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TL;DR: The effectiveness of a detection approach based on machine learning is explored, using the Discriminative Restricted Boltzmann Machine to combine the expressive power of generative models with good classification accuracy capabilities to infer part of its knowledge from incomplete training data.

TL;DR: Experiments show that a classifier training on the augmented set outperforms the same classifier trained on the original data, especially as far the sensitivity is concerned, resulting in an effective fraud detection mechanism.

TL;DR: This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process.

TL;DR: This work used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex, and proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data.

TL;DR: A two‐stage anomaly detection strategy based on multiple distributed sensors located throughout the network, based on a binary classification scheme (detection is casted into an anomalous/normal classification problem) driven by machine learning‐inferred decision trees.