Manuel Curado

• PhD in Computer Science
• Professor (Senior Lecturer) at University of Alicante

Noise pollution in densely populated urban areas is a major issue that affects both quality of life and public health. This study explores and evaluates the application of deep learning techniques to predict urban noise levels, using the city of Madrid, Spain, as a case study. Several complementary approaches are compared: Convolutional Neural Netw...

Super-resolution (SR) techniques have gained traction in biomedical imaging for their ability to enhance image quality. However, it remains unclear whether these improvements translate into better performance in clinical tasks. In this study, we provide a comprehensive evaluation of state-of-the-art SR models—including CNN- and Transformer-based ar...

Artificial intelligence (AI) is transforming industries and decision-making processes, but concerns about transparency and fairness have increased. Explainable artificial intelligence (XAI) is crucial to address these concerns, providing transparency in AI decision making, alleviating the effect of biases and fostering trust. However, the applicati...

According to the World Health Organization, thousands of people die every year in road traffic accidents. A crucial problem is the prediction of medical assistance in these accidents. For this purpose, we propose a new deep learning model whose goal is to distinguish whether a traffic accident requires medical assistance. The proposed perspective i...

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in th...

Millions of people are involved in traffic accidents each year. According to the World Health Organization (WHO), approximately 1.19 million people experience fatal outcomes, while 20 to 50 million suffer non-fatal consequences that may affect them for the rest of their lives. Multiple studies suggest that one of the main causes of this high number...

Dear Colleagues, Medical imaging allows us to evaluate different pathologies for diagnostic and treatment purposes; consequently, it plays an important role in initiatives to improve public (or private) health. The use of X-ray images enables the detection, confirmation, and correct assessment and documentation of many diseases and pathologies, a...

In complex networks, important nodes have a significant impact, both functional and structural. From the perspective of data flow pattern detection, the evaluation of the importance of a node in a network, taking into account the role it plays as a transition element in random paths between two other nodes, has important applications in many areas....

Artificial Intelligence become a tool widely used in the context of urban mobility and road safety applications. This paper focuses on predicting the severity of traffic accidents, from the point of view of the need for assistance, using general features that can be easily and quickly collected. We propose a deep learning model based on a convoluti...

Purpose The authors will review the main concepts of graphs, present the implemented algorithm, as well as explain the different techniques applied to the graph, to achieve an efficient execution of the algorithm, both in terms of the use of multiple cores that the authors have available today, and the use of massive data parallelism through the pa...

(Full Waivers): [Mathematics] (IF: 2.4) Special Issue: Theory and Application of Neural Networks and Complex Networks

League of Legends (LoL) is a multiplayer online battle arena video game developed and published by Riot Games. It is a team-based game with over 140 characters to make epic plays with. The game blends the speed and intensity of an real-time strategy game (RTS) with role-playing game (RPG) elements. Two teams of powerful champions, each with unique...

In recent years, there has been an increasing interest in the exploitation of geo-tagged documents posted on Online Social Networks (OSN) for human-mobility pattern mining. These patterns can ease the development of effective and intelligent location-based systems of different scenarios. However, the validation of OSN geo-data as a reliable source...

To identify influential nodes in real networks, it is essential to note the importance of considering the local and global information in a network. In addition, it is also key to consider the dynamic information. Accordingly, the main aim of this paper is to present a new centrality measure based on return random walk and the effective distance gr...

Special Issue "Theory and Application of Neural Networks and Complex Networks" in Mathematics journal (ISSN 2227-7390). This special issue belongs to the section "Network Science". Deadline for manuscript submissions: 1 November 2023

There are many inventions to avoid the risk of electric contact in the plug. However, the objective of the invention resulting from the research is the proposal of a complementary measure to the existing ones, solving the specific problem of electrocutions that occur in the action of connection and disconnection, due to the contact of the user with...

Centrality metrics are one of the most meaningful features in a large number of real-world network systems. In that sense, the Betweenness centrality is a widely used measurement that quantifies the importance of a node in the information flow in a network. Moreover, there is a centrality measure, based on random-paths betweenness centrality, that...

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a...

The cleaning of the ear canal is very important, and it is crucial that it is performed by an otolaryngologist as it requires a high level of competence and skill. The accumulation of cerumen or earwax in the ear canal is one of the most commonly observed problems in patients, and ignoring it may cause hearing loss, irritation and discomfort. There...

Many scholars have tried to address the identification of critical nodes in complex networks from different perspectives. For instance, by means of the betweenness methods based on shortest paths and random walk, it is possible to measure the global importance of a node as an intermediate node. All these metrics have the common characteristic of no...

Taking into account that accessibility is one of the most strategic and determining factors in economic models and that accessibility and tourism affect each other, we can say that the study and improvement of one of them involved the development of the other. Using network analysis, this study presents an algorithm for labeling the difficulty of t...

In this work we look for characteristics and mobility patterns in the cities of Rome and London, from a dataset of private vehicle movements in those cities. Based on mobility data and other data related to the urban public transport network, commercial activity and tourist information, a multiplex network with three layers is constructed for each...

Complex networks provide a framework for modelling real-world systems. Based on a set of data on mobility by car between different urban areas of the city of Rome, we represent and analyze these mobility data coupled with urban public transport networks, augmenting the network nodes with data on commercial, economic, service and tourist activity in...

