Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Coming soon!
Published:
portfolio
TORRES project (2023-2025)
Traffic prOcessing foR uRban EnvironmentS (2023-2025)
Analysis of ramp events in power systems (2024-…)
Forecasting wind power ramp events using imbalanced time series (2024-…) 
Multiroofs (2026)
Multifunctional Roofscapes for smart, green and just urban densification (2026) 
publications
Multiclassifier for diagnosing childhood hypertension by combining genetic algorithms with diversity measures
Published in Book: Experiencias en la modelación de la toma de decisiones en la salud humana, medio ambiente y desarrollo humano, 2015
This study proposes a genetic algorithm based multiclassifier that combines 18 base classifiers and 13 diversity measures to improve the early diagnosis of hypertension in children aged 10 to 12 years, achieving greater accuracy than individual classifiers.
Machine Learning algorithms for Splice Sites classification in genomic sequences
Published in Revista Cubana de Ciencias Informáticas, 2015
This study addresses the prediction of true and false splice sites in DNA sequences by applying and comparing multiple machine learning algorithms in WEKA, concluding that Bayesian methods provide the best classification performance based on measures such as true positive rate and ROC area.
Genetic Algorithms with diversity measures to build classifiers systems
Published in Investigación Operacional, 2015
This paper proposes a modified genetic algorithm that uses diversity measures to select the most diverse and accurate combination of classifiers, demonstrating its effectiveness through applications in two different domains.
Diversity Measures for Building Multiple Classifier Systems Using Genetic Algorithms
Published in Computacion y Sistemas, 2016
In this paper we present the different diversity measures that exist in the literature to decide if a set of classifiers is diverse, aspect that is very important in the creation of ensembles.
Building multi-classifiers systems with Ant Colony Optimization
Published in Investigación Operacional, 2017
This paper presents an Ant Colony Optimization-based approach to construct efficient and diverse ensemble of classifiers, optimizing classifier selection to enhance accuracy while maintaining model simplicity.
Diagnosis of the hypertension risk in children applying neurofuzzy systems
Published in Revista Cubana de Informática Médica, 2019
This paper applies neurofuzzy systems to diagnose hypertension risk in children, showing that the NSLV algorithm generates effective diagnostic rules from clinical data to support early detection.
Diversity-based selection of learning algorithms: a bagging approach
Published in Investigacion operacional, 2021
This paper proposes a modified bagging algorithm that integrates diverse learning algorithms and optimizations to improve classifier ensembles, demonstrating superior performance over state-of-the-art methods and validating its effectiveness in real biochemical applications.
New Diversity Measures Based on the Coverage and Similarity of the Classification
Published in Computación y Sistemas, 2021
This paper proposes two new diversity measures for classifier ensembles, based on coverage and similarity, to improve ensemble accuracy and provides experimental analysis showing their effectiveness and correlation with existing diversity metrics.
Measuring wind turbine health using fuzzy-concept-based drifting models
Published in Renewable energy, 2022
This paper introduces two fuzzy concept–based methods for wind turbine health monitoring, enabling interpretable detection of performance degradation by analyzing changes and drifts in power production under varying environmental conditions.
Online learning of windmill time series using Long Short-term Cognitive Networks
Published in Expert Systems with Applications, 2022
This paper demonstrates that Long Short-term Cognitive Networks (LSTCNs) provide faster and more accurate online forecasting of windmill time series than traditional RNN-based models, making them well-suited for real-time windmill monitoring and maintenance applications.
Optimization of Plasma-Assisted Surface Treatment for Adhesive Bonding via Artificial Intelligence.
Published in Proceedings in Engineering Mechanics, 2022
This paper demonstrates that Bayesian optimization with Gaussian process models can effectively support adhesive bonding process optimization, achieving similar joint strength as expert-driven methods with up to 40% less budget and reduced production costs, while ensuring robust and durable bonds.
Multi-objective Hyperparameter Optimization with Performance Uncertainty.
Published in Communications in Computer and Information Science, 2022
This paper proposes a hybrid multi-objective hyperparameter optimization method that combines Tree-structured Parzen Estimators with Gaussian Process Regression under heterogeneous noise, improving performance under uncertainty compared to stand-alone approaches.
