Titlebook: Artificial Intelligence and Soft Computing; 21st International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2023

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COMMA

nonchalance

K.-H. Hanne,U. Schmidt,K.-P. Fähnrichn various time series domains. Although several domain-adversarial models have been proposed in the past, there is a lack of empirical results with different types of time series. This paper provides an empirical analysis with multiple models, datasets and evaluation objectives. Two models known fro

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https://doi.org/10.1007/978-3-642-77659-5eights. Any errors made during the forecasting step reduce the accuracy of the asset weightings, and hence the profitability of the overall portfolio. The . (PT) network, introduced here, circumvents the need to predict asset returns and instead directly optimizes the Sharpe ratio, a risk-adjusted p

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,Bau und Einrichtung von Krüppelheimen, network resource with the intent to obstruct the utility of a service is associated with hacktivism, blackmailing and extortion attempts. Intrusion Prevention Systems are an essential line of defence against this problem, strengthening public institutions, industrial and critical infrastructure ali

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K. Biesalski,H. Eckhardt,K. Wickelare many types of deep learning models, however the most important to fit architecture and training model to the input data. In this article we propose a model of deep learning based on architecture in which we use BiLSTM neural network. Proposed model is trained by using Adam algorithm. For the res

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