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Ignacio Rojas
Researcher at University of Granada
Publications - 334
Citations - 6315
Ignacio Rojas is an academic researcher from University of Granada. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 36, co-authored 321 publications receiving 5459 citations. Previous affiliations of Ignacio Rojas include ETH Zurich & Helsinki University of Technology.
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Proceedings ArticleDOI
Towards general forms of interval type-2 fuzzy logic systems
TL;DR: This work aims to introduce the basic structure of those general forms of interval type-2 fuzzy logic systems (gfIT2FLSs) which use IT2FSs that are not equivalent to IVFSs and can have non-convex secondary grades, and presents the mathematical tools to define the inference engine.
Journal ArticleDOI
Evaluating fluorescence illumination techniques for skin lesion diagnosis
TL;DR: This work validates the fluorescence illumination technique for skin cancer diagnosis, indicating concrete image processing techniques that best target the diagnostic problem, and shows that the GA approach obtains the best classification results.
Book ChapterDOI
Handling Displacement Effects in On-Body Sensor-Based Activity Recognition
TL;DR: Two fusion methods are compared to evaluate the importance of decoupling the combination process at feature and classification levels under realistic sensor configurations and reveal that the aggregation of sensor-based decisions may overcome the difficulties introduced by the displacement.
Book ChapterDOI
On Selecting the Best Pre-processing Method for Affymetrix Genechips
TL;DR: Five common pre-processing methods (RMA, GCRMA, MAS5, dChip and VSN) and two customized Loess methods are benchmarked in terms of data variability, similarity of data distributions and correlation coefficient among replicated slides in a variety of real examples.
Journal ArticleDOI
An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of Artificial Neural Networks
TL;DR: This work presents a new framework to be used in Artificial Neural Networks for the task of predicting functional relationships between proteins through the integration of evidences from heterogeneous data sources by selecting smaller representative/non-random subsets from the original data set selected for ANN optimization process.