A5086980043- Neural Networks and Applications
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Face and Expression Recognition
- Imbalanced Data Classification Techniques
- Spectroscopy and Chemometric Analyses
- Time Series Analysis and Forecasting
- Organ Transplantation Techniques and Outcomes
- Machine Learning and Data Classification
- Energy Load and Power Forecasting
- Complex Systems and Time Series Analysis
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
- Fuzzy Logic and Control Systems
- Liver Disease and Transplantation
- Text and Document Classification Technologies
- Advanced Multi-Objective Optimization Algorithms
- Hydrological Forecasting Using AI
- Remote Sensing in Agriculture
- Stock Market Forecasting Methods
- Liver Disease Diagnosis and Treatment
- Medical History and Innovations
- Advanced Chemical Sensor Technologies
- Fault Detection and Control Systems
- Advanced Statistical Methods and Models
University of Córdoba
2016-2025
Instituto Maimónides de Investigación Biomédica de Córdoba
2022-2025
Cordoba University
2003-2019
Vall d'Hebron Hospital Universitari
2006
Hospital Materno-Infantil
1992-2002
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 presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is recent paradigm in evolutionary computation that allows the effective modeling of environments. Although theoretically, single with sufficient number neurons hidden layer would suffice to solve any problem, practice many real-world problems are too hard construct appropriate them. In such problems, ensembles successful alternative. Nevertheless, design complex task. this...
This paper presents COVNET, a new cooperative coevolutionary model for evolving artificial neural networks. is based on the idea of coevolving subnetworks that must cooperate to form solution specific problem, instead complete The combination this part process. best combinations be evolved together with coevolution subnetworks. Several subpopulations coevolve cooperatively and genetically isolated. individual every subpopulation are combined whole different approach from most current models...
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...
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...
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from Spanish multicenter study (Model Allocation of Donor and Recipient España [MADR-E]). The aim is to test the ANN-based methodology different European health care system order validate it. An ANN was designed using cohort patients King's College Hospital (KCH; n = 822). trained tested KCH pairs both 3- 12-month survival models. End points were...
This paper presents a hybrid evolutionary algorithm (EA) to solve nonlinear-regression problems. Although EAs have proven their ability explore large search spaces, they are comparatively inefficient in fine tuning the solution. drawback is usually avoided by means of local optimization algorithms that applied individuals population. The use procedures called algorithms. On other hand, it well known clustering process enables creation groups (clusters) with mutually close points hopefully...