Literatura académica sobre el tema "Classification de séries temporelles biomédicales"
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Artículos de revistas sobre el tema "Classification de séries temporelles biomédicales"
Le Bris, Arnaud, Cyril Wendl, Nesrine Chehata, Anne Puissant y Tristan Postadjian. "Fusion tardive d'images SPOT-6/7 et de données multi-temporelles Sentinel-2 pour la détection de la tâche urbaine". Revue Française de Photogrammétrie et de Télédétection, n.º 217-218 (21 de septiembre de 2018): 87–97. http://dx.doi.org/10.52638/rfpt.2018.415.
Texto completoNjutapvoui, Nourdi, RUDANT Jean Paul y ONGUENE RAPHAEL. "EVALUATION DU POTENTIEL DES SERIES D’IMAGES MULTI-TEMPORELLES OPTIQUE ET RADAR DES SATELLITES SENTINEL 1 & 2 POUR LE SUIVI D’UNE ZONE CÔTIÈRE EN CONTEXTE TROPICAL : CAS DE L’ESTUAIRE DU CAMEROUN POUR LA PÉRIODE 2015-2020". Revue Française de Photogrammétrie et de Télédétection 223 (25 de agosto de 2021): 88–103. http://dx.doi.org/10.52638/rfpt.2021.586.
Texto completoDubreuil, Vincent, Damien Arvor y Nathan Debortoli. "Monitoring the pioneer frontier and agricultural intensification in Mato Grosso using SPOT vegetation images". Revue Française de Photogrammétrie et de Télédétection, n.º 200 (19 de abril de 2014): 2–11. http://dx.doi.org/10.52638/rfpt.2012.56.
Texto completoAcharki, Siham, Mina Amharref, Pierre-Louis Frison y Abdes Samed Bernoussi. "CARTOGRAPHIE DES CULTURES DANS LE PÉRIMÈTRE DU LOUKKOS (MAROC) : APPORT DE LA TÉLÉDÉTECTION RADAR ET OPTIQUE". Revue Française de Photogrammétrie et de Télédétection, n.º 222 (26 de noviembre de 2020): 15–29. http://dx.doi.org/10.52638/rfpt.2020.481.
Texto completoGranda, Catalina, Luis Guillermo Pérez y Juan Carlos Muñoz. "The Environmental Kuznets Curve for Water Quality: An Analysis of its Appropriateness Using Unit Root and Cointegration Tests". Lecturas de Economía, n.º 69 (16 de febrero de 2009): 221–44. http://dx.doi.org/10.17533/udea.le.n69a744.
Texto completoAgudelo Rueda, Diego y Mónica Arango Arango. "La curva de rendimientos a plazo y las expectativas de tasas de interés en el mercado de renta fija en Colombia, 2002-2007". Lecturas de Economía, n.º 68 (24 de noviembre de 2008): 39–66. http://dx.doi.org/10.17533/udea.le.n68a264.
Texto completoBouali, Fatma, Frédéric Plantard, Amina Bouséba y Gilles Venturini. "Fouille visuelle de données temporelles avec DataTube2". Journal d'Interaction Personne-Système Volume 2 (6 de octubre de 2014). http://dx.doi.org/10.46298/jips.65.
Texto completoBailly-Comte, V., B. Ladouche, J. B. Charlier, V. Hakoun y J. C. Maréchal. "XLKarst, un outil Excel pour l'analyse des séries temporelles, l'analyse des courbes de récession des sources et la classification des aquifères karstiques". Hydrogeology Journal, 25 de octubre de 2023. http://dx.doi.org/10.1007/s10040-023-02710-w.
Texto completoTesis sobre el tema "Classification de séries temporelles biomédicales"
Khessiba, Souhir. "Stratégies d’optimisation des hyper-paramètres de réseaux de neurones appliqués aux signaux temporels biomédicaux". Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAE003.
