Browsing by Keyword "Machine learning"
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Item AI-based medical e-diagnosis for fast and automatic ventricular volume measurement in patients with normal pressure hydrocephalus(2022-02-24) Zhou, Xi; Ye, Qinghao; Yang, Xiaolin; Chen, Jiakun; Ma, Haiqin; Xia, Jun; Del Ser, Javier; Yang, Guang; IABased on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient’s ventricles.Item A cognitive robotic ecology approach to self-configuring and evolving AAL systems(2015-10-01) Dragone, Mauro; Amato, Giuseppe; Bacciu, Davide; Chessa, Stefano; Coleman, Sonya; Di Rocco, Maurizio; Gallicchio, Claudio; Gennaro, Claudio; Lozano, Hector; Maguire, Liam; McGinnity, Martin; Micheli, Alessio; O׳Hare, Gregory M.P.; Renteria, Arantxa; Saffiotti, Alessandro; Vairo, Claudio; Vance, P.; O'Hare, Gregory M.P.; Medical Technologies; Robótica MédicaRobotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.Item Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles(2019-02) Dendaluce Jahnke, Martin; Cosco, Francesco; Novickis, Rihards; Pérez Rastelli, Joshué; Gomez-Garay, Vicente; Tecnalia Research & Innovation; CCAMThe combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Item From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability(Multidisciplinary Digital Publishing Institute (MDPI), 2021-02-05) Laña, Ibai; Sanchez-Medina, Javier J.; Vlahogianni, Eleni I.; Del Ser, JavierAdvances in Data Science permeate every field of Transportation Science and Engineering, resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure, vehicles or the travelers’ personal devices act as sources of data flows that are eventually fed into software running on automatic devices, actuators or control systems producing, in turn, complex information flows among users, traffic managers, data analysts, traffic modeling scientists, etc. These information flows provide enormous opportunities to improve model development and decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes; in other words, for data-based models to fully become actionable. Grounded in this described data modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic to its three compounding stages, namely, data fusion, adaptive learning and model evaluation. We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying the majority of ITS applications. Finally, we provide a prospect of current research lines within Data Science that can bring notable advances to data-based ITS modeling, which will eventually bridge the gap towards the practicality and actionability of such models.Item An IoT−based system that aids learning from human behavior: A potential application for the care of the elderly: A potential application for the care of the elderly(2017-10-04) Saralegui, Unai; Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThe goal of this paper is to describe the way of taking advantage of the non-intrusive indoor air quality monitoring system by using data oriented modeling technologies to determine specific human behaviors. The specific goal is to determine when a human presence occurs in a specific room, while the objective is to extend the use of the existing indoor air quality monitoring system to provide a higher level aspect of the house usage. Different models have been trained by means of machine learning algorithms using the available temperature, relative humidity and CO2 levels to determine binary occupation. The paper will discuss the overall acceptable quality provided by those classifiers when operating over new data not previously seen. Therefore, a recommendation on how to proceed is provided, as well as the confidence level regarding the new created knowledge. Such knowledge could bring additional opportunities in the care of the elderly for specific diseases that are usually accompanied by changes in patterns of behavior.Item Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People: Dealing with noisy patterns to help older people(2019-07-02) Antón, Miguel Ángel; Ordieres-Meré, Joaquín; Saralegui, Unai; Sun, Shengjing; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓN; Tecnalia Research & InnovationThis paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.Item A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines(2016-03-25) Villar-Rodríguez, E.; Del Ser, Javier; Torre-Bastida, A. I.; Bilbao, M. N.; Salcedo-Sanz, Sancho; Quantum; IA; HPAThe proliferation of social networks and their usage by a wide spectrum of user profiles has been specially notable in the last decade. A social network is frequently conceived as a strongly interlinked community of users, each featuring a compact neighborhood tightly and actively connected through different communication flows. This realm unleashes a rich substrate for a myriad of malicious activities aimed at unauthorizedly profiting from the user itself or from his/her social circle. This manuscript elaborates on a practical approach for the detection of identity theft in social networks, by which the credentials of a certain user are stolen and used without permission by the attacker for its own benefit. The proposed scheme detects identity thefts by exclusively analyzing connection time traces of the account being tested in a nonintrusive manner. The manuscript formulates the detection of this attack as a binary classification problem, which is tackled by means of a support vector classifier applied over features inferred from the original connection time traces of the user. Simulation results are discussed in depth toward elucidating the potentiality of the proposed system as the first step of a more involved impersonation detection framework, also relying on connectivity patterns and elements from language processing.Item Persistence in complex systems(2022-04-29) Salcedo-Sanz, S.; Casillas-Pérez, D.; Del Ser, J.; Casanova-Mateo, C.; Cuadra, L.; Piles, M.; Camps-Valls, G.; IAPersistence is an important characteristic of many complex systems in nature, related to how long the system remains at a certain state before changing to a different one. The study of complex systems’ persistence involves different definitions and uses different techniques, depending on whether short-term or long-term persistence is considered. In this paper we discuss the most important definitions, concepts, methods, literature and latest results on persistence in complex systems. Firstly, the most used definitions of persistence in short-term and long-term cases are presented. The most relevant methods to characterize persistence are then discussed in both cases. A complete literature review is also carried out. We also present and discuss some relevant results on persistence, and give empirical evidence of performance in different detailed case studies, for both short-term and long-term persistence. A perspective on the future of persistence concludes the work.Item Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming(2019-06) Diez-Olivan, Alberto; Averós, Xavier; Sanz, Ricardo; Sierra, Basilio; Estevez, Inma; Tecnalia Research & InnovationAn efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implies that relationships across parameters that