María N. Moreno García

University of Salamanca · Department of Computer Science and Automatics

Predicting climate variables at seasonal time scales is crucial for climate services. At these time scales, the most important driver of the atmospheric variability patterns is the ocean, which can trigger local perturbations that could affect the climate of remote areas through different teleconnection mechanisms. Dynamical models are not always e...

The quantity and quality of a dataset play a crucial role in the performance of prediction models. Increasing the amount of data increases the computational requirements and can introduce negligible variations, outliers, and noise. These significantly impact the model performance. Thus, instance selection techniques are crucial for building predict...

The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despit...

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of rec...

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models’ reliability in some domains’ Vietnamese language....

The pandemic caused by COVID-19 in 2020 has drastically changed people’s lifestyles and habits, especially in the area of tourism. This situation led people to consider a new way of travelling, one that focused on making up for lost time. As a result, holidaymakers are now looking for more nature-based holidays, for which motorhomes and campervans...

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language....

In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the d...

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between u...

Nurses are often asked to predict factors that influence post-stroke outcome by the patient and family. Many studies have been carried out in order to determine the factors that influence the neurological status of the post-stroke patient at the moment of the discharge from the hospital. However, machine learning techniques have not been used for t...

Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. Fo...

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. The...

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between u...

One of the biggest problems in cities today is the significant increase in the number of motor vehicles. Intelligent traffic control is a fundamental part of controlling city travel. To achieve this goal, it is very important to have sensor technologies capable of identifying the number of vehicles traveling on a road. In this paper, we propose the...

In the music domain, tags are commonly used to categorize the different songs a user listens to on streaming services such as Spotify or Deezer. Different types of tags have been used, such as those related to the musical genre, others related to the emotions evoked by the song, and others related to the user's context of action or activity. These...

This research work is focused on the field of object detection through computer vision, focusing on a very popular topic nowadays, smart cities. Therefore, a proposal is presented, after evaluating the different systems that have been used in the literature, based on deep learning techniques together with the use of images to detect traffic density...

A conceptual approach to a recommender system that is industrial oriented and optimized for business-to-business. With particular needs, industrial datasets seek assertiveness and contextualization to capitalize on recommender systems. Muscle memory must be implanted on business users, enabling them to harvest the benefits of such technologies. The...

Music streaming services have opened the possibility of accessing huge quantities of songs and more sophisticated data can be utilized by recommender systems to improve their performance. Some recommendation methods dealing with different music features have been proposed during the last years, but most of them do not consider emotional aspects. Th...

Traffic flow congestion is a very present problem on the daily life of citizens of big cities. Furthermore, it is growing by the day because of the increase of population. Furthermore, it has undesirable consequences such as an increase of air pollution levels and a worse life quality. Traditional solutions, such as investing on public transport, a...

Nowadays music for groups is consumed in both virtual and physical environments. Recommender systems focused on this domain have mostly focused on individual recommendation, although there are also approaches for group recommendation. Considering that no systematic literature review (SLR) of existing works on this topic has been performed, this res...

In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Thus,...

Nowadays, recommender systems are present in multiple application domains, such as e-commerce, digital libraries, music streaming services, etc. In the music domain, these systems are especially useful, since users often like to listen to new songs and discover new bands. At the same time, group music consumption has proliferated in this domain, no...

In supervised learning scenarios, some applications require solve a classification problem wherein labels are not given as a single ground truth. Instead, the criteria of a set of experts is used to provide labels aimed at compensating for the erroneous influence with respect to a single labeler as well as the error bias (excellent or lousy) due to...

Recommender systems are being used in streaming service platforms to provide users with personalized suggestions to increase user satisfaction. These recommendations are primarily based on data about the interaction of users with the system; however, other information from the large amounts of media data can be exploited to improve their reliabilit...

Music listening preferences at a given time depend on a wide range of contextual factors, such as user emotional state, location and activity at listening time, the day of the week, the time of the day, etc. It is therefore of great importance to take them into account when recommending music. However, it is very difficult to develop context-aware...

In many application domains such as medicine, information retrieval, cybersecurity, social media, etc., datasets used for inducing classification models often have an unequal distribution of the instances of each class. This situation, known as imbalanced data classification, causes low predictive performance for the minority class examples. Thus,...

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the c...

Sentiment analysis on public opinion expressed in social networks, such as Twitter or Facebook, has been developed into a wide range of applications, but there are still many challenges to be addressed. Hybrid techniques have shown to be potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test th...

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product....

The design of recommendation algorithms aware of the user’s context has been the subject of great interest in the scientific community, especially in the music domain where contextual factors have a significant impact on the recommendations. In this type of system, the user’s contextual information can come from different sources such as the specif...

2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT'20), 14-17 December 2020, Melbourne, Australia.

The large amount of music that can be accessed in streaming nowadays has led to the development of more reliable music recommendation systems. To this end, context-aware music recommendation systems, capable of suggesting music taking into account contextual information, have emerged. Studies have shown that music helps to improve mood while can ch...

