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David Camacho received the PhD degree in computer science from the Universidad Carlos III de Madrid, Madrid, Spain, in 2001. He is currently a Full Professor with the Department of Computer Engineering Systems at Universidad Politécnica de Madrid, where he is the Head of the Applied Intelligence and Data Analysis Group. He has authored or coauthored more than 300 journals, books, and conference papers. His current research interests include data mining, machine learning, evolutionary computation, swarm intelligence, and social network analysis.
The effect of atmospheric drag on spacecraft dynamics is considered one of the predominant sources of uncertainty in Low Earth Orbit. These effects are characterised in part by the atmospheric density, a quantity highly correlated to space weather. Current atmosphere models typically account for this through proxy indices such as the F10.7, but with variations in solar radio flux forecasts leading to significant orbit differences over just a few days, prediction of these quantities is a limiting factor in the accurate estimation of future drag conditions, and consequently orbital prediction. In this work, a novel deep residual architecture for univariate time series forecasting, N-BEATS, is employed for the prediction of the F10.7 solar proxy on the days-ahead timescales relevant to space operations. This untailored, pure deep learning approach has recently achieved state-of-the-art performance in time series forecasting competitions, outperforming well-established statistical, as well as statistical hybrid models, across a range of domains. The approach was found to be effective in single point forecasting up to 27-days ahead, and was additionally extended to produce forecast uncertainty estimates using deep ensembles. These forecasts were then compared to a persistence baseline and two operationally available forecasts: one statistical (provided by BGS, ESA), and one multi-flux neural network (by CLS, CNES). It was found that the N-BEATS model systematically outperformed the baseline and statistical approaches, and achieved an improved or similar performance to the multi-flux neural network approach despite only learning from a single variable.
Emma Stevenson; Victor Rodriguez-Fernandez; Edmondo Minisci; David Camacho. A deep learning approach to solar radio flux forecasting. Acta Astronautica 2021, 1 .
AMA StyleEmma Stevenson, Victor Rodriguez-Fernandez, Edmondo Minisci, David Camacho. A deep learning approach to solar radio flux forecasting. Acta Astronautica. 2021; ():1.
Chicago/Turabian StyleEmma Stevenson; Victor Rodriguez-Fernandez; Edmondo Minisci; David Camacho. 2021. "A deep learning approach to solar radio flux forecasting." Acta Astronautica , no. : 1.
This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch’s geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.
J. Pérez-Aracil; C. Camacho-Gómez; A.M. Hernández-Díaz; E. Pereira; D. Camacho; S. Salcedo-Sanz. Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches. Swarm and Evolutionary Computation 2021, 67, 100958 .
AMA StyleJ. Pérez-Aracil, C. Camacho-Gómez, A.M. Hernández-Díaz, E. Pereira, D. Camacho, S. Salcedo-Sanz. Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches. Swarm and Evolutionary Computation. 2021; 67 ():100958.
Chicago/Turabian StyleJ. Pérez-Aracil; C. Camacho-Gómez; A.M. Hernández-Díaz; E. Pereira; D. Camacho; S. Salcedo-Sanz. 2021. "Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches." Swarm and Evolutionary Computation 67, no. : 100958.
Pneumonia is a lung infection that causes 15% of childhood mortality (under 5 years old), over 800,000 children under five every year, around 2,200 every day, all over the world. This pathology is mainly caused by viruses or bacteria. X-rays imaging analysis is one of the most used methods for pneumonia diagnosis. These clinical images can be analyzed using machine learning methods such as convolutional neural networks (CNN), which learn to extract critical features for the classification. However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generate an understandable explanation (from a human-based perspective), about how they have reached those results. Another problem that difficults the impact of this technology is the limited amount of labeled data in many medicine domains. The main contributions of this work are two fold: the first one is the design of a new explainable artificial intelligence (XAI) technique based on combining the individual heatmaps obtained from each model in the ensemble. This allows to overcome the explainability and interpretability problems of the CNN “black boxes”, highlighting those areas of the image which are more relevant to generate the classification. The second one is the development of new ensemble deep learning models to classify chest X-rays that allow highly competitive results using small datasets for training. We tested our ensemble model using a small dataset of pediatric X-rays (950 samples of children between one month and 16 years old) with low quality and anatomical variability (which represents one of the biggest challenges addressed in this work). We also tested other strategies such as single CNNs trained from scratch and transfer learning using CheXNet. Our results show that our ensemble model clearly outperforms these strategies obtaining highly competitive results. Finally we confirmed the robustness of our approach using another pneumonia diagnosis dataset (Kermany et al., 2018).
