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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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models (eg auto-encoders and generative adversarial networks) and reinforcement/imitation learning algorithms for Markov Decision Processes. The application areas are different problems in text processing
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for this purpose. However, such rigid layout doesn’t now allow adaptive layout for interfaces that run on a variety of screen sizes or that need different control sizes due to application being internationalised
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Aim/outline Graphs or networks are effective tools to representing a variety of data in different domains. In the biological domain, chemical compounds can be represented as networks, with atoms as
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and algorithmic bias) towards the underprivileged students. In the first line of research, we will focus on students whose disengagement with learning can be explained by their poor financial conditions