Academic literature on the topic 'Ensemble de données multimodal'
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Journal articles on the topic "Ensemble de données multimodal"
Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (March 1, 2024): 1524. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.
Full textRay, Jean-Claude. "Données censurées et modèles de durées." Recherche et Applications en Marketing (French Edition) 3, no. 2 (June 1988): 77–88. http://dx.doi.org/10.1177/076737018800300204.
Full textMiller, Robert N., and Laura L. Ehret. "Ensemble Generation for Models of Multimodal Systems." Monthly Weather Review 130, no. 9 (September 2002): 2313–33. http://dx.doi.org/10.1175/1520-0493(2002)130<2313:egfmom>2.0.co;2.
Full textPeng, Yang, Xiaofeng Zhou, Daisy Zhe Wang, Ishan Patwa, Dihong Gong, and Chunsheng Victor Fang. "Multimodal Ensemble Fusion for Disambiguation and Retrieval." IEEE MultiMedia 23, no. 2 (April 2016): 42–52. http://dx.doi.org/10.1109/mmul.2016.26.
Full textHathout, Nabil. "La question des données en morphologie." Cahiers du Centre de Linguistique et des Sciences du Langage, no. 45 (April 23, 2016): 123–60. http://dx.doi.org/10.26034/la.cdclsl.2016.513.
Full textBouchard, Diana C. "L’autocorrélation spatiale et les données de santé : une étude préliminaire." Cahiers de géographie du Québec 20, no. 51 (April 12, 2005): 521–38. http://dx.doi.org/10.7202/021333ar.
Full textDunn, S., J. Bottomley, A. Ali, and M. Walker. "Vérification de la qualité de la base de données périnatales Niday pour 2008 : rapport sur un projet d’assurance de la qualité." Maladies chroniques et blessures au Canada 32, no. 1 (December 2011): 36–48. http://dx.doi.org/10.24095/hpcdp.32.1.05f.
Full textAriyanti, Whenty, Tassadaq Hussain, Jia-Ching Wang, Chi-Tei Wang, Shih-Hau Fang, and Yu Tsao. "Ensemble and Multimodal Learning for Pathological Voice Classification." IEEE Sensors Letters 5, no. 7 (July 2021): 1–4. http://dx.doi.org/10.1109/lsens.2021.3091141.
Full textMardinly, Alan R., Ian Antón Oldenburg, Nicolas C. Pégard, Savitha Sridharan, Evan H. Lyall, Kirill Chesnov, Stephen G. Brohawn, Laura Waller, and Hillel Adesnik. "Precise multimodal optical control of neural ensemble activity." Nature Neuroscience 21, no. 6 (April 30, 2018): 881–93. http://dx.doi.org/10.1038/s41593-018-0139-8.
Full textLiu, Yu. "Multimodal Particle Swarm Optimization for Neural Network Ensemble." Journal of Computer Research and Development 42, no. 9 (2005): 1519. http://dx.doi.org/10.1360/crad20050910.
Full textDissertations / Theses on the topic "Ensemble de données multimodal"
Chen, Jianan. "Deep Learning Based Multimodal Retrieval." Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0019.
Full textMultimodal tasks play a crucial role in the progression towards achieving general artificial intelligence (AI). The primary goal of multimodal retrieval is to employ machine learning algorithms to extract relevant semantic information, bridging the gap between different modalities such as visual images, linguistic text, and other data sources. It is worth noting that the information entropy associated with heterogeneous data for the same high-level semantics varies significantly, posing a significant challenge for multimodal models. Deep learning-based multimodal network models provide an effective solution to tackle the difficulties arising from substantial differences in information entropy. These models exhibit impressive accuracy and stability in large-scale cross-modal information matching tasks, such as image-text retrieval. Furthermore, they demonstrate strong transfer learning capabilities, enabling a well-trained model from one multimodal task to be fine-tuned and applied to a new multimodal task, even in scenarios involving few-shot or zero-shot learning. In our research, we develop a novel generative multimodal multi-view database specifically designed for the multimodal referential segmentation task. Additionally, we establish a state-of-the-art (SOTA) benchmark and multi-view metric for referring expression segmentation models in the multimodal domain. The results of our comparative experiments are presented visually, providing clear and comprehensive insights
Wang, Xin. "Gaze based weakly supervised localization for image classification : application to visual recognition in a food dataset." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066577/document.
