- Engineer and Ph.D. in Computer Science specializing in Artificial Intelligence from the University of A Coruña since ... moreEngineer and Ph.D. in Computer Science specializing in Artificial Intelligence from the University of A Coruña since 2005 and 2010, respectively, has built a remarkable trajectory throughout his career during which he has participated in numerous research projects and enjoyed scholarships from both public and private entities.
Since 2008, he has held various teaching positions, including predoctoral fellow (2008-2009), part-time professor (2010-2012, 2013-2015), Assistant Professor (2015-2020), Lecturerr (2020-2021), and Associate Professor (2021-). It is noteworthy that during the period from 2010 to 2015, he combined these positions with private activities in different research projects in various institutions.
Starting from March 16, 2023, he also takes on the role of coordinator for the Master's in Computer Science at the University of A Coruña, solidifying his commitment to education and program management.
His curriculum reflects a strong focus on Applied Artificial Intelligence and Evolutionary Computing, with over 54 contributions in various journals, 37 of which are indexed in the JCR and 16 in the first quartile. This researcher has left a significant impact with over 1000 citations and an h-index of 14 in the last 5 years.
Furthermore, he has undertaken research stays in highly prestigious groups such as the University of York (Julian H. Miller, 2008), the University of Aveiro (Jose Luis Oliveira, 2010), the Università degli Studi di Parma (Stefano Cagnoni, 2011), and the University of Granada (Óscar Cordón, 2017), enriching his experience and collaborations.
Additionally, his commitment to knowledge transfer is evident in various software registrations, patents, and research projects.Recognized with two six-year terms of valuable research recognition by ANECA, this researcher has a distinguished teaching career, with a recognized five-year period spanning from 2008 to 2018. He has successfully supervised 26 undergraduate projects, 9 master's thesis projects, and two doctoral theses, while currently guiding five doctoral theses at various stages of development.
Moreover, he actively serves as a reviewer for various JCR journals and high-level conferences, such as Biomedical Signal Processing and Control, Soft Computing, IEEE Journal of Biomedical and Health Informatics, Artificial Intelligence in Medicine, Neurocomputing, among others.
As a member of the Department of Computer Science and Information Technologies, specifically as part of the RNASA-IMEDIR research group, he is also affiliated with the Research Center for Information and Communication Technologies (CITIC-UDC) and the Institute of Biomedical Research of A Coruña (INIBIC). Since 2005, he has been a member of the Professional College of Computer Engineers of Galicia, advocating for the defense of the profession.edit - Julian Doradoedit
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current... more
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application.
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This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this... more
This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen data.
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Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and... more
Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.
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Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the polysomnogram, which is a collection of different signals recorded during sleep. After its... more
Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be a high-time-consuming task and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is made in order to determine the convenience of using more than one signal simultaneously as input. This approach is similar to the one made in other problems although, additionally to those models, they were also used as parts of an ensemble model to check whether any useful information can be extracted from processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called sleep-EDF-expanded, which is the most commonly used dataset as benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but it overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67% and a Cohen’s kappa value over 0.84 compared to human experts.
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Nowadays, among the Deep Learning works, there is a tendency to develop networks with millions of trainable parameters. However, this tendency has two main drawbacks: overfitting and resource consumption due to the low-quality features... more
Nowadays, among the Deep Learning works, there is a tendency to develop networks with millions of trainable parameters. However, this tendency has two main drawbacks: overfitting and resource consumption due to the low-quality features extracted by those networks. This paper presents a study focused on the scoring of sleeping EEG signals to measure if the increase of the pressure on the features due to a reduction of the number though different techniques results in a benefit. The work also studies the convenience of increasing the number of input signals in order to allow the network to extract better features. Additionally, it might be highlighted that the presented model achieves comparable results to the state-of-the-art with 1000 times less trainable and the presented model uses the whole dataset instead of the simplified versions in the published literature.