Texto completoThis thesis focuses on optimizing the hyperparameters of convolutional neural networks (CNNs) in the medical domain, proposing an innovative approach to improve the performance of decision-making models in the biomedical field. Through the use of a hybrid approach, GS-TPE, to effectively adjust the hyperparameters of complex neural network models, this research has demonstrated significant improvements in the classification of temporal biomedical signals, such as vigilance states, from physiological signals such as electroencephalogram (EEG). Furthermore, by introducing a new DNN architecture, STGCN, for the classification of gestures associated with pathologies such as knee osteoarthritis and Parkinson's disease from video gait analysis, these works offer new perspectives for enhancing medical diagnosis and management through advancements in artificial intelligence
Bailly, Adeline. "Classification de séries temporelles avec applications en télédétection". Thesis, Rennes 2, 2018. http://www.theses.fr/2018REN20021/document.
Texto completoTime Series Classification (TSC) has received an important amount of interest over the past years due to many real-life applications. In this PhD, we create new algorithms for TSC, with a particular emphasis on Remote Sensing (RS) time series data. We first propose the Dense Bag-of-Temporal-SIFT-Words (D-BoTSW) method that uses dense local features based on SIFT features for 1D data. Extensive experiments exhibit that D-BoTSW significantly outperforms nearly all compared standalone baseline classifiers. Then, we propose an enhancement of the Learning Time Series Shapelets (LTS) algorithm called Adversarially-Built Shapelets (ABS) based on the introduction of adversarial time series during the learning process. Adversarial time series provide an additional regularization benefit for the shapelets and experiments show a performance improvementbetween the baseline and our proposed framework. Due to the lack of available RS time series datasets,we also present and experiment on two remote sensing time series datasets called TiSeLaCand Brazilian-Amazon
Jebreen, Kamel. "Modèles graphiques pour la classification et les séries temporelles". Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0248/document.
Texto completoFirst, in this dissertation, we will show that Bayesian networks classifiers are very accurate models when compared to other classical machine learning methods. Discretising input variables often increase the performance of Bayesian networks classifiers, as does a feature selection procedure. Different types of Bayesian networks may be used for supervised classification. We combine such approaches together with feature selection and discretisation to show that such a combination gives rise to powerful classifiers. A large choice of data sets from the UCI machine learning repository are used in our experiments, and the application to Epilepsy type prediction based on PET scan data confirms the efficiency of our approach. Second, in this dissertation we also consider modelling interaction between a set of variables in the context of time series and high dimension. We suggest two approaches; the first is similar to the neighbourhood lasso where the lasso model is replaced by Support Vector Machines (SVMs); the second is a restricted Bayesian network for time series. We demonstrate the efficiency of our approaches simulations using linear and nonlinear data set and a mixture of both
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles". Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066324/document.
Texto completoThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Dilmi, Mohamed Djallel. "Méthodes de classification des séries temporelles : application à un réseau de pluviomètres". Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS087.pdf.
Texto completoThe impact of climat change on the temporal evolution of precipitation as well as the impact of the Parisian heat island on the spatial distribution of précipitation motivate studying the varaibility of the water cycle on a small scale on île-de-france. one way to analyse this varaibility using the data from a rain gauge network is to perform a clustring on time series measured by this network. In this thesis, we have explored two approaches for time series clustring : for the first approach based on the description of series by characteristics, an algorithm for selecting characteristics based on genetic algorithms and topological maps has been proposed. for the second approach based on shape comparaison, a measure of dissimilarity (iterative downscaling time warping) was developed to compare two rainfall time series. Then the limits of the two approaches were discuddes followed by a proposition of a mixed approach that combine the advantages of each approach. The approach was first applied to the evaluation of spatial variability of precipitation on île-de-france. For the evaluation of the temporal variability of the precpitation, a clustring on the precipitation events observed by a station was carried out then extended on the whole rain gauge network. The application on the historical series of Paris-Montsouris (1873-2015) makes it possible to automatically discriminate "remarkable" years from a meteorological point of view
Ziat, Ali Yazid. "Apprentissage de représentation pour la prédiction et la classification de séries temporelles". Electronic Thesis or Diss., Paris 6, 2017. http://www.theses.fr/2017PA066324.