Full textIn this dissertation, we discuss how to use the human gaze data to improve the performance of the weak supervised learning model in image classification. The background of this topic is in the era of rapidly growing information technology. As a consequence, the data to analyze is also growing dramatically. Since the amount of data that can be annotated by the human cannot keep up with the amount of data itself, current well-developed supervised learning approaches may confront bottlenecks in the future. In this context, the use of weak annotations for high-performance learning methods is worthy of study. Specifically, we try to solve the problem from two aspects: One is to propose a more time-saving annotation, human eye-tracking gaze, as an alternative annotation with respect to the traditional time-consuming annotation, e.g. bounding box. The other is to integrate gaze annotation into a weakly supervised learning scheme for image classification. This scheme benefits from the gaze annotation for inferring the regions containing the target object. A useful property of our model is that it only exploits gaze for training, while the test phase is gaze free. This property further reduces the demand of annotations. The two isolated aspects are connected together in our models, which further achieve competitive experimental results
Costa, Daniel Moura Martins da. "Ensemble baseado em métodos de Kernel para reconhecimento biométrico multimodal." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-28072016-190335/.
Full textWith the advancement of technology, traditional strategies for identifying people become more susceptible to failure, in order to overcome these difficulties some approaches have been proposed in the literature. Among these approaches highlights the Biometrics. The field of Biometrics encompasses a wide variety of technologies used to identify and verify the person\'s identity through the measurement and analysis of physiological and behavioural aspects of the human body. As a result, biometrics has a wide field of applications in systems that require precise identification of their users. The most popular biometric systems are based on face recognition and fingerprint matching. Furthermore, there are other biometric systems that utilize iris and retinal scan, speech, face, and hand geometry. In recent years, biometrics authentication has seen improvements in reliability and accuracy, with some of the modalities offering good performance. However, even the best biometric modality is facing problems. Recently, big efforts have been undertaken aiming to employ multiple biometric modalities in order to make the authentication process less vulnerable to attacks. Multimodal biometrics is a relatively new approach to biometrics representation that consolidate multiple biometric modalities. Multimodality is based on the concept that the information obtained from different modalities complement each other. Consequently, an appropriate combination of such information can be more useful than using information from single modalities alone. The main issues involved in building a unimodal biometric System concern the definition of the feature extraction technique and type of classifier. In the case of a multimodal biometric System, in addition to these issues, it is necessary to define the level of fusion and fusion strategy to be adopted. The aim of this dissertation is to investigate the use of committee machines to fuse multiple biometric modalities, considering different fusion strategies, taking into account advanced methods in machine learning. In particular, it will give emphasis to the analyses of different types of machine learning methods based on Kernel and its organization into arrangements committee machines, aiming biometric authentication based on face, fingerprint and iris. The results showed that the proposed approach is capable of designing a multimodal biometric System with recognition rate than those obtained by the unimodal biometrics Systems.
Larsson, Sanna. "Kommunikation - vad är det? : En studie av musikalisk kommunikation i ensemblesammanhang." Thesis, Karlstads universitet, Institutionen för konstnärliga studier, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-33627.
Full textThe study focuses on how singers communicate with their fellow musicians in music ensembles considering the multimodal perspective of communication and learning conditions. With the help of video recordings of my own communication within an ensemble I have analysed which semiotic resources that are used and my underlying intentions with them. The result shows that the communication between the group members first are established after a while into the learning process and that the singing part shows to be the prime communicator for me in this case where dynamic, articulating and playfulness indicate security or insecurity about the form of the song and the melody. It also works as a guide to where I am at in the learning process. In the chapter on discussion I present, among other things, how the first ensemble lesson is created due to the multimodal perspective on favourable learning conditions and also the importance of body language for favourable communication in music ensembles.
Lösegård, Linus. "”Det är inte bara ord som används” : En studie av musiklärares sätt att kommunicera med sina elever i undervisning av ensemble." Thesis, Karlstads universitet, Musikhögskolan Ingesund, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-28917.
Full textThe study focuses on how teachers communicate when teaching music ensembles in which they meet several students simultaneously. With the help of recorded video lessons of four teachers while teaching small ensembles at the high school level, I have analysed the means of communication and how these are applied. The results show that teachers use verbal as well as nonverbal communication in their teaching. They communicate with their students during the lesson via several multimodal semiotic resources. The language of communication that results is a social, humorous, authoritative, creative, knowledgeable and vivid language. In the chapter on discussion, I present among other things, how the teachers in the study chose to design their education based on the situations regarding performance and through performance, and the conditions thus created for themselves and their students.
Aron, Michael. "Acquisition et modélisation de données articulatoires dans un contexte multimodal." Phd thesis, Université Henri Poincaré - Nancy I, 2009. http://tel.archives-ouvertes.fr/tel-00432124.
Full textAron, Michaël. "Acquisition et modélisation de données articulatoires dans un contexte multimodal." Thesis, Nancy 1, 2009. http://www.theses.fr/2009NAN10097/document.
