Browsing by Autor "Veeramani Ganesan"

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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.
Ensemble-Based Machine Learning Approach For Fake News Detection On Telegram With Enhanced Predictive Accuracy
(Turkish Online Journal of Qualitative Inquiry (TOJQI), 2025) Poody Rajan Y; Kishore Kunal; Amutha Govindan; Korra Balu; Veeramani Ganesan; Vairavel Madeshwaren
The rapid proliferation of fake news on social media platforms has raised significant concerns about misinformation, particularly on messaging applications like Telegram. This trend poses a severe threat to public trust and social harmony. Detecting fake news in such environments requires the development of efficient machine learning (ML) models that can accurately identify misleading content while minimizing false positives and negatives. This research aims to propose a robust machine learning-based framework for detecting fake news on Telegram by analyzing text content and user interaction patterns. Data collection involved scraping a dataset from publicly available Telegram channels, which include both genuine and fake news articles with relevant metadata such as user reactions and engagement levels. To address the problem of fake news detection, a set of machine learning algorithms, including XGBoost, K-Nearest Neighbors (KNN), Decision Trees, and Naive Bayes, were explored. A novel ensemble-based approach, termed Ensemble Feature Fusion (EFF), is introduced, combining the strengths of multiple classifiers to enhance predictive accuracy and robustness against diverse fake news characteristics. Performance metrics such as Accuracy, Engagement-Weighted Accuracy (EWA), False Positive Cost (FPC) , Contextual Precision (CP), and Temporal Consistency Index (TCI) were evaluated in this research. Results indicate that the proposed model outperforms conventional ML techniques, demonstrating improved classification accuracy and reduced error rates in detecting fake news. This approach provides a promising solution to the growing problem of misinformation on Telegram.