Bioinformatics Solutions for Image Data Processing

In recent years, the increasing use of medical devices has led to the generation of large amounts of data, including image data. Bioinformatics solutions provide an effective approach for image data processing in order to retrieve information of interest and to integrate several data sources for knowledge extraction; furthermore, images processing techniques support scientists and physicians in diagnosis and therapies. In addition, bioinformatics image analysis may be extended to support several scenarios, for instance, in cyber-security the biometric recognition systems are applied to unlock devices and restricted areas, as well as to access sensitive data. In medicine, computational platforms generate high amount of data from medical devices such as Computed Tomography (CT), and Magnetic Resonance Imaging (MRI); this chapter will survey on bioinformatics solutions and toolkits for medical imaging in order to suggest an overview of techniques and methods that can be applied for the imaging analysis in medicine

Methodological approach for evaluating the geo-exchange potential: VIGOR Project

In the framework of VIGOR Project, a national project coordinated by the Institute of Geosciences and Earth Resources (CNR-IGG) and sponsored by the Ministry of Economic Development (MiSE), dedicated to the evaluation of geothermal potential in the regions of the Convergence Objective in Italy (Puglia, Calabria, Campania and Sicily), is expected to evaluate the ability of the territory to heat exchange with the ground for air conditioning of buildings. To identify the conditions for the development of low enthalpy geothermal systems collected and organized on a regional scale geological and stratigraphic data useful for the preparation of a specific thematic mapping, able to represent in a synergistic and simplified way the physical parameters (geological, lithostratigraphic, hydrogeological, thermodynamic) that most influence the subsoil behavior for thermal exchange. The litho-stratigraphic and hydrogeological database created for every region led to the production of different cartographic thematic maps, such as the thermal conductivity (lithological and stratigraphical), the surface geothermal flux, the average annual temperature of air, the climate zoning, the areas of hydrogeological restrictions. To obtain a single representation of the geo-exchange potential of the region, the different thematic maps described must be combined together by means of an algorithm, defined on the basis of the SINTACS methodology. The purpose is to weigh the contributions of the involved parameters and to produce a preliminary synthesis map able to identify the territorial use of geothermal heat pump systems, based on the geological characteristics and in agreement with the existing regulatory constraints

Cold Start Problem: a Lightweight Approach

The chapter presents the SWAPTeam participation at the ECML/PKDD 2011 - Discovery Challenge for the task on the cold start problem focused on making recommendations for new video lectures. The developed solution uses a content-based approach because it is less sensitive to the cold start problem that is commonly associated with pure collaborative filtering recommenders. The Challenge organizers encouraged solutions that can actually affect VideoLecture.net, thus the proposed integration strategy is the hybridization by switching. In addition, the surrounding idea for the proposed solution is that providing recommendations about cold items remains a chancy task, thus a computational resource curtailment for such task is a reasonable strategy to control performance trade-off of a day-to-day running system. The main contribution concerns about the compromise between recommendation accuracy and scalability performance of proposed approach

Serendipitous Encounters along Dynamically Personalized Museum Tours

Today Recommender Systems (RSs) are commonly used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based RSs rely on the concept of similarity between items. It is a common belief that the user is interested in what is similar to what she has already bought/searched/visited. We believe that there are some contexts in which this assumption is wrong: it is the case of acquiring unsearched but still useful items or pieces of information. This is called serendipity. Our purpose is to stimulate users and facilitate these serendipitous encounters to happen. The paper presents a hybrid recommender system that joins a content-based approach and serendipitous heuristics in order to provide also surprising suggestions. The reference scenario concerns with personalized tours in a museum and serendipitous items are introduced by slight diversions on the context-aware tours. Copyright owned by the authors

Analyse du champ aerodynamique d'un jet supersonique chaud defini par simulation numerique

Communication to : 38eme colloque AAAF d'aerodynamique appliquee, Arcachon (France), 07-08 octobre 2002SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : 22419, issue : a.2002 n.190 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

A Hybrid Content-Collaborative Recommender System Integrated into an Electronic Performance Support System

An Electronic Performance Support System (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project 1 aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles, in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our system is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named "semantic user profile", is exploited by the hybrid recommender in the neighborhood formation process