José Ranilla - IEEE Xplore Author Profile

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Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust ...Show More
High Performance Computing Clusters (HPCCs) are essential platforms for solving up-to-date challenges through parallel and distributed applications. Nevertheless, HPCCs have an important economic and environmental impact owing to the large amounts of energy required for their operation. In this work, an improved version of our EECluster software focused on reducing the operating costs and emission...Show More
A quantum procedure for testing the commutativity of a finite dimensional algebra is introduced. This algorithm, based on Grover's quantum search, is shown to provide a quadratic speed-up (when the number of queries to the algebra multiplication constants is considered) over any classical algorithm (both deterministic and randomized) with equal success rate and shown to be optimal among the class ...Show More
A fuzzy rule-based classifier is proposed in this paper where the number of rules in the knowledge base that are fired when an object is classified is anti-monotone with respect to the prior probability of its class. This classifier is intended to secure an equilibrium between accuracy and energy consumption, which is critical in battery operated embedded devices. The method is compared to legacy ...Show More
In this work, a classifier that jointly optimises the expected total classification cost and the energy consumption is presented. A numerical study is provided, where different alternatives are implemented on a hearing aid. Our proposal is capable of automatically classifying the acoustic environment that surrounds the user and choosing the parameters of the amplification that are best adapted to ...Show More
Today, High Performance Computing clusters (HPC) are an essential tool owing to they are an excellent platform for solving a wide range of problems through parallel and distributed applications. Nonetheless, HPC clusters consume large amounts of energy, which combined with notably increasing electricity prices are having an important economical impact, forcing owners to reduce operation costs. In ...Show More
A computationally efficient system for sound environment classification in digital hearing aids is presented in this paper. The goal is to automatically classify three different listening environments: “speech,” “music,” and “noise.” The system is designed considering the computational limitations found in such devices. The proposed algorithm is based on a novel set of heuristically designed featu...Show More
As an alternative to approaches based on entropy and information gain, we describe a system that uses a measure called the impurity level. The learning algorithm based on this measure, which we call FARNI, first induces fuzzy decision trees by using an impurity-level extension for selecting the best branch. This is similar to the way C4.5 and ARNI induce selections for crisp databases. Once FARNI ...Show More
Text categorization, which consists of automatically assigning documents to a set of categories, usually involves the management of a huge number of features. Most of them are irrelevant and others introduce noise which could mislead the classifiers. Thus, feature reduction is often performed in order to increase the efficiency and effectiveness of the classification. In this paper, we propose to ...Show More
We propose a set of (machine learning) ML-based scoring measures for conducting feature selection. We've tested these measures on documents from two well-known corpora, comparing them with other measures previously applied for this purpose. In particular, we've analyzed which measure obtains the best overall classification performance in terms of properties such as precision and recall, emphasizin...Show More
In this paper a parallel algorithm to solve linear equation systems is presented. This method, known as Neville elimination, is appropriate especially for the case of a totally positive matrix (all its minors are nonnegative). We prove that this algorithm is cost-optimal for a given parallel implementation of Neville elimination, in which the coefficient matrix is rowwise stripe-partitioned among ...Show More