are critical to assure animal welfare and performance are difficult to determine. In this paper a novel decision support system based on environmental indicators and on weights, leg problems and mortality rates is proposed to address this issue. The data-driven modeling process is performed by a quantile regression forests approach that allows estimating growth, welfare and mortality parameters on the basis of environmental deviations from optimal farm conditions. Resulting models also provide confidence intervals able to deal with uncertainty. They are deployed in farm, offering an accessible tool for farmers, veterinarians and technical personnel. Experimental results involving 20 flocks of broiler meat chickens from different farms show the validity of the system, obtaining robust prediction intervals and high accuracy, namely over 81% for every model. The in-field use of the proposed approach will facilitate an efficient and animal welfare-friendly production management.Item Relationship between electrocardiogram‐based features and personality traits: Machine learning approach: Machine learning approach(2022-01) Boljanić, Tanja; Miljković, Nadica; Lazarevic, Ljiljana B.; Knezevic, Goran; Milašinović, Goran; SGBackground: Based on the known relationship between the human emotion and standard surface electrocardiogram (ECG), we explored the relationship between features extracted from standard ECG recorded during relaxation and seven personality traits (Honesty/humility, Emotionality, eXtraversion, Agreeableness, Conscientiousness, Openness, and Disintegration) by using the machine learning (ML) approach which learns from the ECG-based features and predicts the appropriate personality trait by adopting an automated software algorithm. Methods: A total of 71 healthy university students participated in the study. For quantification of 62 ECG-based parameters (heart rate variability, as well as temporal and amplitude-based parameters) for each ECG record, we used computation procedures together with publicly available data and code. Among 62 parameters, 34 were segregated into separate features according to their diagnostic relevance in clinical practice. To examine the feature influence on personality trait classification and to perform classification, we used random forest ML algorithm. Results: Classification accuracy when clinically relevant ECG features were employed was high for Disintegration (81.3%) and Honesty/humility (75.0%) and moderate to high for Openness (73.3%) and Conscientiousness (70%), while it was low for Agreeableness (56.3%), eXtraversion (47.1%), and Emotionality (43.8%). When all calculated features were used, the classification accuracies were the same or lower, except for the eXtraversion (52.9%). Correlation analysis for selected features is presented. Conclusions: Results indicate that clinically relevant features might be applicable for personality traits prediction, although no remarkable differences were found among selected groups of parameters. Physiological associations of established relationships should be further explored.Item A statistical recommendation model of mobile services based on contextual evidences(2012-01) Picón, Artzai; Rodríguez-Vaamonde, Sergio; Jaén, Javier; Mocholi, Jose Antonio; García, David; Cadenas, Alejandro; COMPUTER_VISION; Tecnalia Research & InnovationMobile devices are undergoing great advances in recent years allowing users to access an increasing number of services or personalized applications that can help them select the best restaurant, locate certain shops, choose the best way home or rent the best film. However this great quantity of services does not require the user to find and select those services needed for each specific situation. The classical approaches link some preferences to certain services, include the recommendations given by other users or even include certain fixed rules in order to choose the most appropriate services. However, since these methods assume that user needs can be modelled by fixed rules or preferences, they fail when modelling different users or makes them difficult to train. In this paper we propose a new algorithm that learns from the user's actions in different contextual situations, which allows to properly infer the most appropriate recommendations for a user in a specific contextual situation. This model, by using of a double knowledge diffusion approach, has been specifically designed to face the inherent lack of learning evidences, computational cost and continuous training requirements and, therefore, overcomes the performance and convergence rates offered by other learning methodologies.Item A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges: definitions, literature review, trends and challenges(2022-12-12) Torregrosa, Javier; Bello-Orgaz, Gema; Martínez-Cámara, Eugenio; Ser, Javier Del; Camacho, David; IAExtremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.Item Taking advantage of an existing indoor climate monitorization for measuring occupancy(2017) Saralegui, Unai; Anton, Miguel Angel; Ordieres-Mere, Joaquin; Tecnalia Research & Innovation; DIGITALIZACIÓN Y AUTOMATIZACIÓN DE LA CONSTRUCCIÓNThis paper describes a procedure to gain additional information from an already existing infrastructure primarily designed for other purposes. The deployed sensor network consists of wirelessly communicated indoor climate monitoring sensors, for which it is tried to extend its usage by determining occupancy in the room they are located, in that way the system provides a higher level aspect of the house usage. An elderly caring institution’s building has been monitored for one year obtaining data about temperature, relative humidity and CO2 levels from five different rooms. Such data shows some interesting patterns as the air flow between the rooms which should be considered in any real case scenario. The data has been used to train some machine learning models, which show acceptable quality overall suggesting to use this kind of sensing equipment to perform an occupancy monitoring non-intrusively. The acquired knowledge could bring additional opportunities in the care of the elderly, especially for specific diseases that are usually accompanied by changes in patterns of behaviour. By using the occupancy status it could be possible to determine changes in the daily patterns in that segment of the population which could be an indicative of the initial states of a disease or a worsening in it.Item Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification(Springer Science and Business Media Deutschland GmbH, 2021-01-11) Fernández-Navamuel, Ana; Zamora-Sánchez, Diego; Varona-Poncela, Tomás; Jiménez-Fernández, Carlos; Díez-Hernández, Jesús; García-Sánchez, David; Pardo, David; Rizzo, Piervincenzo; Milazzo, Alberto; E&I SEGURAS Y RESILIENTES; Tecnalia Research & InnovationWe present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain).Item Why deep learning performs better than classical machine learning?(2020-03-01) PICON RUIZ, ARTZAI; ALVAREZ GILA, AITOR; IRUSTA, UNAI; ECHAZARRA HUGUET, JONE; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALNot available