Vivian F. López

Biomedic Organizations: An intelligent dynamic architecture for KDD

The application of information technology in the field of biomedicine has become increasingly important over the last several years. This study presents the Intelligent Biomedic Organizations (IBOs) model, an intelligent dynamic architecture for knowledge discovery in biomedical databases. It involves an organizational model specially designed to support medical personnel in their daily tasks and to establish an innovative intelligent system to make classifications and predictions with huge volumes of information. IBO is based on a multi-agent architecture with Web service integration capability. The core of the system is a type of agent that integrates a novel strategy based on a case-based planning mechanism for automatic reorganization. This agent proposes a new reasoning agent model, where the complex processes are modeled as external services. In this sense, the agents act as coordinators of Web services that implement the four stages of the case-based planning cycle. The multi-agent system has been implemented in a real scenario to classify leukemia patients, and the classification strategy includes services such as a novel ESOINN neural network and statistical methods to analyze patient data. The results obtained are presented within this paper and demonstrate the effectiveness of the proposed organizational model.

A model for multi-label classification and ranking of learning objects

Highlights? We use multi-label classification methods for search tagged learning objects (LOs). ? The methodology illustrates the task of multi-label mapping of LOs into types queries. ? We use of multi-label classification algorithm using only the LOs features. ? We also did experiments using web classification with text features. ? Multi-label classifiers such as RAKEL was very effective. This paper describes an approach that uses multi-label classification methods for search tagged learning objects (LOs) by Learning Object Metadata (LOM), specifically the model offers a methodology that illustrates the task of multi-label mapping of LOs into types queries through an emergent multi-label space, and that can improve the first choice of learners or teachers. In order to build the model, the paper also proposes and preliminarily investigates the use of multi-label classification algorithm using only the LO features. As many LOs include textual material that can be indexed, and such indexes can also be used to filter the objects by matching them against user-provided keywords, we then did experiments using web classification with text features to compare the accuracy with the results from metadata (LO feature).

Using Association Analysis of Web Data in Recommender Systems

The numerous web sites existing nowadays make available more information than a user can manage. Thus, an essential requirement of current web applications is to provide users with instruments for personalized selective retrieval of web information. In this paper, a procedure for making personalized recommendations is proposed. The method is based on building a predictive model from an association model of Web data. It uses a set of association rules generated by a data mining algorithm that discovers knowledge in an incremental way. These rules provide models with relevant patterns that minimize the recommendation errors.

Multi-label Classification for Recommender Systems

Multi-label classification groups a set of supervised learning methods producing models capable of classifying examples in more than one class. These methods have been applied in diverse fields; however, the field of recommender systems has been hardly explored. In this work, books’ recommendation data are used to evaluate the behavior of the main multi-label classification methods in this application domain. The experiments carried out demonstrated their suitability to provide reliable recommendations and to avoid the grey sheep 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.

Self Organized Dynamic Tree Neural Network

Cluster analysis is a technique used in a variety of fields. There are currently various algorithms used for grouping elements that are based on different methods including partitional, hierarchical, density studies, probabilistic, etc. This article will present the SODTNN, which can perform clustering by integrating hierarchical and density-based methods. The network incorporates the behavior of self-organizing maps and does not specify the number of existing clusters in order to create the various groups.

Semantic Web Mining for Book Recommendation

A current strategy for improving sales as well as customer satisfaction in the e-commerce field is to provide product recommendation to users. The increasing acceptance of web recommender systems is mainly due to the advances achieved in the intensive research carried out for several years. However, in spite of these improvements, recommender systems still present some important drawbacks that prevent from satisfying entirely their users. In this work, a methodology that combines an association rule mining method with the definition of a domain-specific ontology is proposed in order make efficient book recommendations.

Taranis: Neural networks and intelligent agents in the early warning against floods

The following article proposes the implementation of a system based on neural networks of CounterPropagation type and intelligent agents for analysis and assessment of the risk of flood caused by rain, in addition to the implementation of agents in mobile devices for the presentation of alerts. Because as is known, natural phenomena have always existed, but in recent years as a result of global warming we have seen that floods are becoming more frequent, which has forced the creation of specialized agencies and intelligent mechanisms to prevent the loss of human lives due to these phenomena.

Data mining for grammatical inference with bioinformatics criteria

Highlights? We describe a novel data mining process that combines algorithms of genomic. ? Data mining procedure generate grammatical structures of a specific language. ? These structures are converted to Context-Free Grammars. ? We have proposed a new method of automatic generation of syntactic categories. ? We used a tool which allows measuring the complexity of grammar automatically. In this work a novel data mining process is described that combines hybrid techniques of association analysis and classical sequentiation algorithms of genomics, to generate grammatical structures of a specific language. Subsequently, these structures are converted to Context-Free Grammars. Initially the method applies to context-free languages with the possibility of being applied to other languages: structured programming, the language of the book of life expressed in the genome and proteome and even the natural languages. We used an application of a compilers generator system that allows the development of a practical application within the area of grammarware, where the concepts of the language analysis are applied to other disciplines, like bioinformatic. The tool allows measuring the complexity of the obtained grammar automatically from textual data.

A SOMAgent for machine translation

This work describes a method that uses artificial neural networks, specially a self-organising map (SOM), to determine the correct meaning of a word. By using a distributed architecture, we take advantages of the parallelism in the different levels of the natural language processing system, for modeling a community of conceptually autonomous agents. Every agent has an individual representation of the environment, and they are related through the coordinating effect of communication between agents with partial autonomy. The aim of our linguistic agents is to participate in a society of entities with different skills, and to collaborate in the interpretation of natural language sentences in a prototype of an automatic German-Spanish translator.