The current entry for the proposed approach obtained an overall score of 84.15% in the last phase of the challenge, which provided the sixth official score and differs from the best score of 86.02% by just 1.87%.
Expert methods, which widely applied for human decision making, were employed for neural networks. It was developed an exchange rates prediction and trading algorithm with using of experts information processing techniques -Delphi method and prediction compatibility. Proposed algorithm limited to eight experts. Each of experts represented recurrent neural network, Evolino-based Long Short-Term Memory (LSTM) by using of genetic learning algorithm, EVOlution of recurrent systems with LINear Outputs (EVOLINO). Statistical investigation of offered algorithm shows the significantly increase of the reliability of prediction. Developed algorithm was applied for trading of historical forex exchange rates. Obtained test trading results were presented
Classification of Atrial Fibrillation from diverse electrocardiographic (ECG) signals is the challenging objective of the 2017 Physionet Challenge. We suggest a Long Short Term Memory (LSTM) network, which learns patterns directly from pre-computed QRS complex features that classifies ECG signals. Although our architecture is considered deep, it only consists of 1791 parameters. The result is an accurate, lightweight solution that classifies ECG records as Normal, Atrial fibrillation, Other or Too noisy with final challenge score of 0.78.
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