Raquel Martínez-España

University of Murcia | UM · Departamento de Ingeniería de la Información y las Comunicaciones

We are witnessing the digitalization era, where artificial intelligence (AI)/machine learning (ML) models are mandatory to transform this data deluge into actionable information. However, these models require large, high-quality datasets to predict high reliability/accuracy. Even with the maturity of Internet of Things (IoT) systems, there are stil...

Nowadays, human overpopulation is stressing our ecosystems in different ways, agriculture being a critical example as different predictions point towards food shortages in the near future. Accordingly, smart farming is becoming key to the optimization of natural resources so that different crops can be grown efficiently, consuming as few resources...

Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelli...

The combination of Artificial Intelligence and the Internet of Things (AIoT) is enabling the next economic revolution in which data and immediacy are at the key players. Agriculture is one of the sectors that can benefit most from the use of AIoT to optimise resources and reduce its environmental footprint. However, this convergence requires comput...

The Internet of Things (IoT) enables the next economic revolution in which data and immediacy are the key players. Edge computing is a compelling alternative for enabling computing capabilities at the network's edge. These computing capabilities could help transform the generated data into useful information by executing machine learning (ML) workl...

Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount...

In water bodies, sediment transport is a potential source of numerous negative effects on water resource projects and can damage environmental services. Two machine learning (ML) algorithms, the M5P and random forest (RF) models, have been explored for the first time as alternatives to the Soil and Water Assessment Tool (SWAT) model to estimate sus...

The management of the COVID-19 pandemic has been shown to be critical for reducing its dramatic effects. Social sensing can analyse user-contributed data posted daily in social-media services, where participants are seen as Social Sensors. Individually, social sensors may provide noisy information. However, collectively, such opinion holders consti...

We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only...

We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only...

The Internet of Things (IoT) is driving the digital revolution. AlSome palliative measures aremost all economic sectors are becoming “Smart” thanks to the analysis of data generated by IoT. This analysis is carried out by advance artificial intelligence (AI) techniques that provide insights never before imagined. The combination of both IoT and AI...

Precision agriculture is a growing sector that improves traditional agricultural processes through the use of new technologies. In southeast Spain, farmers are continuously fighting against harsh conditions caused by the effects of climate change. Among these problems, the great variability of temperatures (up to 20 °C in the same day) stands out....

Precision agriculture has different strategies to collect, process and analyze different types and nature data to be able to make decisions that improve the efficiency, productivity, quality, profitability and sustainability of agricultural production. Specifically, crop sustainability is directly related to reducing costs for farmers and minimizin...

Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The...

Early detection of cancer is important to improve survival and reduce associated morbility. Nowadays, there is no automatic classification process with enough accuracy to be recommended to its use in population cervical cancer screening. In most automatic medical image classifications, these images are clean in background and without overlap betwee...

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detec...

Learning Management System (LMS) platforms have led to a transformation in Universities in the last decade, helping them to adapt and expand their services to new technological challenges. These platforms have made possible the expansion of distance education. A current trend in this area is focused on the evaluation and improvement of the students...

The constant innovation in new technologies and the increase in the use of computing devices in different areas of the society have contributed to a digital transformation in almost every sector. This digital transformation has also reached the world of education, making it possible for members of the educational community to adopt Learning Managem...

Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the su...

Learning Management Systems (LMS) have become the principal resource for collaboration among lecturers and students in Higher Education. A research line is the analysis of LMS users’ behaviour with the goal of developing new methodological proposals to improve the teaching process. In particular, this paper analyses the lecturers’ behaviour and pro...

Portable mid-infrared (MIR) technology is well suited for the provision of detailed and inexpensive information on key soil properties for optimum soil management. This technology requires prior complex multivariate modelling. In this manuscript, we propose an intelligent system approach based on portable MIR spectroscopy data modelled by machine l...

The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment p...

Cervical cancer is the third neoplasm in frequency worldwide between women. Screening techniques in general population have demonstrated clear effectiveness as its implementation has decreased cervical cancer incidence and mortality more than 70% in several countries. This benefit is related with detection of early pre-malignant asymptomatic lesion...

In recent years, learning management systems (LMSs) have played a fundamental role in higher education teaching models. A new line of research has been opened relating to the analysis of student behavior within an LMS, in the search for patterns that improve the learning process. Current e-learning platforms allow for recording student activity, th...

The k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the...

Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five. Smart cities are called to play a decisive role to improve such pollution by first collecting, in real-time, different parameters such as SO2, NOx, O3, NH3, CO, PM1...

Faced with a new concept to learn, our brain does not work in isolation. It uses all previously learned knowledge. In addition, the brain is able to isolate the knowledge that does not benefit us, and to use what is actually useful. In machine learning, we do not usually benefit from the knowledge of other learned tasks. However, there is a methodo...

The reinforced concrete beams is a structural member that is widely used in all types of building and civil constructions. These beams are subjected to different external loads that, above a critical value, may cause the collapse of the whole structure, having devastating consequences for civilians. Therefore, the a priori simulation of the interna...

A classification problem involves selecting a training dataset with class labels, developing an accurate description or a model for each class using the attributes available in the data, and then evaluating the prediction quality of the induced model. In this paper, we focus on supervised classification and models which have been obtained from data...

