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Full-Text Articles in Physical Sciences and Mathematics
Choice Of Feature Space For Classification Of Network Ip-Traffic By Machine Learning Methods, Avazjon Marakhimov, Ulugbek Ohundadaev
Choice Of Feature Space For Classification Of Network Ip-Traffic By Machine Learning Methods, Avazjon Marakhimov, Ulugbek Ohundadaev
Bulletin of National University of Uzbekistan: Mathematics and Natural Sciences
IP-protocol and transport layer protocols (TCP, UDP) have many different parameters and characteristics, which can be obtained both directly from packet headers and statistical observations of the flows. To solve the problem of classification of network traffc by methods of machine learning, it is necessary to determine a set of data (attributes), which it is reasonable to use for solving the classification problem.
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 …
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
Deep Learning For Compressive Sar Imaging With Train-Test Discrepancy, Morgan R. Mccamey
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We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of under sampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining …