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Babita Majhi
Researcher at Guru Ghasidas University
Publications - 100
Citations - 1938
Babita Majhi is an academic researcher from Guru Ghasidas University. The author has contributed to research in topics: Artificial neural network & Particle swarm optimization. The author has an hindex of 20, co-authored 91 publications receiving 1513 citations. Previous affiliations of Babita Majhi include Siksha O Anusandhan University & National Institute of Technology, Rourkela.
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Proceedings ArticleDOI
Sentiment analysis of Twitter data for predicting stock market movements
TL;DR: A strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets, and this work has applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyzed the correlation between stock market movements of a company and sentiments in tweet.
Journal ArticleDOI
IIR system identification using cat swarm optimization
TL;DR: The IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model.
Journal ArticleDOI
Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques
TL;DR: It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.
Posted Content
Sentiment Analysis of Twitter Data for Predicting Stock Market Movements
TL;DR: In this paper, the authors applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyzed the correlation between stock market movements of a company and sentiments in tweets.
Journal ArticleDOI
Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training
TL;DR: Comparisons of different performance measures including the training time of the all three evolutionary computing based models demonstrate that the proposed ARMA-DE exchange rate prediction model possesses superior short and long range prediction potentiality compared to others.