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Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a... more
Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of its main issues. The use of a validation dataset is a common alternative to prevent overfitting in many Machine Learning (ML) techniques, including GP. But, there is one key point which differentiates GP and other ML techniques: instead of training a single model, GP evolves a population of models. Therefore, the use of the validation dataset has several possibilities because any of those evolved models could be evaluated. This work explores the possibility of using the validation dataset not only on the training-best individual but also in a subset with the training-best individuals of the population. The study has been conducted with 5 well-known databases performing regression or classification tasks. In most of the cases, the results of the study point out to an improvement when the validation dataset is used on a subset of the population instead of only on the training-best individual, which also induces a reduction on the number of nodes and, consequently, a lower complexity on the expressions.
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A B S T R A C T The measurements of Near-Infrared (NIR) Spectroscopy, combined with data analysis techniques, are widely used for quality control in food production processes. This paper presents a methodology to optimize the calibration... more
A B S T R A C T The measurements of Near-Infrared (NIR) Spectroscopy, combined with data analysis techniques, are widely used for quality control in food production processes. This paper presents a methodology to optimize the calibration models of NIR spectra in four different stages in a sugar factory. The models were designed for quality monitoring, particularly °Brix and Sucrose, both common parameters in the sugar industry. A three stage optimization methodology, including pre-processing selection, feature selection and support vector machines regression metaparameters tuning, were applied to the spectral data divided by repeated cross-validation. Global models were optimized while endeavoring to ensure they are able to estimate both quality parameters with a single calibration, for the four steps of the process. The proposed models improve the prediction for the test set (unseen data) compared to previously published models, resulting in a more accurate quality assessment of the intermediate products of the process in the sugar industry.
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WSEAS TRANSACTIONS on INFORMATION SCIENCE & APPLICATIONS. Issue 3, Volume 4, March 2007. ISSN 1709-0832 http://www.wseas.org. A New Approach for Impacts Assessment of Urban Mobility, 439. Hichem Omrani, Luminita Ion-Boussier, Philippe... more
WSEAS TRANSACTIONS on INFORMATION SCIENCE & APPLICATIONS. Issue 3, Volume 4, March 2007. ISSN 1709-0832 http://www.wseas.org. A New Approach for Impacts Assessment of Urban Mobility, 439. Hichem Omrani, Luminita Ion-Boussier, Philippe Trigano, Computational Mechanics Applications in a Novel Grid Framework, 445. Michael M. Resch, Natalia Currle-Linde, Uwe Kuster, Introducing an Advanced Topic Map Software Tool Towards the Deployment of a TM-based System for Managing Melanoma Cases Images, 452. ...
ABSTRACT This paper presents a new model that can be framed into the so known as Computational Embryology or Natural Computation. This discipline takes the behaviour of biological cells and tries to adapt some of their characteristics to... more
ABSTRACT This paper presents a new model that can be framed into the so known as Computational Embryology or Natural Computation. This discipline takes the behaviour of biological cells and tries to adapt some of their characteristics to the artificial cells in order to solve computational problems. Besides the theoretical approach, some of the tests that were performed as preliminary implementation of such model are also presented. In this test, simple forms are generated using the development of the cell model
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Computer Morphogenesis in Self-Organizing Structures (9781599048499): Enrique Fernández-Blanco, Julián Dorado de la Calle: Book Chapters.
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Research Interests: Genetic Algorithms, Electroencephalography, Genetic Algorithm, Feature Selection, Artificial Neural Networks, and 11 moreClassifier System, Feature Extraction, Prior Knowledge, Electroencephalogram, Time Frequency Analysis, Pattern Classification, Search Space, Classification Algorithms, Frequency Analysis, Artificial Neural Network, and Classification Algorithm
Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard,... more
Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard, there exists a tiny subset of molecules in nature, named antioxidant proteins that may influence the aging process. However, testing every single protein in order to identify its properties is quite expensive and inefficient. For this reason, this work proposes a model, in which the primary structure of the protein is represented using complex network graphs that can be used to reduce the number of proteins to be tested for antioxidant biological activity. The graph obtained as a representation will help us describe the complex system by using topological indices. More specifically, in this work, Randić's Star Networks have been used as well as the associated indices, calculated with the S2SNet tool. In order to simulate the existing proportion of antioxidant proteins in nature, a dataset containing 1999 proteins, of which 324 are antioxidant proteins, was created. Using this data as input, Star Graph Topological Indices were calculated with the S2SNet tool. These indices were then used as input to several classification techniques. Among the techniques utilised, the Random Forest has shown the best performance, achieving a score of 94% correctly classified instances. Although the target class (antioxidant proteins) represents a tiny subset inside the dataset, the proposed model is able to achieve a percentage of 81.8% correctly classified instances for this class, with a precision of 81.3%.