Texto completoThis thesis deals with the development of time series analysis methods. Our contributions focus on two tasks: time series forecasting and classification. Our first contribution presents a method of prediction and completion of multivariate and relational time series. The aim is to be able to simultaneously predict the evolution of a group of time series connected to each other according to a graph, as well as to complete the missing values in these series (which may correspond for example to a failure of a sensor during a given time interval). We propose to use representation learning techniques to forecast the evolution of the series while completing the missing values and taking into account the relationships that may exist between them. Extensions of this model are proposed and described: first in the context of the prediction of heterogeneous time series and then in the case of the prediction of time series with an expressed uncertainty. A prediction model of spatio-temporal series is then proposed, in which the relations between the different series can be expressed more generally, and where these can be learned.Finally, we are interested in the classification of time series. A joint model of metric learning and time-series classification is proposed and an experimental comparison is conducted
Rhéaume, François. "Une méthode de machine à état liquide pour la classification de séries temporelles". Thesis, Université Laval, 2012. http://www.theses.ulaval.ca/2012/28815/28815.pdf.
Texto completoThere are a number of reasons that motivate the interest in computational neuroscience for engineering applications of artificial intelligence. Among them is the speed at which the domain is growing and evolving, promising further capabilities for artificial intelligent systems. In this thesis, a method that exploits the recent advances in computational neuroscience is presented: the liquid state machine. A liquid state machine is a biologically inspired computational model that aims at learning on input stimuli. The model constitutes a promising temporal pattern recognition tool and has shown to perform very well in many applications. In particular, temporal pattern recognition is a problem of interest in military surveillance applications such as automatic target recognition. Until now, most of the liquid state machine implementations for spatiotemporal pattern recognition have remained fairly similar to the original model. From an engineering perspective, a challenge is to adapt liquid state machines to increase their ability for solving practical temporal pattern recognition problems. Solutions are proposed. The first one concentrates on the sampling of the liquid state. In this subject, a method that exploits frequency features of neurons is defined. The combination of different liquid state vectors is also discussed. Secondly, a method for training the liquid is developed. The method implements synaptic spike-timing dependent plasticity to shape the liquid. A new class-conditional approach is proposed, where different networks of neurons are trained exclusively on particular classes of input data. For the suggested liquid sampling methods and the liquid training method, comparative tests were conducted with both simulated and real data sets from different application areas. The tests reveal that the methods outperform the conventional liquid state machine approach. The methods are even more promising in that the results are obtained without optimization of many internal parameters for the different data sets. Finally, measures of the liquid state are investigated for predicting the performance of the liquid state machine.
Petitjean, François. "Dynamic time warping : apports théoriques pour l'analyse de données temporelles : application à la classification de séries temporelles d'images satellites". Thesis, Strasbourg, 2012. http://www.theses.fr/2012STRAD023.
Texto completoSatellite Image Time Series are becoming increasingly available and will continue to do so in the coming years thanks to the launch of space missions, which aim at providing a coverage of the Earth every few days with high spatial resolution (ESA’s Sentinel program). In the case of optical imagery, it will be possible to produce land use and cover change maps with detailed nomenclatures. However, due to meteorological phenomena, such as clouds, these time series will become irregular in terms of temporal sampling. In order to consistently handle the huge amount of information that will be produced (for instance, Sentinel-2 will cover the entire Earth’s surface every five days, with 10m to 60m spatial resolution and 13 spectral bands), new methods have to be developed. This Ph.D. thesis focuses on the “Dynamic Time Warping” similarity measure, which is able to take the most of the temporal structure of the data, in order to provide an efficient and relevant analysis of the remotely observed phenomena
Benkabou, Seif-Eddine. "Détection d’anomalies dans les séries temporelles : application aux masses de données sur les pneumatiques". Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1046/document.
