Proyectos

Applications - Aerobiology

Aplicación de modelos de clasificación para el área de Aerobiología.

Identificación de rodales de mala hierba con técnicas de teledetección, clasificación de cubiertas en cultivos.

Collaborator Institution: Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund and Spanish Ministry of Education and Science. Institute for Sustainable Agriculture, CSIC, Spain.
Main targeted goal: Avoid the soil erosion with cover crops between rows, control strategies for herbicides, to address the presence or absence of cover crops, detection of weed infested fields, Yield prediction.
Methodological subfield: Supervised Classification, Logistic Regression, Artificial Neural Networks, Evolutionary Computation, Metaheuristics, Decision-making process, agricultural remote sensing, crop parameters in precision farming.
Methodological contribution: New classification and regresion algorithms, new methods to analyze multispectral imagery, new artificial neural netwoks models.
Impact in domain field: New models for stimating parameters in precision farming, more accuracy in precision agriculture for economic savings and production increases.

Aplicación de modelos de clasificación para el área de Agronomía.

Collaborator Institution: Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund, “Junta de Andalucia” (Spain) and Spanish Ministry of Education and Science. Transplant Unit of the Reina Sofia Hospital, Córdoba (Spain) and Astellas Pharma Company.
Main targeted goal: Computational tools for the decision-making in liver transplantation.
Methodological subfield: Supervised Classification, Logistic Regression, Artificial Neural Networks, Evolutionary Computation, Metaheuristics, Decision-making process, Rule-based Systems.
Methodological contribution: New Artificial Neural Networks models with projection and kernel basis functions, Memetic Evolutionary Algorithms for unbalanced data, Hybrid models, Rule-based Systems and Oversampling techniques.
Impact in domain field: New decision support system which lead to making the correct decision about receptor choice based on efficient and impartial criteria (principles of justice, efficiency and equity). A Rule-based system that aids medical experts in making decisions about the allocation of liver transplants.

Aplicación de modelos de clasificación para realizar Distribución en Planta.

Collaborator Institution: Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund and “Junta de Andalucia” (Spain). Department of Management and Quantitative Methods, ETEA, Business Administration Faculty, Spain.
Main targeted goal: Prediction and classification of economic crises, economic growth and allocating resources to countries.
Methodological subfield: Supervised Classification, Logistic Regression, Artificial Neural Networks, Evolutionary Computation, Clustering, decision making...
Methodological contribution: New models for the use of the macroeconomic variables and economical interpretation. New methods with adequacy, adaptability and interpretability in analysing dichotomous classification problems in a very complex environment.
Impact in domain field: More accuracy in the Research and Development performance classification model in the European Union Member States, new methods for detecting and predicting banking crises. Decision making and economic management in livestock enterprises.

Collaborator Institution: Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund, “Junta de Andalucia” (Spain) and Spanish Ministry of Education and Science. Department of Food Science and Technology of University of Córdoba (Spain).
Main targeted goal: Design mathematical models for predicting the growth limits in microbiology field with a high confidence level. Implementation of risk management measures in food industries. This suppose a breakthrough in guaranteeing microbial food safety.
Methodological subfield: Supervised Classification, Logistic Regression, Artificial Neural Networks, Evolutionary Computation, Metaheuristics, Decision-making process.
Methodological contribution: New Artificial Neural Networks models with projection and kernel basis functions, Memetic Evolutionary Algorithms for unbalanced data, Hybrid models and Oversampling techniques.
Impact in domain field: More accurate predictions and to provide additional information regarding the variability of microbial responses under limiting conditions. Help to predictive modelers to better define the growth boundaries of microorganisms and to model the microbial variability associated with these conditions.