Search results for: Gaetano Zazzaro

Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

Abstract:

Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: Artificial Neural Network, Data Mining, Electroencephalogram, Epilepsy, Feature Extraction, Seizure Detection, Signal Processing.

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Authors: G. Zazzaro, F.M. Pisano, G. Romano

Abstract:

During last decades, worldwide researchers dedicated efforts to develop machine-based seismic Early Warning systems, aiming at reducing the huge human losses and economic damages. The elaboration time of seismic waveforms is to be reduced in order to increase the time interval available for the activation of safety measures. This paper suggests a Data Mining model able to correctly and quickly estimate dangerousness of the running seismic event. Several thousand seismic recordings of Japanese and Italian earthquakes were analyzed and a model was obtained by means of a Bayesian Network (BN), which was tested just over the first recordings of seismic events in order to reduce the decision time and the test results were very satisfactory. The model was integrated within an Early Warning System prototype able to collect and elaborate data from a seismic sensor network, estimate the dangerousness of the running earthquake and take the decision of activating the warning promptly.

Keywords: Bayesian Networks, Decision Support System, Magnitude Classification, Seismic Early Warning System

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Authors: Domenico Pirozzi, Angelo Ausiello, Gaetano Zuccaro, Filomena Sannino, Abu Yousuf

Abstract:

The oleaginous yeasts Lipomyces starkey were grown in the presence of dairy industry wastewaters (DIW). The yeasts were able to degrade the organic components of DIW and to produce a significant fraction of their biomass as triglycerides. When using DIW from the Ricotta cheese production or residual whey as growth medium, the L. starkey could be cultured without dilution nor external organic supplement. On the contrary, the yeasts could only partially degrade the DIW from the Mozzarella cheese production, due to the accumulation of a metabolic product beyond the threshold of toxicity. In this case, a dilution of the DIW was required to obtain a more efficient degradation of the carbon compounds and an higher yield in oleaginous biomass. The fatty acid distribution of the microbial oils obtained showed a prevalence of oleic acid, and is compatible with the production of a II generation biodiesel offering a good resistance to oxidation as well as an excellent cold-performance.

Keywords: Yeasts, Lipids, Biodiesel, Dairy industry wastewaters.

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