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ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, few works describe techniques for developing recurrent networks. This... more
ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, few works describe techniques for developing recurrent networks. This work uses a genetic algorithm for automatic recurrent ANN development. This system has been applied to solve a well-known problem: classification of EEG signals from epileptic
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The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic... more
The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic detection of seizures by using the signals of long-term electroencephalographic (EEG) recordings. Due to the non-stationary character of EEG signals, the conventional methods of frequency analysis are not the best alternative to obtain good results in diagnostic purpose. The present work proposes a method of EEG signal analysis based on star graph topological indices (SGTIs) for the first time. The signal information, such as amplitude and time occurrence, is codified into invariant SGTIs which are the basis for the classification models that can discriminate the epileptic EEG records from the non-epileptic ones. The method with SGTIs and the simplest linear discriminant methods provide similar results to those previously published, which are based on the time-frequency analysis and artificial neural networks. Thus, this work proposes a simpler and faster alternative for automatic detection of seizures from the EEG recordings.
Research Interests: Cognitive Science, Support Vector Machines, Electroencephalography, Signal Analysis, Brain Mapping, and 11 moreFourier Analysis, Humans, Linear Discriminant Analysis, Brain Waves, Time Frequency Analysis, Seizures, Statistical Methods for Neuroscience, Frequency Analysis, Artificial Neural Network, Neurosciences, and Topological Indices
This paper presents a model in the Artificial Embryogene (AE) framework. The presented system tries to model the main functions of the biological cell model. The main part of this paper describes the Gene Regulatory Network (GRN) model,... more
This paper presents a model in the Artificial Embryogene (AE) framework. The presented system tries to model the main functions of the biological cell model. The main part of this paper describes the Gene Regulatory Network (GRN) model, which has a similar processing information capacity as Boole’s Algebra. This paper also describes how to use it to perform the Iris Classification problem which is a pattern classification problem. The aim of this work is to show that the model can solve this kind of problems.
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Page 1. 145 Chapter IX Artificial Cell Systems Based in Gene Expression Protein Effects Enrique Fernández-Blanco University of A Coruña, Spain Julian Dorado University of A Coruña, Spain Nieves Pedreira University of A Coruña, Spain ...
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Research Interests: Graph Theory, Computational Biology, Statistical Analysis, Entropy, Humans, and 15 moreCancer Diagnosis, Quantitative Structure Activity Relationship, Proteins, ROC Curve, Protein Sequence Analysis, Naive Bayes, Colon cancer, Shannon entropy, Amino Acid Sequence, Bayes Theorem, Theoretical Model, Area Under Curve, Biochemistry and cell biology, Topological Indices, and Molecular Sequence Data
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Research Interests: Bioinformatics, Machine Learning, Modeling, Drug Discovery, Network, and 18 moreGenetics of complex disease, Humans, QSAR, Complex network, Method, Drug Design, Quantitative Structure Activity Relationship, Graph, Current, Coronary heart disease, Graphs, Neoplasms, Drug Therapy, Molecular Conformation, Nervous System Diseases, Computational Method, Molecular Structure, and Pharmaceutical preparations
Research Interests: Bioinformatics, Machine Learning, Drug Discovery, Genetics of complex disease, Humans, and 10 moreDrug Design, Quantitative Structure Activity Relationship, Current, Coronary heart disease, Neoplasms, Drug Therapy, Molecular Conformation, Nervous System Diseases, Computational Method, and Pharmaceutical preparations
Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties... more
Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang-Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.
