Hand Gesture Recognition For Multi-culture Sign Language Using Graph And General Deep Learning Network

Abstract

This Project Proposes A Novel Stock Price Prediction Framework That Builds Upon Existing Models By Incorporating Several Key Enhancements. The Proposed Model Will Utilize Advanced Deep Learning Architectures, Such As Transformer Networks Or More Sophisticated LSTM Variants, To Capture Complex Temporal Dependencies In Stock Price Data. Furthermore, It Will Implement A Dynamic Feature Selection Mechanism To Identify And Prioritize The Most Relevant Features For Prediction, Adapting To Changing Market Conditions. Optionally, The Framework Will Explore The Integration Of Sentiment Analysis From News Articles And Social Media To Capture Market Sentiment As An Additional Predictive Factor. The Model's Performance Will Be Rigorously Evaluated And Compared Against Existing State-of-the-art Models Using Standard Metrics. This Research Aims To Demonstrate The Potential Of Enhanced Deep Learning Techniques And Dynamic Feature Selection To Achieve More Accurate And Robust Stock Price Predictions.

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