123 research outputs found
A smart financial advisory system exploiting Case-Based Reasoning
In the financial advisory context, knowledge-based recommendations based on Case-Based Reasoning are an emerging trend. They usually exploit knowledge about past experiences and about the characterization of both customers and financial products. In the present paper, we report the experience related to the development of a case-based recommendation module in a project called SmartFasi. We present a solution aimed at personalizing the asset picking phase, by taking into consideration choices made by customers who have a financial and personal data profile "similar" to the current one. We discuss the notion of distance-based similarity adopted in our system and how to actually implement an asset recommendation strategy integrated with the other software modules of SmartFasi. We finally discuss the impact such a strategy may have both from the point of view of private investors and professional users
Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont
This paper discusses the data mining approach followed in
a project called TRAQUASwine, aimed at the definition of
methods for data analytical assessment of the authenticity
and protection, against fake versions, of some of the highest
value Nebbiolo-based wines from Piedmont region in Italy.
This is a big issue in the wine market, where commercial
frauds related to such a kind of products are estimated to
be worth millions of Euros. The objective is twofold: to
show that the problem can be addressed without expensive
and hyper-specialized wine analyses, and to demonstrate
the actual usefulness of classification algorithms for
data mining on the resulting chemical profiles. Following
Wagstaff\u2019s proposal for practical exploitation of machine
learning (and data mining) approaches, we describe how
data have been collected and prepared for the production
of different datasets, how suitable classification models have
been identified and how the interpretation of the results suggests
the emergence of an active role of classification techniques,
based on standard chemical profiling, for the assesment
of the authenticity of the wines target of the stud
DBNet, a tool to convert Dynamic Fault Trees into Dynamic Bayesian Networks
The unreliability evaluation of a system including dependencies involving
the state of components or the failure events, can be performed by modelling the system as a Dynamic Fault Tree (DFT). The combinatorial technique used to solve standard Fault Trees is not suitable for the analysis of a DFT. The conversion into a Dynamic Bayesian Network (DBN) is a way to analyze a DFT. This paper presents a software tool allowing the automatic analysis of a DFTexploiting its conversion to a DBN. First, the architecture of the tool is described, together with the rules implemented in the tool, to convert dynamic gates in DBNs. Then, the tool is tested on a case of system: its DFT model and the corresponding DBN are provided and analyzed by means of the tool. The obtained unreliability results are compared with those returned by other tools, in order to verify their correctness. Moreover, the use of DBNs allows to compute further results on the model, such as diagnostic and sensitivity indices
A Bayesian Network Approach for the Interpretation of Cyber Attacks to Power Systems
The focus of this paper is on the analysis of the cyber security
resilience of digital infrastructures deployed by power grids, internationally recognized as a priority since several recent cyber attacks targeted
energy systems and in particular the power service. In response to the
regulatory framework, this paper presents an analysis approach based
on the Bayesian Networks formalism and on real world threat scenarios.
Our approach enables analyses oriented to planning of security measures
and monitoring, and to forecasting of adversarial behaviours
Analisi e rilevamento intelligente di processi di attacco alle Smart-Grid
Proponiamo una metodologia basata sulle Reti Bayesiane come strumento di supporto all’analisi della sicurezza di Smart Grid, ed in particolare per la previsione di intrusioni e attività ostili
On the Usefulness of Re-using Diagnostic Solutions
Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is to show that the same kind of results apply also for diagnosis. We propose a theoretical complexity analysis coupled with some experimental tests, intended to evaluate the adequacy of adaptation strategies which re-use the solutions of past diagnostic problems in order to build a solution to the problem to be solved. Results of such analysis show that, even if diagnosis re-use falls into the same complexity class of diagnosis generation (they are both NP-complete problems), practical advantages can be obtained by exploiting a hybrid architecture combining case-based and modelbased diagnostic problem solving in a unifying framework
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