Waldo Fajardo Contreras

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This paper presents the development of BioMen (Biological Management Executed over Network), an Internet-managed system. By using service ontologies, the user is able to perform services remotely from a web browser. The services are managed by means of a Multi-Agent System, i.e. an Input/Output system, which interacts with the web server. In addition,(More)
present a new associative memory model that stores arbitrary bipolar patterns without the problems we can find in other models like BAM or LAM. After identifying those problems we show the new memory topology and we explain its learning and recall stages. Mathematical demonstrations are provided to prove that the new memory model guarantees the perfect(More)
In this article we present the so-called continuous classifying associative memory, able to store continuous patterns avoiding the problems of spurious states and data dependency. This is a memory model based on our previously developed classifying associative memory, which enables continuous patterns to be stored and recovered. We will also show that the(More)
Automatic extraction of frequent repeated patterns in music material is an interesting problem. This paper presents an effective approach of unsupervised frequent pattern discovery method from symbolic music sources. Patterns are discovered even if they are transposed. Experiments on some songs suggest that our approach is promising, specially when dealing(More)
This paper presents the development of XKey, a tool for generating taxonomical identification keys by means of decision tree construction. The tool is based on an XML standard for the representation of general taxonomical information, which makes it ideal for different fields of application. The article analyses the problem by examining the adaptation of(More)
OBJECTIVE Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can(More)
The discovery of frequent musical patterns (motifs) is a relevant problem in musicology. This paper introduces an unsupervised algorithm to address this problem in symbolically-represented musical melodies. Our algorithm is able to identify transposed patterns including exact matchings, i.e., null transpositions. We have tested our algorithm on a corpus of(More)