Helena Liz; Manuel Sánchez-Montañés; Alfredo Tagarro; Sara Domínguez-Rodríguez; Ron Dagan; David Camacho. Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis. Future Generation Computer Systems 2021, 122, 220 -233.
AMA StyleHelena Liz, Manuel Sánchez-Montañés, Alfredo Tagarro, Sara Domínguez-Rodríguez, Ron Dagan, David Camacho. Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis. Future Generation Computer Systems. 2021; 122 ():220-233.
Chicago/Turabian StyleHelena Liz; Manuel Sánchez-Montañés; Alfredo Tagarro; Sara Domínguez-Rodríguez; Ron Dagan; David Camacho. 2021. "Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis." Future Generation Computer Systems 122, no. : 220-233.
Nowadays, Artificial intelligence (AI), combined with the digitalisation of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.
Francesco Piccialli; Fabio Giampaolo; Edoardo Prezioso; David Camacho; Giovanni Acampora. Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion. Information Fusion 2021, 74, 1 -16.
AMA StyleFrancesco Piccialli, Fabio Giampaolo, Edoardo Prezioso, David Camacho, Giovanni Acampora. Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion. Information Fusion. 2021; 74 ():1-16.
Chicago/Turabian StyleFrancesco Piccialli; Fabio Giampaolo; Edoardo Prezioso; David Camacho; Giovanni Acampora. 2021. "Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion." Information Fusion 74, no. : 1-16.
Subtitles are a key element to make any media content accessible for people who suffer from hearing impairment and for elderly people, but also useful when watching TV in a noisy environment or learning new languages. Most of the time, subtitles are generated manually in advance, building a verbatim and synchronised transcription of the audio. However, in TV live broadcasts, captions are created in real time by a re-speaker with the help of a voice recognition software, which inevitability leads to delays and lack of synchronisation. In this paper, we present Deep-Sync, a tool for the alignment of subtitles with the audio-visual content. The architecture integrates a deep language representation model and a real-time voice recognition software to build a semantic-aware alignment tool that successfully aligns most of the subtitles even when there is no direct correspondence between the re-speaker and the audio content. In order to avoid any kind of censorship, Deep-Sync can be deployed directly on users’ TVs causing a small delay to perform the alignment, but avoiding to delay the signal at the broadcaster station. Deep-Sync was compared with other subtitles alignment tool, showing that our proposal is able to improve the synchronisation in all tested cases.
Alejandro Martín; Israel González-Carrasco; Victor Rodriguez-Fernandez; Mónica Souto-Rico; David Camacho; Belén Ruiz-Mezcua. Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation. Neural Computing and Applications 2021, 1 -15.
AMA StyleAlejandro Martín, Israel González-Carrasco, Victor Rodriguez-Fernandez, Mónica Souto-Rico, David Camacho, Belén Ruiz-Mezcua. Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation. Neural Computing and Applications. 2021; ():1-15.
Chicago/Turabian StyleAlejandro Martín; Israel González-Carrasco; Victor Rodriguez-Fernandez; Mónica Souto-Rico; David Camacho; Belén Ruiz-Mezcua. 2021. "Deep-Sync: A novel deep learning-based tool for semantic-aware subtitling synchronisation." Neural Computing and Applications , no. : 1-15.
