View Proposal
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Proposer
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Radu-Casian Mihailescu
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Title
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Sentiment-Augmented Deep Learning for Anomaly Detection in Cryptocurrency Markets
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Goal
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Description
- Cryptocurrency markets are highly volatile, sentiment-driven, and difficult to model using traditional anomaly detection methods. Existing statistical and price-based techniques often fail to adapt to rapid regime shifts and subtle behavioural patterns. With the rise of deep learning and natural language processing, particularly large language models (LLMs), new opportunities have emerged to enhance market surveillance and risk detection.
Research Problem:
How can deep learning models be augmented with sentiment signals derived from LLMs to improve the detection of anomalies and regime shifts in cryptocurrency markets?
- Resources
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Background
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Url
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Difficulty Level
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Moderate
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Ethical Approval
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None
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Number Of Students
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1
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Supervisor
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Radu-Casian Mihailescu
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Keywords
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Degrees
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Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Bachelor of Science in Information Systems
Master of Engineering in Software Engineering
Master of Design in Games Design and Development
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Business Information Management
Master of Science in Computer Science for Cyber Security
Master of Science in Computer Systems Management
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Information Technology (Business)
Master of Science in Information Technology (Software Systems)
Master of Science in Network Security
Master of Science in Robotics
Master of Science in Software Engineering
Bachelor of Science in Computing Science
Bachelor of Engineering in Robotics
Bachelor of Science in Computer Science (Cyber Security)
Master of Science in Robotics with Industrial Application
Postgraduate Diploma in Artificial Intelligence
Bachelor of Science in Statistical Data Science
BSc Data Sciences