A5063964437- Neural Networks and Applications
- Face and Expression Recognition
- Imbalanced Data Classification Techniques
- Metaheuristic Optimization Algorithms Research
- Time Series Analysis and Forecasting
- Evolutionary Algorithms and Applications
- Machine Learning and Data Classification
- Energy Load and Power Forecasting
- Complex Systems and Time Series Analysis
- Anomaly Detection Techniques and Applications
- Machine Learning and ELM
- Hydrological Forecasting Using AI
- Spectroscopy and Chemometric Analyses
- Fuzzy Logic and Control Systems
- Stock Market Forecasting Methods
- Remote Sensing in Agriculture
- Organ Transplantation Techniques and Outcomes
- Text and Document Classification Technologies
- Data Mining Algorithms and Applications
- Wind Energy Research and Development
- Artificial Intelligence in Healthcare
- Smart Agriculture and AI
- Ocean Waves and Remote Sensing
- Microtubule and mitosis dynamics
- Meteorological Phenomena and Simulations
University of Córdoba
2016-2025
Instituto Maimónides de Investigación Biomédica de Córdoba
2024-2025
NOAA Atlantic Oceanographic and Meteorological Laboratories
2024
Columbia University
2021-2023
University of California, Davis
2017-2023
Universidade do Porto
2023
University of Miami
2015-2021
Instituto Superior de Formación Docente Salomé Ureña
2020
Universidad Politécnica de Madrid
2006-2007
Instituto de Agricultura Sostenible
2007
Ordinal regression problems are those machine learning where the objective is to classify patterns using a categorical scale which shows natural order between labels. Many real-world applications present this labelling structure and that has increased number of methods algorithms developed over last years in field. Although ordinal can be faced standard nominal classification techniques, there several specifically benefit from ordering information. Therefore, paper aimed at reviewing state...
The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have similar spectral response and cropping pattern. In such cases, identification could be improved by combining object-based image analysis advanced machine learning methods. this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector (SVM) multilayer perceptron (MLP) neural...
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: high correct classification rate level and for each class. last objective is not usually optimized in classification, but considered here given the need obtain precision class real problems. To solve this machine learning problem, we use Pareto-based multiobjective optimization methodology based on memetic evolutionary...
Atmospheric low-visibility events are usually associated with fog formation. Extreme deeply affect the air and ground transportation, airports motor-road facilities causing accidents traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many formation prediction problems. The problem can be formulated either as a regression or classification task, which has an impact on type of ML approach used quality predictions obtained. In this paper we carry out...
The imbalanced nature of some real-world data is one the current challenges for machine learning researchers. One common approach oversamples minority class through convex combination its patterns. We explore general idea synthetic oversampling in feature space induced by a kernel function (as opposed to input space). If matches underlying problem, classes will be linearly separable and synthetically generated patterns lie on region. Since not directly accessible, we use empirical (EFS) (a...
Thickness of the melanoma is most important factor associated with survival in patients melanoma. It commonly reported as a measurement depth given millimeters (mm) and computed by means pathological examination after biopsy suspected lesion. In order to avoid use an invasive method estimation thickness before surgery, we propose computational image analysis system from dermoscopic images. The proposed feature extraction based on clinical findings that correlate certain characteristics...
Abstract Processive transport by the microtubule motor cytoplasmic dynein requires regulated assembly of a dynein-dynactin-adapter complex. Interactions between and dynactin were initially ascribed to intermediate chain N-terminus subunit p150 Glued . However, recent cryo-EM structures have not resolved this interaction, questioning its importance. The also interacts with Nde1/Ndel1, which compete for binding. We reveal that is critical evolutionarily conserved hub Ndel1, latter recruits...
This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable (XAI) refers to transparency AI systems explanations their predictions decision-making processes, contribute improve prediction accuracy enhance trust systems. The focus this relies on interpretability challenges ordinal classification problems within weather forecasting. Ordinal involves predicting...
This paper proposes a hybrid multilogistic methodology, named logistic regression using initial and radial basis function (RBF) covariates. The process for obtaining the coefficients is carried out in three steps. First, an evolutionary programming (EP) algorithm applied, order to produce RBF neural network (RBFNN) with reduced number of transformations simplest structure possible. Then, attribute space (or, as commonly known literature, covariate space) transformed by adding nonlinear input...
The classification of patterns into naturally ordered labels is referred to as ordinal regression or classification. Usually, this setting by nature highly imbalanced, because there are classes in the problem that a priori more probable than others. Although standard over-sampling methods can improve minority classification, they tend introduce severe errors terms label scale, given do not take ordering account. A specific method developed paper for first time order performance machine...
Kinesin-5 motors organize mitotic spindles by sliding apart microtubules. They are homotetramers with dimeric motor and tail domains at both ends of a bipolar minifilament. Here, we describe regulatory mechanism involving direct binding between its fundamental role in microtubule sliding. tails decrease microtubule-stimulated ATP-hydrolysis specifically engaging the nucleotide-free or ADP states. Cryo-EM reveals that stabilizes an open domain ATP-active site. Full-length undergo slow...
Time-series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for and standard method. However, these do not take different subsequences each into account, which can be used better compare time-series objects dataset. In this article, we propose novel technique consisting two stages. first step, least-squares polynomial segmentation procedure applied series, based on growing...
This paper presents long- and short-term analyses predictions of dammed water level in a hydropower reservoir. The long-term analysis was carried out by using techniques such as detrended fluctuation analysis, auto-regressive models, persistence-based algorithms. On the other hand, reservoir modeled prediction problem, where machine learning regression were studied. A set including different types neural networks, Support Vector regression, or Gaussian processes tested. Real data from...