The aim of data transformation is to transform the original feature space of data into another space with better properties. This is typically combined with dimensionality reduction, so that the dimensionality of the transformed space is smaller. A widely used method for data transformation and dimensionality reduction is Principal Component Analysis (PCA). PCA finds a subspace that explains most of the data variance. While the new PCA feature space has interesting properties, such as removing linear correlation, PCA is an unsupervised method. Therefore, there is no guarantee that the PCA feature space will be the most appropriate for supervised tasks, such as classification or regression. On the other hand, 3-layer Multi Layer Perceptrons (MLP), which are supervised methods, can also be understood as a data transformation carried out by the hidden layer, followed by a classification/regression operation performed by the output layer. Given that the hidden layer is obtained after a supervised training process, it can be considered that it is performing a supervised data transformation. And if the number of hidden neurons is smaller than the input, also dimensionality reduction. Despite this kind of transformation being widely available (any neural network package that allows access to the hidden layer weights can be used), no extensive experimentation on the quality of 3-layer MLP data transformation has been carried out. The aim of this article is to carry out this research for classification problems. Results show that, overall, this transformation offers better results than the PCA unsupervised transformation method.
José M. Valls; Ricardo Aler; Inés M. Galván; David Camacho. Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -13.
AMA StyleJosé M. Valls, Ricardo Aler, Inés M. Galván, David Camacho. Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-13.
Chicago/Turabian StyleJosé M. Valls; Ricardo Aler; Inés M. Galván; David Camacho. 2021. "Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems." Journal of Ambient Intelligence and Humanized Computing , no. : 1-13.
Virtual Worlds (VWs) are popular tools for teaching/learning in the twenty-first century classroom. The challenge remains however, to provide the means by which teachers could sustainably analyse and assess the performance of large groups of students in such environments. Unfortunately, external game features such as game scores and play duration have turned out to be unfair in some assessments. In this context, a case study was carried out in a foreign language course, illustrating how teachers could easily retrieve a number of performance indicators from VW-interaction logs and harness them to conduct a fine-grained analysis of students’ performance, while facilitating at the same time valuable tools for their assessment. Objective performance indicators in a server database were made accessible using an end-user development programming language. This way, a range of data visualisation methods could be employed to contrast different assumptions regarding learner performance when playing a VW-based game, which was designed to help CEFR A1 level students to learn German. This way, factors such as randomisation of game tasks, which could negatively affect learner performance, were alleviated.
Manuel Palomo-Duarte; Anke Berns; Antonio Balderas; Juan Dodero; David Camacho. Evidence-Based Assessment of Student Performance in Virtual Worlds. Sustainability 2020, 13, 244 .
AMA StyleManuel Palomo-Duarte, Anke Berns, Antonio Balderas, Juan Dodero, David Camacho. Evidence-Based Assessment of Student Performance in Virtual Worlds. Sustainability. 2020; 13 (1):244.
Chicago/Turabian StyleManuel Palomo-Duarte; Anke Berns; Antonio Balderas; Juan Dodero; David Camacho. 2020. "Evidence-Based Assessment of Student Performance in Virtual Worlds." Sustainability 13, no. 1: 244.
Cloud type classification is a complex multi-class problem where total sky images are analysed to determine their category such as Stratus or Cirrus, among others. However, many properties of this domain make high classification accuracy difficult to achieve. In this paper, we design a novel fusion approach, showing that recent image classification architectures based on deep learning, such as Convolutional Neural Networks, can be improved using statistical features directly calculated from images. In this research, three powerful CNNs have been trained on a comprehensive dataset: VGG-19, Inception-ResNet V2 and Inception V3. Simultaneously, a pool of standard machine learning classifiers have been trained on 14 different statistical characteristics on each colour channel. The results evidence that a fusion approach of the predictions of an image-trained CNN and a feature-trained Random Forest classifier improves the classification ability of both methods individually, reaching 95.05% macro average weighted precision over 12 classes in a complex highly imbalanced dataset with noisy examples.
