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Found 3 papers, 1 papers with code
Smart Parking Space Detection under Hazy conditions using Convolutional Neural Networks: A Novel Approach
Additionally, training procedures are proposed for dehazing networks to maximize the performance of the system on both hazy and non-hazy conditions.
Forecasting directional movements of stock prices for intraday trading using LSTM and random forests
Hence we outperform the single-feature setting in Fischer & Krauss (2018) and Krauss et al. (2017) consisting only of the daily returns with respect to the closing prices, having corresponding daily returns of 0. 41% and of 0. 39% with respect to LSTM and random forests, respectively.
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Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction
Stock prediction is a topic undergoing intense study for many years.
