Toward trust-based multi-modal user authentication on the Web : a fuzzy approach

In the last few years authentication has become of paramount importance both on the corporate Intranets and on the global Web. While most approaches focus on the initial authentication and then no further check ensure the identity of the navigating user, in this work we present a fuzzy approach to multi-modal authentication for a trust-based, continuous identity check during Web navigation. The potentiality of such an approach for generating trust-based metadata is also discussed

Dermatology disease classification via novel evolutionary artificial neural network

Neuro-genetic systems are biologically inspired computational models that use evolutionary algorithms (EAs) in conjunction with neural networks (NNs) to solve problems. They are especially useful in classification problems in which classifier systems are not able to provide easy answers. In this paper a novel neuro-genetic approach is used in order to predict a known classification problem, related to dermatology diseases

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 fuzzy trust model proposal to ensure the identity of a user in time

Access controls ensure that all direct accesses to objects are authorized by means of user identification. However, in some scenarios it is also necessary to continuously check the identity of the user in order to avoid malicious behaviors such as person exchanges immediately after the initial authentication phase. Aim of this work is to propose a methodology based on a balanced mix of strong and weak authentication techniques studied to guarantee a high and prolonged in time level of security combining the advantages of each authenticator

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

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

International audienceThis chapter presents an evolutionary Artificial Neural Networks (ANN) classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 (Moody, Comput Cardiol Challenge 38:273-276, 2011), whose aim is to develop an efficient algorithm able to run within a mobile phone that can provide useful feedback when acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Transform has been applied before the evolutionary approach in order to extract an ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge

Rappresentare l’armatura culturale del territorio con QGIS: l’esperienza del PTRA della Franciacorta

Landscape features are the result of interrelated actions of man-and-nature and can provide ecosystem services that need to be protected. Since urban planning policies can impact negatively on the conservation of cultural ecosystem services, urban plans must map them and make provision for their protection. For the Plan of Franciacorta (22 municipalities in Lombardy), we chose QGIS to set up a geo-database and map cultural heritage information. QGIS can provide more flexibility than a typical map, thanks to its graphics tools. To plan the development of actions to protect the landscape and suggest a range of planning opportunities for municipalities, an integrated representation of the landscape and protected ecological elements can highlight some critical issues: municipal borders can prove an obstacle in the implementation of supra-municipal projects and protected areas can include enclaves potentially vulnerable to urban pressures. Such maps have proved useful in guiding the planning choices in the development of the landscape protection schemes. The geo-location of critical aspects has brought out a range of inter-municipal planning opportunities

Knowledge Driven Behavioural Analysis in Process Intelligence

InthispaperweillustratehowtheknowledgedrivenBehaviourAnal- ysis, which has been used in the KITE.it process management framework, can support the evolution of analytics from descriptive to predictive. We describe how the methodology uses an iterative three-step process: first the descriptive knowledge is collected, querying the knowledge base, then the prescriptive and predictive knowledge phases allow us to evaluate business rules and objectives, extract unexpected business patterns, and screen exceptions. The procedure is iterative since this novel knowledge drives the definition of new descriptive an- alytics that can be combined with business rules and objectives to increase our level of knowledge on the combination between process behaviour and contex- tual information