With the increasing adoption of AI in safety-critical applications within urban environments, the interpretability of these systems is paramount. This study explores the application of Explainable Artificial Intelligence (XAI) techniques to enhance transparency in audio-based detection of emergency vehicle sirens, a crucial component in urban sound management. Adopting methods such as SHAP (SHapley Additive exPlanations) values, Permutation Feature Importance, and model-specific feature scores, this research identifies key audio features, including mid-frequency spectral contrasts and targeted chroma components, which significantly help in distinguishing siren sounds among urban noise. The study examines various machine learning models, identifying K-Nearest Neighbors (KNN) and XGBoost as top performers; KNN excelled in class-specific precision, while XGBoost demonstrated strong cross-class discrimination. The findings highlight the potential of XAI in improving both accuracy and accountability for sound detection systems in safety-critical urban applications, advancing the deployment of transparent AI within smart city infrastructures.

Countries all around the world recognise the impact of air quality on public health, advocating for city centre decarbonisation and pollutant monitoring via Internet of Things technologies. Using data collected from these systems, it is possible to generate models that predict pollution based on regular patterns where mobility data can enhance the accuracy and robustness of these advanced machine learning models. This paper follows this approach, utilising vehicle traffic data from image recognition, on-site vehicle detectors, and synthetic data to maximise prediction accuracy in various urban environments. The results reveal that this proposal improves prediction for traffic-related pollutants, such as ${\text{SO}}_{2}$ and ${\text{PM}}_{2.5}$, which are linked to severe respiratory diseases. These results also highlight the role of synthetic data in enhancing prediction performance under limited datasets.

Sentiment analysis has gained prominence in the digital era, particularly within social media contexts. This study explores the integration of metadata, such as reposts, favorites, or followers, alongside textual information for sentiment analysis. A dataset of posts in Spanish is created, filtering by a set of keywords (a total of 17) of interest to us. A total of 3,962 posts were collected along with their metadata. Then, they have been manually classified as positive, negative, and neutral, and processed. The research studies and compares different models like LightGBM, BERT, and a hybrid model combining BERT outputs with metadata. Evaluation metrics, such as F1-score and precision, were employed, revealing that the hybrid model achieved the highest F1-score of 69.4%, showcasing the effectiveness of merging text comprehension capabilities with contextual metadata. While the incorporation of metadata enhanced overall sentiment classification, precision varied across sentiment classes, with notable improvements in neutral and positive sentiments, over 16% and 8% respectively.

Nowadays, air pollution is one of the most relevant environmental problems in most urban settings. Due to the utility in operational terms of anticipating certain pollution levels, several predictors based on Graph Neural Networks (GNN) have been proposed for the last years. Most of these solutions usually encode the relationships among stations in terms of their spatial distance, but they fail when it comes to capturing other spatial and feature-based contextual factors. Besides, they assume a homogeneous setting where all the stations are able to capture the same pollutants. However, large-scale settings frequently comprise different types of stations, each one with different measurement capabilities. For that reason, the present article introduces a novel GNN framework able to capture the similarities among stations related to the land use of their locations and their primary source of pollution. Furthermore, we define a methodology to deal with heterogeneous settings on the top of the GNN architecture. Finally, the proposal has been tested with a nation-wide Spanish air-pollution dataset with very promising results.

The management of road traffic incidents is a problem faced by governments in many countries. Normally, road operators have the infrastructure in place to monitor such incidents, albeit in a reactive manner. In Spain, there are traffic cameras on major roads to check for possible incidents, however, incident notification is slow and not automated. As an alternative, this paper proposes a system for automatic real-time traffic alerts. Thus, 1,500 camera images from the Dirección General de Tráfico (DGT) deployed on the main Spanish roads are analyzed in real time every 4 minutes. These images are not preprocessed, they have different qualities and are also affected by weather conditions such as fog, rain, sun reflections, etc. The system uses several Deep Learning classification models trained on a well-known dataset of traffic images including flowing traffic, dense traffic, accidents and fires. These models are used to classify the DGT images in real time, with satisfactory initial results, detecting both flowing traffic and dense traffic.

Greenhouses are complex systems where many variables are involved in order to optimize crops in an intensive agriculture framework. Therefore, monitoring and visualization of all these variables in real-time is mandatory to meet the trade-off between natural resource consumption and production maximization. In this article, we introduce an intelligent warning system to efficiently control agricultural activity in an operational greenhouse to increase productivity by optimizing crop production and energy consumption. The system includes a web application that allows the graphical and statistical representation of data measured by several sensors located inside a greenhouse. These sensors are located in strategic points that allow the reading of real-time data in a more accurate manner, therefore allowing the generation of information with the minimum percentage of error. In addition, the web application offers different data representations to allow a more exhaustive analysis of the data obtained. As a result, this warning system may help greenhouse managers to anticipate abnormal situations affecting their crops.

