in the field of summarization which is unique and path-breaking. © Springer Nature Singapore Pte Ltd. 2020.