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@article{178094,
        author = {JAKKULA SNEHA and PONKAM CHANDRASEKHAR and PONNERI MUTHUPRIYA and SHAIK HUSSAIN BASHA and DUVVURU NISHWANTH},
        title = {Survey on AI-Powered Simulations to Elevate Financial Literacy in Real-Time},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2133-2144},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178094},
        abstract = {This research explores the application of advanced machine learning techniques, particularly deep learning, to improve stock market prediction accuracy. The studies investigate novel models, including stock sequence array convolutional neural networks (CNNs), long short-term memory (LSTM) networks, recurrent neural networks (RNNs), and deep reinforcement learning (DRL), for analyzing financial time series data and forecasting price trends. These models aim to capture complex, non-linear market patterns that traditional statistical methods often fail to recognize. The research emphasizes the importance of feature extraction, turning point identification, and hyperparameter optimization in achieving accurate predictions. Results demonstrate promising performance, with some models reporting prediction accuracies exceeding 99%. The studies highlight the potential of these AI-driven approaches to provide investors with more reliable forecasts, ultimately aiding in informed trading decisions and risk management. Furthermore, the use of both historical and leading indicator data is explored to enhance the model's predictive capabilities.},
        keywords = {Artificial Intelligence, Stock Market Analysis, Stock Prediction , CNN, Deep Neural Networks, Reinforcement Learning, Machine Learning, Investment guide.},
        month = {May},
        }