F
Francesco Palmieri
Researcher at University of Salerno
Publications - 454
Citations - 8596
Francesco Palmieri is an academic researcher from University of Salerno. The author has contributed to research in topics: The Internet & Cloud computing. The author has an hindex of 43, co-authored 434 publications receiving 7038 citations. Previous affiliations of Francesco Palmieri include University of Naples Federico II & University of Delaware.
Papers
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Network anomaly detection with the restricted Boltzmann machine
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.
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Using generative adversarial networks for improving classification effectiveness in credit card fraud detection
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.
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Original Contribution: Optimal filtering algorithms for fast learning in feedforward neural networks
TL;DR: This paper applies optimal filtering techniques to train feedforward networks in the standard supervised learning framework, and presents three algorithms which are computationally more expensive than standard back propagation, but local at the neuron level.
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Classifying mammographic lesions using computerized image analysis
TL;DR: The classification of 3 common breast lesions, fibroadenomas, cysts, and cancers, was achieved using computerized image analysis of tumor shape in conjunction with patient age using a video camera and commercial frame grabber on a PC-based computer system.
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Smart Cloud Storage Service Selection Based on Fuzzy Logic, Theory of Evidence and Game Theory
TL;DR: The fuzzy sets theory is used to express vagueness in the subjective preferences of the customers and the service selection is resolved with the distributed application of fuzzy inference or Dempster-Shafer theory of evidence.