Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning

Bachelor Thesis , 2023 , 33 Pages

Medicine - Biomedical Engineering

This analysis explores advanced deep-learning techniques for stock price prediction, assessing transfer learning-based DTRSI, CNNs, and collaborative networks with sentiment analysis. DTRSI effectively addresses overfitting, outperforming traditional models. CNNs excel in predicting stock trends across time frames, while collaborative networks combining sentiment analysis and candlestick data show promise, particularly for specific stocks over longer periods. The study investigates the relevance of sentiment analysis from platforms like Twitter and StockTwits in predicting market movements. It introduces an innovative active deep learning approach for stock price forecasting, considering data size and sector impact. Emphasizing LSTM-based models, it highlights their potential to enhance stock price forecasting, offering insights for traders and investors by consolidating diverse prediction methods. This research lays the groundwork for future studies optimizing trading systems via data integration and advanced neural network architectures.

• Author: Yenni Rajasekhar (Author)
• Publication Year: 2023
• Pages: 33
• Catalog Number: V1437075
• ISBN (PDF): 9783346992611
• ISBN (Book): 9783346992628
• Language: English
• Tags: Stock price prediction, Deep learning techniques, DTRSI (Deep Transfer Reinforcement Stock Index), Sentiment analysis, LSTM-based models