Research Interests: Artificial Intelligence, Machine Learning, Support Vector Machines, Feature Selection, Software, and 12 moreEnzymes, Markov chains, ROC Curve, Enzyme, Protein Function, Biological Process, Amino Acid Profile, Protein Classification, Enzymatic Activity, Physicochemical Properties, Biochemistry and cell biology, and Feature Subset Selection
Page 1. 145 Chapter IX Artificial Cell Systems Based in Gene Expression Protein Effects Enrique Fernández-Blanco University of A Coruña, Spain Julian Dorado University of A Coruña, Spain Nieves Pedreira University of A Coruña, Spain ...
Research Interests:
Page 1. 145 Chapter IX Artificial Cell Systems Based in Gene Expression Protein Effects Enrique Fernández-Blanco University of A Coruña, Spain Julian Dorado University of A Coruña, Spain Nieves Pedreira University of A Coruña, Spain ...
Research Interests:
Research Interests:
Research Interests: Graph Theory, Computational Biology, Statistical Analysis, Entropy, Humans, and 15 moreCancer Diagnosis, Quantitative Structure Activity Relationship, Proteins, ROC Curve, Protein Sequence Analysis, Naive Bayes, Colon cancer, Shannon entropy, Amino Acid Sequence, Bayes Theorem, Theoretical Model, Area Under Curve, Biochemistry and cell biology, Topological Indices, and Molecular Sequence Data
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ABSTRACT From the unicellular to the more complex pluricellular organism needs to process the signals from its environment to survive. The computation science has already observed, hat fact could be demonstrated remembering the artificial... more
ABSTRACT From the unicellular to the more complex pluricellular organism needs to process the signals from its environment to survive. The computation science has already observed, hat fact could be demonstrated remembering the artificial neural networks (ANN). This computation tool is based on the nervous system of the animals, but not only the nervous cells process information in an organism. Every cell has to process the development and functioning plan encoded at its DNA and every one of these cells executes this program in parallel with the others. Another interesting characteristic of natural cells is that they form systems that are tolerant to partial failures: small errors do not induce a global collapse of the system. The present work proposes a model that is based on DNA information processing, but adapting it to general information processing. This model can be based on a set of techniques called Artificial Embryogeny which adapts characteristics from the biological cells to solve different problems.
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ABSTRACT This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is... more
ABSTRACT This paper describes a new technique for signal classification by means of Genetic Programming (GP). The novelty of this technique is that no prior knowledge of the signals is needed to extract the features. Instead of it, GP is able to extract the most relevant features needed for classification. This technique has been applied for the solution of a well-known problem: the classification of EEG signals in epileptic and healthy patients. In this problem, signals obtained from EEG recordings must be correctly classified into their corresponding class. The aim is to show that the technique described here, with the automatic extraction of features, can return better results than the classical techniques based on manual extraction of features. For this purpose, a final comparison between the results obtained with this technique and other results found in the literature with the same database can be found. This comparison shows how this technique can improve the ones found.
Research Interests:
Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties... more
Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang-Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.
