Browsing by Autor "Paul Narayanan"

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    A Review on Emoji Entry Prediction for Future Finance Market Analysis Using Convolutional Neural Network
    (Turkish Online Journal of Qualitative Inquiry (TOJQI), 2025) Paul Narayanan; Kishore Kunal; Veeramani Ganesan; Singaravelu Ganesan; A. T. Jaganathan; Vairavel Madeshwaren
    Textual and financial data in social media has come a long way in the present. Emojis, the primary focus of this study piece, allow emotions to be visually represented thanks to the advent of text-based digital communication. By adding visual currency attractiveness to text, emojis in digital communication enhance communication and open up new channels for innovation and exchange. The neural network model for text-based emoji entry prediction is highly optimised, however because of little knowledge in this field, it is more difficult to predict future emojis from images all the finance symbols. Emojis are a great alternative to linguistically independent, sentiment-aligned embeddings since they are consistent and convey a clear sentiment signal NSE and BSE market. Compared to models for text, models for symbolic description have received less attention. In this study,Main researchers employed CNN architecture for image classification together with an emoji2vec embedding into the word2vec model to predict emoji from photos apply in finance sector and finding. Additionally, we performed a sentiment analysis on the text to forecast upcoming emoji labels added. Our approach effectively communicates how the emojis relate to one another. The length of the search for incoming image-based emoji predictions has been optimised using this model.
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    Stock Price Prediction in India: Comparing Stochastic Differential Equations with MCMC, LSTM, and ARIMA Models and Exploring a Hybrid Approach
    (Turkish Online Journal of Qualitative Inquiry (TOJQI), 2025) S. Bhuvaneshwari; S. Nirmala Sugirtha Rajini; Paul Narayanan; Kishore Kunal; Vairavel Madeshwaren; Saranya Anbarasu
    The inherent volatility and nonlinearity of stock prices make them a crucial challenge in financial markets. This study investigates how well stochastic differential equations (SDEs) with parameter estimation employ the Markov Chain Monte Carlo (MCMC) algorithm to model changes in Indian stock prices. To evaluate this methods predictive accuracy we compare its performance to that of conventional Long Short-Term Memory (LSTM) networks and AutoRegressive Integrated Moving Average (ARIMA) models. A probabilistic estimation of important parameters in the SDE model is made possible by the Bayesian inference framework used in the MCMC algorithm which successfully captures market uncertainties. Our results show each models advantages and disadvantages in predicting stock prices highlighting how well-suited each is for various time horizons and market circumstances. In order to take advantage of both stochastic modeling and deep learning capabilities we also suggest a novel hybrid model that combines SDE-MCMC with LSTM and ARIMA. Results from experiments show that by fusing the advantages of machine learning and statistics the hybrid model increases forecasting accuracy. In addition to offering insights for analysts and investors in making data-driven decisions this research advances stock price prediction techniques.