Full textThere is no single technique that will allow all relevant behaviour of the speech articulators (lips, tongue, palate...) to be spatially ant temporally acquired. Thus, this thesis investigates the fusion of multimodal articulatory data. A framework is described in order to acquire and fuse automatically an important database of articulatory data. This includes: 2D Ultrasound (US) data to recover the dynamic of the tongue, stereovision data to recover the 3D dynamic of the lips, electromagnetic sensors that provide 3D position of points on the face and the tongue, and 3D Magnetic Resonance Imaging (MRI) that depict the vocal tract for various sustained articulations. We investigate the problems of the temporal synchronization and the spatial registration between all these modalities, and also the extraction of the shape articulators from the data (tongue tracking in US images). We evaluate the uncertainty of our system by quantifying the spatial and temporal inaccuracies of the components of the system, both individually and in combination. Finally, the fused data are evaluated on an existing articulatory model to assess their quality for an application in speech production
Bato, Mary Grace. "Vers une assimilation des données de déformation en volcanologie." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAU018/document.
Full textTracking magma emplacement at shallow depth as well as its migration towards the Earth's surface is crucial to forecast volcanic eruptions.With the recent advances in Interferometric Synthetic Aperture Radar (InSAR) imaging and the increasing number of continuous Global Navigation Satellite System (GNSS) networks recorded on volcanoes, it is now possible to provide continuous and spatially extensive evolution of surface displacements during inter-eruptive periods. For basaltic volcanoes, these measurements combined with simple dynamical models can be exploited to characterise and to constrain magma pressure building within one or several magma reservoirs, allowing better predictive information on the emplacement of magma at shallow depths. Data assimilation—a sequential time-forward process that best combines models and observations, sometimes a priori information based on error statistics, to predict the state of a dynamical system—has recently gained popularity in various fields of geoscience (e.g. ocean-weather forecasting, geomagnetism and natural resources exploration). In this dissertation, I present the very first application of data assimilation in volcanology from synthetic tests to analyzing real geodetic data.The first part of this work focuses on the development of strategies in order to test the applicability and to assess the potential of data assimilation, in particular, the Ensemble Kalman Filter (EnKF) using a simple two-chamber dynamical model (Reverso2014) and artificial geodetic data. Synthetic tests are performed in order to address the following: 1) track the magma pressure evolution at depth and reconstruct the synthetic ground surface displacements as well as estimate non-evolving uncertain model parameters, 2) properly assimilate GNSS and InSAR data, 3) highlight the strengths and weaknesses of EnKF in comparison with a Bayesian-based inversion technique (e.g. Markov Chain Monte Carlo). Results show that EnKF works well with the synthetic cases and there is a great potential in utilising data assimilation for real-time monitoring of volcanic unrest.The second part is focused on applying the strategy that we developed through synthetic tests in order to forecast the rupture of a magma chamber in real time. We basically explored the 2004-2011 inter-eruptive dataset at Grímsvötn volcano in Iceland. Here, we introduced the concept of “eruption zones” based on the evaluation of the probability of eruption at each time step estimated as the percentage of model ensembles that exceeded their failure overpressure values initially assigned following a given distribution. Our results show that when 25 +/- 1% of the model ensembles exceeded the failure overpressure, an actual eruption is imminent. Furthermore, in this chapter, we also extend the previous synthetic tests by further enhancing the EnKF strategy of assimilating geodetic data in order to adapt to real world problems such as, the limited amount of geodetic data available to monitor ice-covered active volcanoes. Common diagnostic tools in data assimilation are presented.Finally, I demonstrate that in addition to the interest of predicting volcanic eruptions, sequential assimilation of geodetic data on the basis of EnKF shows a unique potential to give insights into volcanic system roots. Using the two-reservoir dynamical model for Grímsvötn 's plumbing system and assuming a fixed geometry and constant magma properties, we retrieve the temporal evolution of the basal magma inflow beneath Grímsvötn that drops up to 85% during the 10 months preceding the initiation of the Bárdarbunga rifting event. The loss of at least 0.016 km3 in the magma supply of Grímsvötn is interpreted as a consequence of magma accumulation beneath Bárdarbunga and subsequent feeding of the Holuhraun eruption 41 km away
Pinson, Franck. "Ajustement de primitives d'objets de forme libre sur un ensemble de données réelles." Compiègne, 1989. http://www.theses.fr/1989COMPD179.
Full textLê, Thanh Vu. "Visualisation interactive 3D pour un ensemble de données géographiques de très grande taille." Pau, 2011. http://www.theses.fr/2011PAUU3005.