Research Interests: Artificial Intelligence, Machine Learning, Support Vector Machines, Feature Selection, Software, and 12 moreEnzymes, Markov chains, ROC Curve, Enzyme, Protein Function, Biological Process, Amino Acid Profile, Protein Classification, Enzymatic Activity, Physicochemical Properties, Biochemistry and cell biology, and Feature Subset Selection
Research Interests: Graph Theory, Computational Biology, Statistical Analysis, Entropy, Humans, and 15 moreCancer Diagnosis, Quantitative Structure Activity Relationship, Proteins, ROC Curve, Protein Sequence Analysis, Naive Bayes, Colon cancer, Shannon entropy, Amino Acid Sequence, Bayes Theorem, Theoretical Model, Area Under Curve, Biochemistry and cell biology, Topological Indices, and Molecular Sequence Data
Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard,... more
Aging and life quality is an important research topic nowadays in areas such as life sciences, chemistry, pharmacology, etc. People live longer, and, thus, they want to spend that extra time with a better quality of life. At this regard, there exists a tiny subset of molecules in nature, named antioxidant proteins that may influence the aging process. However, testing every single protein in order to identify its properties is quite expensive and inefficient. For this reason, this work proposes a model, in which the primary structure of the protein is represented using complex network graphs that can be used to reduce the number of proteins to be tested for antioxidant biological activity. The graph obtained as a representation will help us describe the complex system by using topological indices. More specifically, in this work, Randić's Star Networks have been used as well as the associated indices, calculated with the S2SNet tool. In order to simulate the existing proportion of antioxidant proteins in nature, a dataset containing 1999 proteins, of which 324 are antioxidant proteins, was created. Using this data as input, Star Graph Topological Indices were calculated with the S2SNet tool. These indices were then used as input to several classification techniques. Among the techniques utilised, the Random Forest has shown the best performance, achieving a score of 94% correctly classified instances. Although the target class (antioxidant proteins) represents a tiny subset inside the dataset, the proposed model is able to achieve a percentage of 81.8% correctly classified instances for this class, with a precision of 81.3%.
Research Interests:
The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic... more
The recognition of seizures is very important for the diagnosis of patients with epilepsy. The seizure is a process of rhythmic discharge in brain and occurs rarely and unpredictably. This behavior generates a need of an automatic detection of seizures by using the signals of long-term electroencephalographic (EEG) recordings. Due to the non-stationary character of EEG signals, the conventional methods of frequency analysis are not the best alternative to obtain good results in diagnostic purpose. The present work proposes a method of EEG signal analysis based on star graph topological indices (SGTIs) for the first time. The signal information, such as amplitude and time occurrence, is codified into invariant SGTIs which are the basis for the classification models that can discriminate the epileptic EEG records from the non-epileptic ones. The method with SGTIs and the simplest linear discriminant methods provide similar results to those previously published, which are based on the time-frequency analysis and artificial neural networks. Thus, this work proposes a simpler and faster alternative for automatic detection of seizures from the EEG recordings.
Research Interests: Cognitive Science, Support Vector Machines, Electroencephalography, Signal Analysis, Brain Mapping, and 11 moreFourier Analysis, Humans, Linear Discriminant Analysis, Brain Waves, Time Frequency Analysis, Seizures, Statistical Methods for Neuroscience, Frequency Analysis, Artificial Neural Network, Neurosciences, and Topological Indices
This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple... more
This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple structure generation and provide promising results with regard to its behaviour and applicability to more complex problems.
EEG classification is a research topic that has attracted a lot of interest in recent years, as proven by the large number of papers published. To accomplish this task, a lot of classification systems such as Support Vector Machines... more
EEG classification is a research topic that has attracted a lot of interest in recent years, as proven by the large number of papers published. To accomplish this task, a lot of classification systems such as Support Vector Machines (SVMs) or Artificial Neural Networks (ANNs) are used. However, Recurrent Artificial Neural Networks (RANNs) that allow using the previously computed results
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... University of A Coruña, Campus Elviña, A Coruña, Spain {efernandez,julian,jserantesv,drivero, juanra}@udc.es ... Complex Systems 4, 461476 (1990) 5. Hornby, GS, Pollack, JB: Creating high-level components with a generative... more
... University of A Coruña, Campus Elviña, A Coruña, Spain {efernandez,julian,jserantesv,drivero, juanra}@udc.es ... Complex Systems 4, 461476 (1990) 5. Hornby, GS, Pollack, JB: Creating high-level components with a generative representation for body-brain generation. ...
