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Extraction of texture features from x-ray images: case of osteoarthritis detectionTexture features quantitatively represent patterns of interest in image analysis and interpretation. Texture features can vary so largely that the analysis leads to interpretation errors and undesirable consequences. In such cases, finding of informative features becomes problematic. In medical imaging, the texture features were found useful for representing variations in patterns of pixel intensity, which were correlated with pathological changes. In this paper, we describe a new approach to extracting the texture features which are represented on the basis of Zernike orthogonal polynomials. We report the preliminary results which were obtained for a case of osteoarthritis detection in X-ray images using a deep learning paradigm known as group method of data handling.
Using autoregressive modelling and machine learning for stock market prediction and tradingInvestors raise profit from stock market by maximising gains and minimising loses. The profit is difficult to raise because of the volatile nature of stock market prices. Predictive modelling allows investors to make informed decisions. In this paper, we compare four forecasting models: autoregressive integrated moving average (ARIMA), vector autoregression (VAR), long short-term memory (LSTM) and nonlinear autoregressive Exogenous (NARX). The results of predictive modelling are analysed and compared in terms of prediction accuracy. The research aims to develop a new profitable trading strategy. Our findings are: (i) the NARX model has provided accurate short-term predictions but failed long forecasts, and (ii) the VAR model can form a good trend line required for trading. Thus, the profitable trading strategy can combine the machine learning predictive modelling and technical analysis.