In this paper, we propose to embed edges instead of nodes using state-of-the-art neural/factorization methods (DeepWalk, node2vec, NetMF). These methods produce latent representations based on co-ocurrence statistics by simulating fixed-length random walks and then taking bags-of-vectors as the input to the Skip Gram Learning with Negative Sampling...

This Special Issue is dedicated to collecting original research contributions focused on the application of artificial intelligence (AI) methods for acquisition, filtering, management, analysis, discovery, and visualization of geo-information systems from multiple sources. We aim to take an integrated approach to AI solutions that fosters a robust...

https://www.mdpi.com/journal/jimaging/special_issues/RIA

For a considerable time, researchers have focused on defining different measures capable to characterizing the importance of vertices in networks. One type of these networks, the cities, are complex systems that generate large quantity of information. These data are an important part of the characteristics of the urban network itself. Because of th...

Alzheimer’s disease has been extensively studied using undirected graphs to represent the correlations of BOLD signals in different anatomical regions through functional magnetic resonance imaging (fMRI). However, there has been relatively little analysis of this kind of data using directed graphs, which potentially offer the potential to capture a...

Special Issue "The Application of AI Techniques on Geo-Information Systems" A special issue of International Journal of Geo-Information Deadline for manuscript submissions: 31 Dec 2020 https://www.mdpi.com/journal/ijgi/special_issues/GIS_AI Dear Colleagues, This Special Issue is dedicated to collecting original research contributions focused on t...

State-of-the-art methods for finding the m-best solutions to graph matching (QAP) rely on exclusion strategies. The k-th best solution is found by excluding all better ones from the search space. This provides diversity, a natural requirement for transforming a MAP problem into a m-best one. Since diversity enforces mode hopping, it is usually comb...

In this paper we propose a new method, Return Random Walk, for link prediction to infer new intra-class edges while minimizing the amount of inter-class noise, and we show how to exploit it in an unsupervised densifier method, Dirichlet densification, which can be used to increase the edge density in undirected graphs, setting so that commute times...

In this paper, we characterize the universal bounds of our recently reported Dirichlet Densifier. In particular we aim to study the impact of densification on the bounding of intra-class node similarities. To this end we derive a new bound for commute time estimation. This bound does not rely on the spectral gap, but on graph densification (or grap...

In this paper, we propose to embed edges instead of nodes using state-of-the-art neural/factorization methods (DeepWalk, node2vec). These methods produce latent representations based on co-ocurrence statistics by simulating fixed-length random walks and then taking bags-of-vectors as the input to the Skip Gram Learning with Negative Sampling (SGNS)...

Usually, the nodes’ interactions in many complex networks need a more accurate mapping than simple links. For instance, in social networks, it may be possible to consider different relationships between people. This implies the use of different layers where the nodes are preserved and the relationships are diverse, that is, multiplex networks or bi...

In this paper, we develop a novel Dirichlet densifier that can be used to increase the edge density in undirected graphs. Dirichlet densifiers are implicit minimizers of the spectral gap for the Laplacian spec- trum of a graph. One consequence of this property is that they can be used improve the estimation of meaningful commute distances for mid-s...

In this paper we analyze the practical implications of Szemerédi's regularity lemma in the preservation of metric information contained in large graphs. To this end, we present a heuristic algorithm to find regular partitions. Our experiments show that this method is quite robust to the natural sparsification of proximity graphs. In addition, this...

In this paper, we draw on Spielman and Srivastava’s method for graph sparsification in order to simplify shape representations. The underlying principle of graph sparsification is to retain only the edges which are key to the preservation of desired properties. In this regard, sparsification by edge resistance allows us to preserve (to some extent)...

In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs. To this end, we present a heuristic algorithm to find regular partitions. Our experiments show that this method is quite robust to the natural sparsification of proximity graphs. In addition, thi...

In this paper, we draw on Spielman and Srivastava’s method for graph sparsification in order to simplify shape representations. The underlying principle of graph sparsification is to retain only the edges which are key to the preservation of desired properties. In this regard, sparsification by edge resistance allows us to preserve (to some extent)...

In this paper, we introduce the approach of graph densification as a means of preconditioning spectral clustering. After motivating the need of densification, we review the fundamentals of graph densifiers based on cut similarity and then analyze their associated optimization problems. In our experiments we analyze the implications of densification...

In this paper, we propose a graph densification method based on minimizing the combinatorial Dirichlet integral for the line graph. This method allows to estimate meaningful commute distances for mid-size graphs. It is fully bottom up and unsupervised, whereas anchor graphs, the most popular alternative, are top-down. Compared with anchor graphs, o...

The estimation of mutual information between graphs has been an elusive problem until the formulation of graph matching in terms of manifold alignment. Then, graphs are mapped to multi-dimensional sets of points through structure preserving embeddings. Point-wise alignment algorithms can be exploited in this context to re-cast graph matching in ter...

Similarity compression is a critical step to improve the efficiency of edge detection. In this paper, we compare two approaches for compressing/decompressing similarity matrices, being edge detection our application domain. In this regard, state-of-the-art contour detectors rely on spectral clustering where pixel or patch similarity is encoded in a...