Javier Huertas-Tato; Alejandro Martín; David Camacho. Cloud Type Identification Using Data Fusion and Ensemble Learning. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 137 -147.
AMA StyleJavier Huertas-Tato, Alejandro Martín, David Camacho. Cloud Type Identification Using Data Fusion and Ensemble Learning. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():137-147.
Chicago/Turabian StyleJavier Huertas-Tato; Alejandro Martín; David Camacho. 2020. "Cloud Type Identification Using Data Fusion and Ensemble Learning." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 137-147.
Smart grids are a type of complex cyber–physical system (CPS) that integrates the communication capabilities of smart devices into the grid to facilitate remote operation and control of power systems. However, this integration exposes many existing vulnerabilities of conventional supervisory control and data acquisition (SCADA) systems, resulting in severe cyber threats to the smart grid and potential violation of security objectives. Stealing sensitive information, modifying firmware, or injecting function codes through compromised devices are examples of possible attacks on the smart grid. Therefore, early detection of cyberattacks on the grid is crucial to protect it from sabotage. Machine learning (ML) methods are conventional approaches for detecting cyberattacks that use features of smart grid networks. However, developing an effective, highly accurate detection method with reduced computational overload, is still a challenging research problem. In this work, an efficient and effective security control approach is proposed to detect cyberattacks on the smart grid. The proposed approach combines both feature reduction and detection techniques to reduce the extremely large number of features and achieve an improved detection rate. A correlation-based feature selection (CFS) method is used to remove irrelevant features, improving detection efficiency. An instance-based learning (IBL) algorithm classifies normal and cyberattack events using the selected optimal features. This study describes a set of experiments conducted on public datasets from a SCADA power system based on a 10-fold cross-validation technique. Experimental results show that the proposed approach achieves a high detection rate based on a small number of features drawn from SCADA power system measurements.
Abdu Gumaei; Mohammad Mehedi Hassan; Shamsul Huda; Rafiul Hassan; David Camacho; Javier Del Ser; Giancarlo Fortino. A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Applied Soft Computing 2020, 96, 106658 .
AMA StyleAbdu Gumaei, Mohammad Mehedi Hassan, Shamsul Huda, Rafiul Hassan, David Camacho, Javier Del Ser, Giancarlo Fortino. A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids. Applied Soft Computing. 2020; 96 ():106658.
Chicago/Turabian StyleAbdu Gumaei; Mohammad Mehedi Hassan; Shamsul Huda; Rafiul Hassan; David Camacho; Javier Del Ser; Giancarlo Fortino. 2020. "A robust cyberattack detection approach using optimal features of SCADA power systems in smart grids." Applied Soft Computing 96, no. : 106658.
The exponential growth of social media and online social networks (e.g., Facebook, Twitter, Instagram, and TikTok) has changed the daily lives of millions of people. The ease to accessing, gathering and processing available data and the high societal and industrial interest in such data have attracted the interest of a large of research disciplines. This special issue has been focused mainly on Data Science and Artificial Intelligence techniques, and their application to social network analysis. The issue provides a total of 12 selected papers (out of 65) that represent latest advances and developments in these areas.
David Camacho; Ma Victoria Luzón; Erik Cambria. New research methods & algorithms in social network analysis. Future Generation Computer Systems 2020, 114, 290 -293.
AMA StyleDavid Camacho, Ma Victoria Luzón, Erik Cambria. New research methods & algorithms in social network analysis. Future Generation Computer Systems. 2020; 114 ():290-293.
Chicago/Turabian StyleDavid Camacho; Ma Victoria Luzón; Erik Cambria. 2020. "New research methods & algorithms in social network analysis." Future Generation Computer Systems 114, no. : 290-293.