A survey on multi-objective hyperparameter optimization algorithms for machine learning
Published in Artificial Intelligence Review, 2022
This article systematically surveys multi-objective hyperparameter optimization methods from 2014–2020, categorizing algorithms, evaluating comparison metrics, and highlighting future research directions.
Multi-objective optimization of adhesive bonding process in constrained and noisy settings
Published in Communications in Computer and Information Science, 2023
This study leverages Gaussian Process and Logistic Regression models within a Bayesian optimization framework to handle multi-objective, constrained, and uncertain adhesive bonding optimization, achieving efficient solutions with minimal experimental effort.
Bayesian multi-objective optimization of process design parameters in constrained settings with noise: an engineering design applications
Published in Engineering with computers, 2024
This study applies advanced learning and Bayesian optimization to efficiently identify Pareto-optimal adhesive bonding process parameters under multiple noisy objectives and strict experimental constraints, reducing the need for costly physical testing.
Constrained Bayesian Optimization: a review
Published in IEEE Access, 2024
This paper reviews the literature on single-objective constrained Bayesian optimization, classifying methods by metamodels, acquisition functions, and constraint handling, while outlining real-world applications, limitations, and future research directions.
Calibration of vehicular traffic simulation models by local optimization
Published in Transportation, 2025
This paper proposes a novel stochastic, simulation-based calibration method that uses only traffic count data to improve the accuracy, scalability, and real-time applicability of traffic models, demonstrating a 16% accuracy gain over state-of-the-art methods in a Brussels case study.
A direct classification approach for reliable wind ramp event forecasting under severe class imbalance
Published in Electric Power Systems Research, 2026
The paper proposes a multivariate time series classification method with imbalance-aware preprocessing and ensemble learning to improve Wind Power Ramp Event forecasting, achieving over 85% accuracy and 88% weighted F1 score on real-world wind power data.
software
talks
Majority vote modification to consider the classifier experience on multi-classifier systems
Published:
Encore Abstract: Online learning of windmill time series using Long Short-term Cognitive Networks
Published:
Multi-objective optimization of adhesive bonding process in constrained and noisy settings
Published:
Integration of simulation and machine learning to predict the traffic in the event of disruptions
Published:
Strategic NPI Optimisation: Solving a MOOP to Mitigate Healthcare Burden and Educational Loss
Published:
teaching
Data Structure and Algorithms I
Bachelor course - Computer Science & Engineering Informatic, Central University of Las Villas, Faculty of Mathematics, Physics, and Informatics, 2017
Hands-on experience in designing, analyzing, and implementing efficient algorithms and data structures to solve complex computational problems.
Introduction to Java programming
Bachelor course - Biology, Central University of Las Villas, Faculty of Agricultural Sciences, 2017
Introduction to Java programming with a focus on applying object-oriented concepts and computational methods to solve biological problems.
Informatics
Bachelor course - Tourism, Central University of Las Villas, Faculty of Industrial Engineering and Tourism, 2018
Introduction to Informatics fundamentals with practical training in Microsoft Office tools (Word, Excel, PowerPoint, Access), database design, and internet applications for information processing and management.
Data Structure and Algorithms II
Bachelor course - Computer Science & Engineering Informatic, Central University of Las Villas, Faculty of Mathematics, Physics, and Informatics, 2018
Hands-on experience in designing, analyzing, and implementing efficient algorithms and data structures to solve complex computational problems.
Introduction to Information Systems
Master course - Master of Management, Hasselt University, Faculty of Business Economics, 2020
Study of business information systems with a focus on Business Intelligence, data management, and practical applications in spreadsheets, while exploring digital commerce, security risks, and decision-support tools.
Machine Learning for managers
Guest lecture - Program Master of Management, Hasselt University, Faculty of Business Economics, 2026
Lecture designed to give business students a practical, non-technical grounding in ML concepts, covering the main types of supervised learning through intuitive real-world examples. The lecture closes with a business-oriented lens on ROI, ethical considerations, and model selection trade-offs, reinforcing that models are decision-support tools rather than replacements for human judgment.