Ana Belén Gil

A Multi-agent System that Searches for Learning Objects in Heterogeneous Repositories

This paper presents the BRENHET application, which introduces a new concept in searching for educational resources by using a learning object paradigm that describes these resources. The application is composed of a complete agent-based architecture that implements the concept of federated search. It can search different repositories in parallel, and is based on abstraction layers between the repositories and the search clients.

Automatic Learning Object Extraction and Classification in Heterogeneous Environments

This paper proposes the use of federated databases techniques in searching for educational resources by using a learning object paradigm that describes these resources based on metadata. Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organizing and sorting search results in a meaningful way for educational content. The paper presents also the ground for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients.

A bio-inspired computational high-precision dental milling system

A novel bio-inspired computational high-precision dental milling system is proposed in this interdisciplinar research. The system applies several bio-inspired models, based on unsupervised learning, that analyse and identify the most relevant features of high-precision dental-milling data sets and their internal structures. Finally, a supervised neural architecture and certain identification techniques are applied, in order to model and to optimize the high-precision process. This is done by empirically testing the model using a real data set taken from a dynamic high-precision machining centre with five axes.

Optimizing a dental milling process by means of soft computing techniques

A novel soft computing system to optimize a dental milling process is proposed. The model is based on the initial application of several statistical and projection methods as Principal Component Analysis and Cooperative Maximum Likelihood Hebbian Learning to analyze the structure of the data set and to identify the most relevant variables. Finally, a supervised neural model and identification techniques are applied, in order to model the process and optimize it. In this study a real data set obtained by a dynamic machining center with five axes simultaneously is analyzed to empirically test the novel system in order to optimize the time error.

An Adaptive e-Commerce System Definition

The emergence and growing popularity of electronic commerce and more specifically of catalogue-based sales, has made it a necessity to define flexible and adaptive e-commerce systems and architectures. Our work describes an e-commerce system that is suitable for small and medium enterprises, where several enterprises share a common e-commerce site, which automatically adapts its interface to offer end-users the products organised in electronic catalogues (e-catalogues). To be precise, in this paper we present the overall architecture of the e-commerce system, an architecture that is based on an agent-oriented technology. We also describe the actual state our system, which is called e-CoUSAL, and implements an adaptive agent in the server side of the architecture.

Learning Object Repositories with Federated Searcher over the Cloud

The education sector is a significant generator, consumer and depository for educational content. Educators and Learners have access to technologies that allow them to obtain information ubiquitously on demand. The problems arising from the integration of educational content are usually caused by the vast amount of educational content distributed among several repositories. This work presents a proposal for an architecture based on a cloud computing paradigm that will permit the evolution of current learning resource repositories by means of cloud computing paradigm and the integration of federated search system.

TV-SeriesRec: A Recommender System Based on Fuzzy Associative Classification and Semantic Information

Recommender systems have become essential in many web sites, especially in the e-commerce area; however, they are not extended enough in some domains. In this work, a recommender system for TV series is presented due to the increasing interest for this kind of products. The system implements a methodology that deals with the most important problems of recommender systems.

BRENHET2, A MAS to Facilitate the Reutilization of LOs through Federated Search

Nowadays, the reutilization of learning resources makes possible a reduction of the time of creation educational contents. In this sense, one of the most advances is the use of federated search systems in order to retrieve different resources from a set of repositories more quickly. But nowadays, the main problem is the high heterogeneity in this context. Multiagent Systems witch consider organizational could be the appropriate answer to solve this problem.

Hybrid multiagent system for automatic object learning classification

The rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of learning object metadata, which provides learners in a web-based educational system with ubiquitous access to multiple distributed repositories This article presents a hybrid agent-based architecture that enables the recovery of learning objects tagged in Learning Object Metadata (LOM) and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives.

Learning object retrieval in heterogeneous environments

This paper presents a solution to the problem of the search and retrieval digital tagged content in heterogeneous learning object repositories through architecture for intelligent retrieval of educational content in heterogeneous environments AIREH framework. This architecture unifies the search and retrieval of objects, thus facilitating the personalised learning search process by filtering and properly classifying learning objects retrieved for an approach for semantic-aware learning content retrieval based on abstraction layers between the repositories and the search clients. The use of federated databases techniques by using an organisation of agents allows those agents to work in a coordinated manner to solve a common problem, allowing the agents to adapt to the constantly changing environment users, content repositories, etc.. Combining a complete agent-based architecture that implements the concept of federated search along with IR technologies may help organising and sorting search results in a meaningful way for educational content.