The fast growth of social media platforms and their related applications have dramatically changed the way billions of people relate to each other on the Web. This evolution of social media has blossomed in a plethora of end-user, or user-centered, applications that required innovative and efficient techniques for data processing. This was made possible recently thanks to advances in data science and artificial intelligence in fields like pattern recognition, information fusion, knowledge discovery and data visualization. This special issue provides a set of 12 selected papers (out of 65 submissions) that represent latest advances and developments in these areas.
David Camachoa; Ma Victoria Luzón; Erik Cambriac. New trends and applications in social media analytics. Future Generation Computer Systems 2020, 114, 318 -321.
AMA StyleDavid Camachoa, Ma Victoria Luzón, Erik Cambriac. New trends and applications in social media analytics. Future Generation Computer Systems. 2020; 114 ():318-321.
Chicago/Turabian StyleDavid Camachoa; Ma Victoria Luzón; Erik Cambriac. 2020. "New trends and applications in social media analytics." Future Generation Computer Systems 114, no. : 318-321.
Nowadays, Twitter is used by several political extremist groups to establish close communities on which the opinions are amplified following an echo-chamber effect. However, few literature analyses the effect of the use of an extremist discourse in relation to the relevance of these users on their online network. With the aim of analyzing this effect, this work studies the relationship between the use of indicators of extremist discourse from users belonging to an alt-right network on Twitter and their relevance on it. The network of alt-right users is created using the retweets of 96 accounts where the user relevance is measured by five different types of centrality metrics, including in-degree, eigenvector, k-shells, betweenness, and closeness. Both the linguistic indicators and the tone were analyzed using LIWC and VADER software. The network analysis outcomes show that user relevance on the network is indeed related to the use of an extremist discourse. Finally, this relationship is also tested on different corpus of texts and about different topics, being found that this relationship is more clear on retweets made by the users and when discussing about hate speech topics.
Javier Torregrosa; Ángel Panizo-Lledot; Gema Bello-Orgaz; David Camacho. Analyzing the relationship between relevance and extremist discourse in an alt-right network on Twitter. Social Network Analysis and Mining 2020, 10, 1 -17.
AMA StyleJavier Torregrosa, Ángel Panizo-Lledot, Gema Bello-Orgaz, David Camacho. Analyzing the relationship between relevance and extremist discourse in an alt-right network on Twitter. Social Network Analysis and Mining. 2020; 10 (1):1-17.
Chicago/Turabian StyleJavier Torregrosa; Ángel Panizo-Lledot; Gema Bello-Orgaz; David Camacho. 2020. "Analyzing the relationship between relevance and extremist discourse in an alt-right network on Twitter." Social Network Analysis and Mining 10, no. 1: 1-17.
Victor Rodriguez‐Fernandez; David Camacho. Special issue on “Machine Learning Challenges and Applications for Industry 4.0”. Expert Systems 2020, 37, 1 .
AMA StyleVictor Rodriguez‐Fernandez, David Camacho. Special issue on “Machine Learning Challenges and Applications for Industry 4.0”. Expert Systems. 2020; 37 (4):1.
Chicago/Turabian StyleVictor Rodriguez‐Fernandez; David Camacho. 2020. "Special issue on “Machine Learning Challenges and Applications for Industry 4.0”." Expert Systems 37, no. 4: 1.
Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the mission planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.
Cristian Ramirez-Atencia; Victor Rodriguez-Fernandez; David Camacho. A revision on multi-criteria decision making methods for multi-UAV mission planning support. Expert Systems with Applications 2020, 160, 113708 .
AMA StyleCristian Ramirez-Atencia, Victor Rodriguez-Fernandez, David Camacho. A revision on multi-criteria decision making methods for multi-UAV mission planning support. Expert Systems with Applications. 2020; 160 ():113708.
Chicago/Turabian StyleCristian Ramirez-Atencia; Victor Rodriguez-Fernandez; David Camacho. 2020. "A revision on multi-criteria decision making methods for multi-UAV mission planning support." Expert Systems with Applications 160, no. : 113708.
