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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Salunke, Mahendra B.
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Computer Science & IT Electrical & Electronics Engineering

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Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies Korade, Nilesh B.; Salunke, Mahendra B.; Bhosle, Amol A.; Asalkar, Gayatri G.; Lal, Bechoo; Kumbharkar, Prashant B.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp507-517

Abstract

Businesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which can provide round-the-clock support. Chatbots can react to user inquiries quickly, reducing wait times and improving customer satisfaction. It becomes challenging for the chatbot to differentiate between two queries that users pose that carry the same meaning, making it harder for it to understand and react appropriately. The aim of this research is to develop a chatbot capable of understanding the semantic meaning of questions as well as recognizing various speech patterns, accents, and dialects to provide accurate responses. In this research, we have implemented a voice-enabled chatbot system where users can verbally pose questions, and the chatbot provides responses through voice assistance. The architecture incorporates several key components: a question-answer database, OpenAI embedding for semantic representation, and OpenAI text-to-speech (TTS) and speech-to-text (STT) for audio-to-text and text-to-audio conversion, respectively. Specifically, OpenAI embedding is utilized to encode questions and responses into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) is trained on OpenAI embeddings to identify similarities between user queries and questions within the dataset. This framework allows for seamless interaction between users and the chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) and speech recognition. The outcome demonstrates that the XGBoost model delivers excellent outcomes when it is trained on OpenAI embedding and tuned with the particle swarm optimizer (PSO). The OpenAI-generated embedding has good potential for capturing sentence similarity and provides excellent information for models trained on it.
Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders Korade, Nilesh B.; Salunke, Mahendra B.; Bhosle, Amol; Joshi, Dhanashri; Patil, Kavita; Sangve, Sunil M.; Deshmukh, Rushali A.; Patil, Aparna S.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2254-2263

Abstract

The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
Co-Authors Asalkar, Gayatri G. Bhosle, Amol Bhosle, Amol A. Deshmukh, Rushali A. Joshi, Dhanashri Korade, Nilesh B. Kumbharkar, Prashant B. Lal, Bechoo Patil, Aparna S. Patil, Kavita Sangve, Sunil M.