Antonia Azzini

A fuzzy approach to multimodal biometric continuous authentication

In the last few years the security of the user’s identity has become of paramount importance. In this paper we investigate the opportunity of using a multimodal biometric system as input of a fuzzy controller designed with the aim of preventing user substitution after the initial authentication process.

Evolving neural networks for static single-position automated trading

This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given profit target is met or at market close. Experimental results indicate that, despite its simplicity, both in terms of input data and in terms of trading strategy, such an approach to automated trading may yield significant returns.

A neural evolutionary approach to financial modeling

This paper presents an approach to the joint optimization of neural network structure and weights which can take advantage of backpropagation as a specialized decoder. The approach has been applied to a financial problem, whereby a factor model capturing the mutual relationships among several financial instruments is sought for. A sample application of such a model to statistical arbitrage is also presented.

Impostor Users Discovery Using a Multimodal Biometric Continuous Authentication Fuzzy System

In the last few years the security of the users identity has become of paramount importance. In this paper we investigate the behavior of a fuzzy controller with a multimodal biometric system as input designed with the aim of preventing user substitution after the initial authentication process. In particular this paper presents the results of the system behavior tested with impostor users.

Incipient Fault Diagnosis in Electrical Drives by Tuned Neural Networks

In order to identify any decrease in efficiency and any loss in industrial application a suitable monitoring system for processes is often required. With the proposed approach useful diagnostic indications can be obtained by a low-cost extension of the monitoring activity. In this way, the reliability of the obtained indications can be significantly increased considering the combination of advanced time-frequency transform, or time times scale, such as wavelets, and a new evolutionary optimisation approach based on artificial neural networks (ANNs). This paper describes an approach to the joint optimization of neural network structure and weights which can take advantage of the backpropagation algorithm as a specialized decoder. The presented approach has been successfully applied to a real-world machine fault diagnosis problem

A novel similarity-based crossover for artificial neural network evolution

This work presents an evolutionary approach for the optimization of neural networks design, based on the joint evolution of the topology and the connection weights, providing a novel similarity-based crossover that aims to overcome one of the major problems of this operator, known as the permutation problem. The approach has been implemented and applied to two benchmark classification problems in machine learning, and the experimental results, compared to those obtained by other works in the literature, show how it can produce compact neural networks with a satisfactory generalization capability.

A New Genetic Approach for Neural Network Design

Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of study in evolutionary design. They are biologically-inspired computational models that use evolutionary algorithms (EAs) in conjunction with neural networks (NNs) to solve problems. EAs are based on natural genetic evolution of individuals in a defined environment and they are useful for complex optimization problems with huge number of parameters and where the analytical solutions are difficult to obtain.

Evolutionary ANNs: A state of the art survey

Neuro-genetic systems have become a very important topic of study in evolutionary computation in recent years. They are models that use evolutionary algorithms to optimize neural network design. This article is a survey of the state of art of evolutionary ANN systems, with a focus on the most recent developments, presented in the literature during the last decade. The main purpose of this work is to provide an update and extension of Yaos milestone survey, published back in 1999, by taking the most recent literature into account.

Evolving Neural Networks for Word Sense Disambiguation

We propose a supervised approach to word sense disambiguation based on neural networks combined with evolutionary algorithms. Large tagged datasets for every sense of a polysemous word are considered, and used to evolve an optimized neural network that correctly disambiguates the sense of the given word considering the context in which it occurs. The viability of the approach has been demonstrated through experiments carried out on a representative set of polysemous words.

Ensuring the identity of a user in time: a multi-modal fuzzy approach

This work proposes a fuzzy multimodal technique capable of guaranteeing the desired level of security while keeping under control the high costs typically associated to some biometric authentication devices. Specifically we describe a fuzzy controller choosing within a palette of authentication techniques to continuously check and confirm its trust in the identity of a user.