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This paper proposes a new evolutionary method for generating ANNs. In this method, a simple real-number string is used to codify both architecture and weights of the networks. Therefore, a simple GA can be used to evolve ANNs. One of the... more
This paper proposes a new evolutionary method for generating ANNs. In this method, a simple real-number string is used to codify both architecture and weights of the networks. Therefore, a simple GA can be used to evolve ANNs. One of the most interesting features of the technique presented here is that the networks obtained have been optimised, and they have a low number of neurons and connections. This technique has been applied to solve one of the most used benchmark problems, and results show that this technique can obtain better results than other automatic ANN development techniques.
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This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple... more
This paper presents a new model for computational embryology that mimics the behaviour of biological cells, whose characteristics can be applied to the solution of computational problems. The presented tests apply the model to simple structure generation and provide promising results with regard to its behaviour and applicability to more complex problems.
Research Interests:
Research Interests:
Data processing and the use of machine learning techniques make it possible to solve a wide variety of problems. The great disadvantage of using this type of technology is the enormous amount of computation involved. This is why we have... more
Data processing and the use of machine learning techniques make it possible to solve a wide variety of problems. The great disadvantage of using this type of technology is the enormous amount of computation involved. This is why we have tried to develop an architecture that makes the best possible use of the resources available on each machine. The growth of cloud computing and the rise of virtualization techniques have led to a development that allows these tasks to be carried out in a more optimized way.
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Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the most studied knowledge areas in the related literature. However, few of them have been applied to groups of UAVs. The use of swarms allows to speed up the flight... more
Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the most studied knowledge areas in the related literature. However, few of them have been applied to groups of UAVs. The use of swarms allows to speed up the flight time and, thus, reducing the operational costs. When combined with Artificial Intelligence (AI) algorithms, a single system or operator can control all aircraft while optimal paths for each one can be computed. In order to introduce the current situation of these AI-based systems, a review of the most novel and relevant articles was carried out. This review was performed in two steps: first, a summary of the found articles; second, a quantitative analysis of the publications found based on different factors, such as the temporal evolution or the number of articles found based on different criteria. Therefore, this review provides not only a summary of the most recent work but it gives an overview of the trend in the use of AI algorithms in UAV swarms for Path Planning problems. The AI techniques of the articles found can be separated into four main groups based on their technique: reinforcement Learning techniques, Evolutive Computing techniques, Swarm Intelligence techniques, and, Graph Neural Networks. The final results show an increase in publications in recent years and that there is a change in the predominance of the most widely used techniques.
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This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this... more
This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way to address two different issues: music representation into the latent space, and using this representation to make predictions of the future note events of the musical piece. This approach was trained with different songs of Handel. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions on unseen data.
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In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current... more
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use...
Research Interests: Computer Science, Artificial Intelligence, Analytical Chemistry, Machine Learning, Global Positioning System, and 14 moreMedicine, Activity Recognition, Sensors, Human Activity Recognition, Smartphones, Support vector machine, Wearable Computer, Svm, Gyroscope, Support Vector Machines (SVMs), Accelerometer, Dataset, Electrical And Electronic Engineering, and har
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Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest... more
Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at le...
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Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein... more
Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular structure. Therefore, the proposed solution involves the use of protein star graphs for the peptide sequence information encoding into specific topological indices calculated with S2SNet tool. The Quantitative Structure-Activity Relationship classification model obtained with Machine Learning techniques is able to predict new signaling peptides. The best classification model is the first signaling prediction model, which is based on eleven descriptors and it was obtained using the Support Vector Machines - Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel (RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown function from t...
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Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages. Although any polysomnography usually collects over a dozen of different signals, this particular problem has... more
Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages. Although any polysomnography usually collects over a dozen of different signals, this particular problem has been mainly tackled by using only the Electroencephalograms presented in those records. On the other hand, the other recorded signals have been mainly ignored by most works. This paper explores and compares the convenience of using additional signals apart from electroencephalograms. More specifically, this work uses the SHHS-1 dataset with 5,804 patients containing an electromyogram recorded simultaneously as two electroencephalograms. To compare the results, first, the same architecture has been evaluated with different input signals and all their possible combinations. These tests show how, using more than one signal especially if they are from different sources, improves the results of the classification. Additionally, the best models obtained for...
