Browsing by Author "Sanjeev, K.V."

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A survey on recurrent neural network architectures for sequential learning
(Springer Verlag service@springer.de, 2019) Prakash, B.; Sanjeev, K.V.; Prakash, R.; Chandrasekaran, K.
The expanding textual information and significance of examining the substance has started a colossal research in the field of synopsis. Text summarization is the process of conveying the gist of a text with a minimized representation. The requirement for automation of the procedure is at its apex with exponential burst of information because of digitization. Text captioning comes under the branch of abstractive summarization which captures the gist of the article in a few words. In this paper, we present an approach to text captioning using recurrent neural networks which comprise of an encoder–decoder model. The key challenges dealt here was to figure out the ideal input required to produce the desired output. The model performs better when the input is fed with the summary as compared to the original article itself. The recurrent neural network model with LSTM results has been effective in transcribing a caption for the textual data. © Springer Nature Singapore Pte Ltd. 2019
Analysis of stock prices and its associated volume of social network activity
(Springer Science and Business Media Deutschland GmbH, 2018) Kinnal, K.; Sanjeev, K.V.; Chandrasekaran, K.
Stock market prediction has been a convenient testing ground and a highly cited example for applying machine learning techniques to real-life scenarios. However, most of these problems using twitter feeds to analyze stock market prices make use of techniques such as sentimental analysis, mood scoring, financial behavioral analysis, and other such similar methods. In this paper, we propose to discover a correlation between the stock market prices and their associated twitter activity. It is always observed that whenever there are spikes in the stock market prices, there is a preceding twitter activity indicating the imminent spike in the aforementioned stock price. Our objective is to discover this existence of a correlation between the volumes of tweets observed when a market indices’ stock price spikes and the amount by which the stock price changes, with the help of machine learning techniques. If this correlation does exist, then an attempt is made to figure out a mathematical relationship between the two factors. © Springer Nature Singapore Pte Ltd. 2018.
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 oneâ€™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. Â© Springer Nature Singapore Pte Ltd. 2020.