Social network based applications have experienced exponential growth in recent years. One of the reasons for this rise is that this application domain offers a particularly fertile place to test and develop the most advanced computational techniques to extract valuable information from the Web. The main contribution of this work is three-fold: (1) we provide an up-to-date literature review of the state of the art on social network analysis (SNA); (2) we propose a set of new metrics based on four essential features (or dimensions) in SNA; (3) finally, we provide a quantitative analysis of a set of popular SNA tools and frameworks. We have also performed a scientometric study to detect the most active research areas and application domains in this area. This work proposes the definition of four different dimensions, namely Pattern & Knowledge discovery, Information Fusion & Integration, Scalability, and Visualization, which are used to define a set of new metrics (termed degrees) in order to evaluate the different software tools and frameworks of SNA (a set of 20 SNA-software tools are analyzed and ranked following previous metrics). These dimensions, together with the defined degrees, allow evaluating and measure the maturity of social network technologies, looking for both a quantitative assessment of them, as to shed light to the challenges and future trends in this active area.
David Camacho; Ángel Panizo-Lledot; Gema Bello-Orgaz; Antonio Gonzalez-Pardo; Erik Cambria. The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Information Fusion 2020, 63, 88 -120.
AMA StyleDavid Camacho, Ángel Panizo-Lledot, Gema Bello-Orgaz, Antonio Gonzalez-Pardo, Erik Cambria. The four dimensions of social network analysis: An overview of research methods, applications, and software tools. Information Fusion. 2020; 63 ():88-120.
Chicago/Turabian StyleDavid Camacho; Ángel Panizo-Lledot; Gema Bello-Orgaz; Antonio Gonzalez-Pardo; Erik Cambria. 2020. "The four dimensions of social network analysis: An overview of research methods, applications, and software tools." Information Fusion 63, no. : 88-120.
Deep Learning models have consistently provided excellent results in highly complex domains. Its deep architecture of layers allows to face problems where classical machine learning approaches fail, or simply are not able to provide good enough solutions. However, these deep models usually involve a complex topology and hyperparameters that have to be carefully defined, typically following a grid search, in order to reach the most profitable configuration. Neuroevolution presents a perfect instrument to perform an evolutionary search pursuing this configuration. Through an evolution of the hyperparameters (activation functions, initialisation methods and optimiser) and the topology of the network (number and type layers and the number of units) it is possible to deeply explore the space of solutions in order to find the most proper architecture. Among the multiple applications of this approach, in this chapter we focus on the Android malware detection problem. This domain, which has led to a large amount of research in the last decade, presents interesting characteristics which make the application of Neuroevolution a logical approach to determine the architecture which will better discern between malicious and benign applications. In this research, we leverage a modification of EvoDeep, a framework for the evolution of valid deep layers sequences, to implement this evolutionary search using a genetic algorithm as means. To assess the approach, we use the OmniDroid dataset, a large set of static and dynamic features extracted from 22,000 malicious and benign Android applications. The results show that the application of a Neuroevolution based strategy leads to build Deep Learning models which provide high accuracy rates, greater than those obtained with classical machine learning approaches.
Alejandro Martín; David Camacho. Evolving the Architecture and Hyperparameters of DNNs for Malware Detection. Natural Computing Series 2020, 357 -377.
AMA StyleAlejandro Martín, David Camacho. Evolving the Architecture and Hyperparameters of DNNs for Malware Detection. Natural Computing Series. 2020; ():357-377.
Chicago/Turabian StyleAlejandro Martín; David Camacho. 2020. "Evolving the Architecture and Hyperparameters of DNNs for Malware Detection." Natural Computing Series , no. : 357-377.
