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Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: 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.
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Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning
Published by
GRIN Verlag, GRIN Verlag Jan 2024, 2024
ISBN 10: 3346992624
ISBN 13: 9783346992628
New
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: 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. 36 pp. Englisch. Seller Inventory # 9783346992628
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Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: 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. Seller Inventory # 9783346992628
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Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning
Published by
GRIN Verlag, GRIN Verlag Jan 2024, 2024
ISBN 10: 3346992624
ISBN 13: 9783346992628
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bachelor Thesis from the year 2023 in the subject Medicine - Biomedical Engineering, , language: English, abstract: 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. 36 pp. Englisch. Seller Inventory # 9783346992628
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Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning
Print on DemandSeller: preigu, Osnabrück, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Revolutionizing Cardiac Muscle Detection. Harnessing the Power of Machine Learning | Yenni Rajasekhar | Taschenbuch | Englisch | 2024 | GRIN Verlag | EAN 9783346992628 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 128277654