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              <text>An Intelligent Stock Market Automation with Conversational Web Based Build Operate Transfer (BOT)</text>
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              <text>artificial intelligence; chatbot; long short term memory; natural language processing; recurrent neural network; speech recognition</text>
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              <text>Zerodha, Upstox, Angel Broking, Groww, etc. Such companies have the most significant users of traders/investors in the equity share market. Their trust is based on their ease of use, less time-consuming process, and accurate graphs and charts of real-Time data. But what if such companies had an algorithm that could predict the future prices of any share? Not just based on historical data but also on sentimental data? This project aims to build a speech recognition chatbot like Alexa Google, which will use Recurring Neural Network-Long Short-Term Memory (RNNLSTM) and Natural Language Processing (NLP) to predict future intra-day prices.   2022 IEEE.</text>
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              <text>Bajaj A.; Preethi N.; Godwin B.J.; George F.J.P.</text>
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              <text>2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2022</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT54229.2022.10041477" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT54229.2022.10041477&lt;/a&gt;
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              <text>ISBN: 978-166545361-5</text>
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              <text>Bajaj A., CHRIST University, School of Business and Management, Pune, India; Preethi N., CHRIST University, Data Science Department, Pune, India; Godwin B.J., CHRIST University, School of Business and Management, Pune, India; George F.J.P., CHRIST University, Data Science Department, Pune, India</text>
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