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This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is... more
This paper describes a new method for Symbolic Regression that allows to find mathematical expressions from a dataset. This method has a strong mathematical basis. As opposed to other methods such as Genetic Programming, this method is deterministic, and does not involve the creation of a population of initial solutions. Instead of it, a simple expression is being grown until it fits the data. The experiments performed show that the results are as good as other Machine Learning methods, in a very low computational time. Another advantage of this technique is that the complexity of the expressions can be limited, so the system can return mathematical expressions that can be easily analysed by the user, in opposition to other techniques like GSGP.
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It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link... more
It is a fact that, non-destructive measurement technologies have gain a lot of attention over the years. Among those technologies, NIR technology is the one which allows the analysis of electromagnetic spectrum looking for carbon-link interactions. This technology analyzes the electromagnetic spectrum in the band between 700 nm and 2500 nm, a band very close to the visible spectrum. Traditionally, the devices used to measure are utterly expensive and enormously bulky. That is why this project was focused on a portable spectrophotometer to make measures. This device is smaller and cheaper than the common spectrophotometer, although at the cost of a lower resolution. In this work, that device in combination with the use of machine learning was used to detect if a beer contains alcohol or it can be labeled as non-alcoholic drink.
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Unmanned Aerial Vehicle (UAV) swarms adoption shows a steady growth among operators due to the benefits in time and cost arisen from their use. However, this kind of system faces an important problem which is the calculation of many... more
Unmanned Aerial Vehicle (UAV) swarms adoption shows a steady growth among operators due to the benefits in time and cost arisen from their use. However, this kind of system faces an important problem which is the calculation of many optimal paths for each UAV. Solving this problem would allow a to control many UAVs without human intervention at the same time while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a system capable of calculating the optimal flight path for a UAV swarm. The aim of these paths is to achieve full coverage of a flight area for tasks such as field prospection. All this, regardless of the size of maps and the number of UAVs in the swarm. It is not necessary to establish targets or any other previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show tha...
Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard.... more
Among the bovine diseases, mastitis causes high economic losses in the dairy production system. Nowadays, detection under field conditions is mainly performed by the California Mastitis Test, which is considered the de facto standard. However, this method presents with problems of slowness and the expensiveness of the chemical-reactive process, which is deeply dependent on an expert’s trained eye and, consequently, is highly imprecise. The aim of this work is to propose a new method for bovine mastitis detection under field conditions. The proposed method uses a low-cost, smartphone-connected NIR spectrometer which solves the aforementioned problems of slowness, expert dependency and disposability of the chemical methods. This method uses spectra in combination with two k-Nearest Neighbors models. The first model is used to detect the presence of mastitis while the second model classifies the positive cases into weak and strong. The resulting method was validated by using a leave-on...
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Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive... more
Knowing the chemical composition of a substance provides valuable information about it. That is why numerous techniques have been developed to try to obtain it. One of them is the Near Infrared Spectrometry technique, a non-destructive technique that analyzes the electromagnetic spectrum in search of waves of a certain length. The aim of this project is to combine this technology with machine learning techniques to try to detect the presence of milk, as well as the level of cocoa present in an ounce of chocolate. This has given satisfactory results in both cases, so it is considered that the combination of these techniques offers great possibilities.
The authors wish to make the following corrections to this paper [...]
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The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main... more
The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better tr...
Artificial Neuron–Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed... more
Artificial Neuron–Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way information is processed by neural and astrocytic networks in the most evolved living organisms. Although ANGNs are not a consolidated method, their performance against the traditional approach, i.e. without artificial astrocytes, was already demonstrated on classification problems. However, the corresponding learning algorithms developed so far strongly depends on a set of glial parameters which are manually tuned for each specific problem. As a consequence, previous experimental tests have to be done in order to determine an adequate set of values, making such manual parameter configuration time-consuming, error-prone, biased and problem dependent. Thus, in this paper, we propose a novel learning approach for ANGNs that fully automates the learning process, and gives the p...