According to many analysts, February 2022 is dated as the exacerbation of the armed conflict between Ukraine and Russia. Of course, this shocking event has an impact on mood of people. In this context, we studied the music listening habits of people in Eastern European countries. We applied timeseries and statistical analysis of mood-relate features (danceability, valence and energy) to a Spotify dataset in order to define new paradigms of emotion-aware systems that can be applied to detect the population’s state of mind. The results show that the trend in the type of music changes notably towards songs with less genre diversity and reflecting less positivity.

Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results.

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport-network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids a time-invariant approach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.

Novel cooperative intelligent transportation systems (ITS) serve as the basis for the provision of a number of services for drivers, occupants, and third parties. The vast amount of information to be collected, especially in vehicle-to-infrastructure (V2I) communication services, requires new algorithms and hardware platforms to cope with real-time requirements; however, this combination is not properly addressed in the literature. In this paper, we introduce a high-throughput hardware-software infrastructure to gather information from vehicles and efficiently process it to provide novel ITS services. We propose a parallelization approach of a fuzzy clustering technique on heterogeneous servers based on CPU and several GPUs, tailored to classification problems in V2I. The infrastructure is empirically tested to offer a geo-located pollution information service through the periodical collection of both vehicle's position and status data. We offer a real service that correctly identifies highly polluting traffic areas and drivers. The results indicate a good performance of the system under high loads, and our scalability analysis reveals a good operation in real-ambitious deployments thanks to the use of the both CPU and multiple GPUs, showing that our proposal can efficiently host cooperative services involving high processing in the ITS context.

Currently, many of the elements that surround us in daily life need software systems that work from the information available in the domain (data-driven application domains) by performing a process of data mining from it. Between the data mining techniques used in everyday problems we find the k-Nearest Neighbors technique. However, in domains and real situations it is very common to find vague, ambiguous and noisy data, that is, imperfect information.Although this imperfect information is inevitable, most applications have traditionally ignored the need for developing appropriate approaches for representing and reasoning with such data imperfections. The soft computing field has dealt with the development of techniques that can work with this kind of information as discipline whose main characteristic is tolerance to inaccuracy and uncertainty.In this work, we extend the k-Nearest Neighbors technique using concepts and methods provided by Soft Computing. The aim is to carry out the processes of instance selection and classification in everyday problems from imperfect information making the technique more realistic.

The behavior of university students is a field of study on the rise, whose main objective is the search for patterns that help improve their learning process. This paper analyzes the use of Learning Management Systems (LMS) in Higher Education and the interactions with their different tools from the students' viewpoint. For the analysis of the student activity statistical techniques and algorithms are extending to be used for big data platform. The information extracted from each student is based both on the events held in each session and on the number of sessions held. The analyzed data belongs to subjects of different modalities (on-campus, blended, online). The results of the methods are compared and discussed regarding the learning modalities. The results are interpreted in a discussion focus obtaining satisfactory knowledge for the identification of patterns of behavior.

The efficient market hypothesis (EMH) affirms that asset prices should reveal all available information. Therefore, it is impossible to "beat the market" always on a risk-adjusted basis since market prices should only respond to new information. Here, we propose a new model to validate the EMH that is inspired on an elastic network model. More specifically, we apply this comparison to Foreign Exchange (FOREX) market under some restrictive conditions. In our hypothesis, several interaction potentials are used to characterize the interaction between banks and each particular quotation. This hypothesis comes from the study of several natural systems, such as macromolecules in solution. An algorithm based on the Monte Carlo methods is also presented in order to predict the evolution of the system.

This paper offers a proposal for user profiling based on similarity, trust and reputation notions. We present a general profile ontology to model the basic concepts related to the profile assignment process. Based on this ontology, we propose an adaptive mechanism to guide the interactions between user and broker agents aimed to select the most suitable profile taking into account the user's requirements and preferences. Broker agents estimate the suitability of the profiles using trust and reputation information coming from its own experiences or from other brokers. Under uncertainty conditions, similarity between two set of user's requirements and preferences is also used. The evaluation of suitability of each suggested profile to the user's requirements and the similarity between two set ofuser's requirements are based on the service discovery processes proposed by WSMO.