The application of microarray technology to the diagnosis of cancer has been a challenge for computational techniques because the datasets obtained have high dimension and a few examples. In this paper two computational techniques are applied to tumor datasets in order to carry out the task of diagnosis of cancer (classification task) and identifyi...

In this paper, we present an extension of k nearest neighbors method so it can perform imputation/classification from datasets with low quality data. The method performs a weighting of neighbors based on their imperfection and distance of classes. Thus the method allows us explicitly to indicate the average degree of imperfection of the neighbors t...

Currently, most datasets from real-world problems contain low-quality data. In particular, within soft computing and data mining areas, the research and development of techniques that can deal with this type of data has been increased recently. In order to facilitate the design of experiments in this field and with these data, an experimenter envir...

One factor which greatly affects the performance of the machine learning techniques is the quality of the dataset with which to work. In this paper we focus on two problems that can affect these data: 1) the existence of irrelevant, redundant features and 2) the existence of low quality values for such features resulting of the measurement process....

Companies have to be competitive to survive, offering their customers a more customized and complete service. Moreover, society is increasingly more aware of the need to solve problems in an ecological way trying to reduce pollution/environmental impact. Furthermore, when we try to solve management problems in crowded cities, companies expect their...

Today, feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, as well as redundant features that are strongly correlated. There are a lot of approaches for feature selection, but most of them can on...

Machine learning techniques are useful tools that can help us in the knowledge extraction from gene expression data in biological systems. In this paper two machine learning techniques are applied to tumor datasets based on gene expression data. Both techniques are based on a fuzzy decision tree ensemble and are used to carry out the classification...

Classification problems in which the number of attributes is larger than the number of examples are increasingly common with rapid technological advances in data collection. Also numerical data are predominant in real world applications and many algorithms in supervised learning are restricted to discrete attributes. Focusing on these issues, we pr...

An important aspect to consider in applications which work with great volumes of data is that frequently these data are of low quality and also cannot be use other types of data. The field of Soft Computing has dealt, among other things, with developing techniques that will be able to work with these types of low quality data in a suitable way, res...

Every day there are more techniques that can work with low quality data. As a result, issues related to data quality have become more crucial and have consumed a majority of the time and budget of data mining projects. One problem for researchers is the lack of low quality data in order to test their techniques with this data type. Also, as far as...

In areas of Data Mining and Soft Computing is important the discretization of numerical attributes because there are techniques that can not work with numerical domains or can get better results when working with discrete domains. The precision obtained with these techniques depends largely on the quality of the discretization performed. Moreover,...

Companies have to be competitive to survive, offering their customers a more customized and complete service. Moreover, society is increasingly more aware of the need to solve problems in an ecological way trying to reduce pollution/environmental impact. Furthermore, when we try to solve management problems in crowded cities, companies expect their...

Nowadays in the world of Soft Computing there is a new challenge which consists of working with low quality data. To test the techniques that are designed in this area, there is the need for repositories of low quality datasets. Currently we can find various data mining techniques that are designed to handle some kind of low quality data. But, as f...

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty, and imprecision that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble f...

Many algorithms for classification need to discretize the continuous attributes for their development. Therefore the discretization of continuous attributes is a very important part in data mining. In this paper, we propose a technique for discretizing continuous attributes by means of a series of fuzzy sets which constitute a fuzzy partition of th...

Feature selection is an active research in machine learning. The main idea of feature selection is to choose a subset of available features, by eliminating features with little or no predictive information, and features strongly correlated. There are many approaches for feature selection, but most of them can only work with crisp data. Until our kn...

When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Classifier ensembles are the result of combining several individual classifiers. In this work we propose and compare various consensus based combination methods to obtain the final decision of the ensemble based on fu...

Instrument errors or noise interference during experiments may lead to incomplete data when measuring a specific attribute. Obtaining models from imperfect data is a topic currently being treated with more interest. In this paper, we present the learning phase of a Fuzzy Random Forest ensemble for classification from imperfect data. We perform expe...

Imperfect information inevitably appears in real situations for a variety of reasons. Although efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty and imprecision that can be handled. In this paper, we will present a Fuzzy Random Forest ensemble fo...

The discretization of values plays a critical role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means o...

The problem of selecting variables in data-mining can be modelled as an optimisation problem involving multiple objectives which must be simultaneously optimised. This contribution proposes a multiple objective optimisation model for the problem of selecting variables applicable to the classification of mortality in patients from a hospital burns u...

A multi-classifier system -obtained by combining sev-eral individual classifiers -usually exhibits a better performance (precision) than any of the original classifiers. In this work we use a multi-classifier based on a forest of randomly generated fuzzy deci-sion trees (Fuzzy Random Forest), and we propose a new method to combine their decisions t...

RESUMEN En trabajos previos mostramos el buen comportamiento obtenido al aplicar soft computing a ensambles basados en boosting, bagging y random forest, destacando entre ellos este último. Entre los métodos de combinación que aplica-mos para obtener la decisión final del ensamble destacaba uno basado en consenso. En este trabajo proponemos una mej...