This work is aimed at finding behavioural patterns among professional players of League of Legends, one of the greatest recent phenomena in the world of video games. For that purpose, Hidden Markov Models (HMM) are used to model the sequence of events produced by a gameplay. First, the set of interesting game events for analysis is defined, and based on that, each gameplay of the dataset is transformed into a sequences of events. Then, four HMMs will be trained with the data from four different groups of sequences, according to the team that produces the events of the sequence (red/blue) and to whether that sequence led to a victory of the team or not. Finally, the resulting HMMs will be visualized and compared in order to achieve some conclusions about the macro game strategy in League of Legends, which will help to understand the game at the highest level of its competition.
Alberto Mateos Rama; Victor Rodriguez-Fernandez; David Camacho. Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 419 -430.
AMA StyleAlberto Mateos Rama, Victor Rodriguez-Fernandez, David Camacho. Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():419-430.
Chicago/Turabian StyleAlberto Mateos Rama; Victor Rodriguez-Fernandez; David Camacho. 2020. "Finding Behavioural Patterns Among League of Legends Players Through Hidden Markov Models." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 419-430.
In a scenario where more and more individuals use online social network platforms as an instrument to propagate news without any control, it is necessary to design and implement new methods and techniques that guarantee the veracity of the disseminated news. In this paper, we propose a method to classify true and false news, commonly known as fake news, which exploits time series-based features extracted from the evolution of news, and features from the users involved in the news spreading. Applying our methodology over a real Twitter dataset of precategorized true and false news, we have obtained an accuracy of 84.61% in 10-fold cross-validation, and proved experimentally that all the selected features are relevant for this classification task.
Marialaura Previti; Victor Rodriguez-Fernandez; David Camacho; Vincenza Carchiolo; Michele Malgeri. Fake News Detection Using Time Series and User Features Classification. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 339 -353.
AMA StyleMarialaura Previti, Victor Rodriguez-Fernandez, David Camacho, Vincenza Carchiolo, Michele Malgeri. Fake News Detection Using Time Series and User Features Classification. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():339-353.
Chicago/Turabian StyleMarialaura Previti; Victor Rodriguez-Fernandez; David Camacho; Vincenza Carchiolo; Michele Malgeri. 2020. "Fake News Detection Using Time Series and User Features Classification." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 339-353.
This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data is not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data, the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time series log is carried out, highlighting the benefits of this change of paradigm.
Victor Rodriguez-Fernandez; Agnieszka Trzcionkowska; Antonio Gonzalez-Pardo; Edyta Brzychczy; Grzegorz J. Nalepa; David Camacho. Conformance Checking for Time-Series-Aware Processes. IEEE Transactions on Industrial Informatics 2020, 17, 871 -881.
AMA StyleVictor Rodriguez-Fernandez, Agnieszka Trzcionkowska, Antonio Gonzalez-Pardo, Edyta Brzychczy, Grzegorz J. Nalepa, David Camacho. Conformance Checking for Time-Series-Aware Processes. IEEE Transactions on Industrial Informatics. 2020; 17 (2):871-881.
Chicago/Turabian StyleVictor Rodriguez-Fernandez; Agnieszka Trzcionkowska; Antonio Gonzalez-Pardo; Edyta Brzychczy; Grzegorz J. Nalepa; David Camacho. 2020. "Conformance Checking for Time-Series-Aware Processes." IEEE Transactions on Industrial Informatics 17, no. 2: 871-881.
Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company’s Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company’s and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.
Gema Bello-Orgaz; Rus M. Mesas; Carmen Zarco; Victor Rodriguez; Oscar Cordón; David Camacho. Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter. Information Processing & Management 2020, 57, 102220 .
AMA StyleGema Bello-Orgaz, Rus M. Mesas, Carmen Zarco, Victor Rodriguez, Oscar Cordón, David Camacho. Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter. Information Processing & Management. 2020; 57 (5):102220.
Chicago/Turabian StyleGema Bello-Orgaz; Rus M. Mesas; Carmen Zarco; Victor Rodriguez; Oscar Cordón; David Camacho. 2020. "Marketing analysis of wineries using social collective behavior from users’ temporal activity on Twitter." Information Processing & Management 57, no. 5: 102220.