Texto completoAnomaly detection is a crucial task that has attracted the interest of several research studies in machine learning and data mining communities. The complexity of this task depends on the nature of the data, the availability of their labeling and the application framework on which they depend. As part of this thesis, we address this problem for complex data and particularly for uni and multivariate time series. The term "anomaly" can refer to an observation that deviates from other observations so as to arouse suspicion that it was generated by a different generation process. More generally, the underlying problem (also called novelty detection or outlier detection) aims to identify, in a set of data, those which differ significantly from others, which do not conform to an "expected behavior" (which could be defined or learned), and which indicate a different mechanism. The "abnormal" patterns thus detected often result in critical information. We focus specifically on two particular aspects of anomaly detection from time series in an unsupervised fashion. The first is global and consists in detecting abnormal time series compared to an entire database, whereas the second one is called contextual and aims to detect locally, the abnormal points with respect to the global structure of the relevant time series. To this end, we propose an optimization approaches based on weighted clustering and the warping time for global detection ; and matrix-based modeling for the contextual detection. Finally, we present several empirical studies on public data to validate the proposed approaches and compare them with other known approaches in the literature. In addition, an experimental validation is provided on a real problem, concerning the detection of outlier price time series on the tyre data, to meet the needs expressed by, LIZEO, the industrial partner of this thesis
Varasteh, Yazdi Saeed. "Représentations parcimonieuses et apprentissage de dictionnaires pour la classification et le clustering de séries temporelles". Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM062/document.
Texto completoLearning dictionary for sparse representing time series is an important issue to extract latent temporal features, reveal salient primitives and sparsely represent complex temporal data. This thesis addresses the sparse coding and dictionary learning problem for time series classification and clustering under time warp. For that, we propose a time warp invariant sparse coding and dictionary learning framework where both input samples and atoms define time series of different lengths that involve varying delays.In the first part, we formalize an L0 sparse coding problem and propose a time warp invariant orthogonal matching pursuit based on a new cosine maximization time warp operator. For the dictionary learning stage, a non linear time warp invariant kSVD (TWI-kSVD) is proposed. Thanks to a rotation transformation between each atom and its sibling atoms, a singular value decomposition is used to jointly approximate the coefficients and update the dictionary, similar to the standard kSVD. In the second part, a time warp invariant dictionary learning for time series clustering is formalized and a gradient descent solution is proposed.The proposed methods are confronted to major shift invariant, convolved and kernel dictionary learning methods on several public and real temporal data. The conducted experiments show the potential of the proposed frameworks to efficiently sparse represent, classify and cluster time series under time warp
Capítulos de libros sobre el tema "Classification de séries temporelles biomédicales"
DUMITRU, Corneliu Octavian y Mihai DATCU. "Analyse sémantique de séries chronologiques d’images satellitaires". En Détection de changements et analyse des séries temporelles d’images 2, 99–123. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch3.
Texto completoMOLINIER, Matthieu, Jukka MIETTINEN, Dino IENCO, Shi QIU y Zhe ZHU. "Analyse de séries chronologiques d’images satellitaires optiques pour des applications environnementales". En Détection de changements et analyse des séries temporelles d’images 2, 125–74. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch4.
Texto completoHEDHLI, Ihsen, Gabriele MOSER, Sebastiano B. SERPICO y Josiane ZERUBIA. "Champs de Markov et séries chronologiques d’images multicapteurs et multirésolution". En Détection de changements et analyse des séries temporelles d’images 2, 5–39. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch1.
Texto completoPELLETIER, Charlotte y Silvia VALERO. "Techniques de classification basées sur les pixels pour les séries chronologiques d’images satellitaires". En Détection de changements et analyse des séries temporelles d’images 2, 41–98. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch2.
Texto completoATTO, Abdourrahmane M., Héla HADHRI, Flavien VERNIER y Emmanuel TROUVÉ. "Apprentissage multiclasse multi-étiquette de changements d’état à partir de séries chronologiques d’images". En Détection de changements et analyse des séries temporelles d’images 2, 247–71. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9057.ch6.
Texto completoZANETTI, Massimo, Francesca BOVOLO y Lorenzo BRUZZONE. "Statistiques par différences pour les changements multispectraux". En Détection de changements et analyse des séries temporelles d’images 1, 247–303. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9056.ch9.
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