Nowadays, the use of social information is extending to more and more application domains. In the field of recommender systems, this information has been exploited in different ways to address some problems, especially associated with collabora-tive filtering methods, and thus achieve more reliable recom-mendations. Specifically, social tagging is...

The large amount of digital content available through web sites, social networks, streaming services, and other distribution media, allows more and more people to access virtually unlimited sources of information, products, and services [...]

The use of machine learning into economics scenarios results appealing since it allows for automatically testing economic models and predict consumer/client behavior to support decision-making processes. The finance market typically uses a set of expert labelers or Bureau credit scores given by governmental or private agencies such as Expe-rian, Eq...

The digital entertainment sector is one of the fastest growing in recent years. In the case of video games, the productions of some of the most popular titles are on a par with film productions. The sale of video games is in the millions, and yet there are few works on the recommendation of video games. In this work a hybrid system of video game re...

After the paradigm shift produced by Web 2.0, the volume of opinion on the Internet has increased exponentially. The expansion of social media, whose textual content is somewhat subjective and comes loaded with opinions and assessments, can be very useful for recommending a product or brand. This information is an interesting challenge from the per...

This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which suppor...

Recent research in the field of recommender systems focuses on the incorporation of social information into collaborative filtering methods to improve the reliability of recommendations. Social networks enclose valuable data regarding user behavior and connections that can be exploited in this area to infer knowledge about user preferences and soci...

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects...

Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects...

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encoun...

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field i...

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-c...

In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated. However, developing reliable recommender systems in the music field i...

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users’ opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encoun...

Music is a universal language present in all cultures throughout history, and its influence on human psychology is amply demonstrated. Music provides a direct link to emotions. Numerous investigations study the relationship between musical characteristics and physiological parameters produced or even induced. This paper presents the development of...

Companies, among other sectors, require that the opinions generated on the web be extracted automatically, obtaining their polarity on products or services, to achieve their objectives. Since the opinions are subjective and unstructured, there are still many problems within this field that must be solved. To mention a few, the problem of ambiguity...

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-c...

This paper describes WEBECOOL, a hybrid web/mobile application with the purpose of improving the self-management of anxiety disorders. The development of WEBECOOL was driven by a multidisciplinary collaboration between computing engineers, psychologists and psychiatrics. The first stage of the web/mobile application test was performed to assess the...

It is a fact that music is directly linked to emotions. Various researches study the link between musical characteristics and the feeling produced or even induced. This work shows a web tool that allows the automatically extraction of musical characteristics of songs including the emotional classification and it uses this metadata to manage the use...

The wide acceptance by people when using mobile applications to communicate or interact, replacing traditional media, makes mobile applications a very useful tool today. Thanks to the interconnection that smartphones provide and the technological evolution that allows everyday objects to be connected with people, the concept of smart cities has gai...

The importance that data have taken on in recent years, mainly due to technological evolutions (mainly in connectivity and processing capacity) and the reduction of associated costs, means that cities, generators of large volumes of data, invest and bet on infrastructures that analyze the data to obtain benefits. Such has been the importance that m...

Social characteristics present in current music streaming services allow to use methods for endowing these systems with more reliable recommendation functionalities. There are many proposals in the literature that take advantage of that information and use it in the context of recommender systems. However, in the specific application domain of musi...

Influence zone generating is used in different fields: environmental studies, urban areas establishment, road construction and fire propagation. The influence zone is defined as the geometric space of the points that are at a shorter or similar distance to a given object (point, polyline or polygon). The simulation of the phenomena that are subject...

Suppliers of music streaming services are showing an increasing interest for providing users with reliable personalized recommendations since their practically unlimited offerings make it difficult for users to find the music they like. In this work, we take advantage of social tags that users give to music through streaming platforms for improving...

The interest for providing users with suitable recommendations of songs and playlists has increased since online services for listening to music have become popular. Many methods for achieving this objective have been proposed, some of them addressed to solve well-known problems of recommender systems. However, music application domain has addition...

The teaching work in the university face-to-face with large groups of students re-quires an important updating of the role of the teacher in the management of his teaching. Not only in terms of actual teaching per se, but also in the management of all the information linked to the grades, attendance data, calendars of face-to-face activities, etc....

This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the...

Geographic information systems have usually implemented a module that allows areas to be delimited by an isotropic buffer. In this kind of buffer, the generating polygon is a circle, which implies a constant distance from the border of the buffer to the object. The simulation of anisotropic processes, in contrast, requires the use of generating pol...

The imbalanced class problem is noteworthy given its impact on the induction of predictive models and its constant presence in several application areas. It is a challenge in supervised classification, since most of classifiers are very sensitive to class distributions. Consequently, the predictive model is biased to the majority class, which leads...

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related...

The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-shee...

Nowadays, online social networks such as Facebook and Twitter become increasingly popular. These social media channels allow people to create, share, and comment on information about anything related to their real-life. Such information is very useful for various application domains, e.g., decision support systems or online advertising. In this pap...

Surveillance and prevention of infections acquired in the hospital environment is an important challenge in the current health systems given the great impact of these kind of infections on patient mortality as well as on sanitary costs. Data analysis can contribute to make easier these tasks by means of identification of risk factors and prediction...

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related...