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Full-Text Articles in Computer Engineering
A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine
A Hybrid Neural Network For Stock Price Direction Forecasting, Daniel Devine
Dissertations
The volatility of stock markets makes them notoriously difficult to predict and is the reason that many investors sell out at the wrong time. Contrary to the efficient market hypothesis (EMH) and the random walk theory, contribution to the study of machine learning models for stock price forecasting has shown evidence of stock markets predictability with varying degrees of success. Contemporary approaches have sought to use a hybrid of convolutional neural network (CNN) for its feature extraction capabilities and long short-term memory (LSTM) neural network for its time series prediction. This comparative study aims to determine the predictability of stock …
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Exploring The Effect Of Different Numbers Of Convolutional Filters And Training Loops On The Performance Of Alphazero, Jared Prince
Masters Theses & Specialist Projects
In this work, the algorithm used by AlphaZero is adapted for dots and boxes, a two-player game. This algorithm is explored using different numbers of convolutional filters and training loops, in order to better understand the effect these parameters have on the learning of the player. Different board sizes are also tested to compare these parameters in relation to game complexity. AlphaZero originated as a Go player using an algorithm which combines Monte Carlo tree search and convolutional neural networks. This novel approach, integrating a reinforcement learning method previously applied to Go (MCTS) with a supervised learning method (neural networks) …
A Sign-To-Speech Translation System, Koka Veera Raghava Rao
A Sign-To-Speech Translation System, Koka Veera Raghava Rao
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
This thesis describes sign-to-speech translation using neural networks. Sign language translation is an interesting but difficult problem for which neural network techniques seem promising because of their ability to adjust to the user's hand movements, which is not possible to do by most other techniques. However, even using neural networks and artificial sign languages, the translation is hard, and the best-known system, that of Fels & Hinton (1993), is capable of translating only 66 root words and 203 words including their conjugations. This research improves their results to 790 root signs and 2718 words including their conjugations while preserving a …