Full textReal-time terrain rendering remains an active area of research for a lot of modern computer based applications such as geographic information systems (GIS), interactive 3D games, flights simulators or virtual reality. The technological breakthroughs in data aquisition, coupled with recent advances in display technology have simultaneously led to substantial increases in resolution of both the Digital Elevation Models (DEM) and the various displays used to present this information. In this phD, we have presented a new out-of-core terrain visualization algorithm that achieves per-pixel accurate shading of large textured elevation maps in real-time : our first contribution is the LOD scheme which is based on a small precomputed quadtree of geometric errors, whose nodes are selected for asynchronous loading and rendering depending on a projection in screenspace of those errors. The terrain data and its color texture are manipulated by the CPU in a unified manner as a collection of raster image patches, whose dimensions depends on their screen-space occupancy ; our second contribution is a novel method to remove artifacts that appear on the border between quadtree blocks, we generate a continuous surface without needing additional mesh ; our latest contribution is an effective method adapted to our data structure for the geomorphing, it can be implemented entirely on the GPU. The presented framework exhibits several interesting features over other existing techniques : there is no mesh manipulation or mesh data structures required ; terrain geometric complexity only depends on projected elevation error views from above result in very coarse meshes), lower geometric complexity degrades terrain silhouettes but not details brought in through normal map shading, real-time rendering with support for progressive data loading ; and geometric information and color textures are similarly and efficiently handled as raster data by the CPU. Due to simplified data structures, the system is compact, CPU and GPU efficient and is simple to implement
Books on the topic "Ensemble de données multimodal"
Berztiss, A. T., and Werner Rheinboldt. Data Structures: Theory and Practice. Elsevier Science & Technology Books, 2014.
Find full textBook chapters on the topic "Ensemble de données multimodal"
Khanna, Piyush, Abhinav Mathur, Anunay Chandra, and Akshi Kumar. "Multimodal Cyberbullying Detection Using Ensemble Learning." In Communications in Computer and Information Science, 221–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82322-1_16.
Full textPan, Yini, Hongfeng Li, Lili Liu, Quanzheng Li, Xinlin Hou, and Bin Dong. "aEEG Signal Analysis with Ensemble Learning for Newborn Seizure Detection." In Multiscale Multimodal Medical Imaging, 76–84. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37969-8_10.
Full textSopov, Evgenii. "Self-configuring Ensemble of Multimodal Genetic Algorithms." In Studies in Computational Intelligence, 56–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48506-5_4.
Full textLiang, Jing, Panpan Wei, Boyang Qu, Kunjie Yu, Caitong Yue, Yi Hu, and Shilei Ge. "Ensemble Learning Based on Multimodal Multiobjective Optimization." In Communications in Computer and Information Science, 299–313. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_24.
Full textPhophalia, Ashish, and Pradipta Maji. "Multimodal Brain Tumor Segmentation Using Ensemble of Forest Method." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 159–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75238-9_14.
Full textWang, Jie, Bo Wang, Jing Liang, Kunjie Yu, Caitong Yue, and Xiangyang Ren. "Ensemble Learning via Multimodal Multiobjective Differential Evolution and Feature Selection." In Communications in Computer and Information Science, 439–53. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_34.
Full textVenkatraman, Santhi, and Puja Saha. "Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning." In Big Data Analytics in Smart Manufacturing, 109–20. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003202776-6.
Full textHao, Xiaoke, and Daoqiang Zhang. "Ensemble Universum SVM Learning for Multimodal Classification of Alzheimer’s Disease." In Machine Learning in Medical Imaging, 227–34. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_29.
Full textGhosh, Shankhanil, Chhanda Saha, Nagamani Molakathala, Souvik Ghosh, and Dhananjay Singh. "reSenseNet: Ensemble Early Fusion Deep Learning Architecture for Multimodal Sentiment Analysis." In Intelligent Human Computer Interaction, 689–702. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98404-5_62.
Full textPadha, Anupama, and Anita Sahoo. "Ensemble of Parametrized Quantum LSTM Neural Networks for Multimodal Stress Monitoring." In Algorithms for Intelligent Systems, 59–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7041-2_5.
Full textConference papers on the topic "Ensemble de données multimodal"
Hashmi, Ammarah, Sahibzada Adil Shahzad, Wasim Ahmad, Chia Wen Lin, Yu Tsao, and Hsin-Min Wang. "Multimodal Forgery Detection Using Ensemble Learning." In 2022 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2022. http://dx.doi.org/10.23919/apsipaasc55919.2022.9980255.
Full textGideon, John, Biqiao Zhang, Zakaria Aldeneh, Yelin Kim, Soheil Khorram, Duc Le, and Emily Mower Provost. "Wild wild emotion: a multimodal ensemble approach." In ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2993148.2997626.
Full textEsfar-E-Alam, A. M., Mehran Hossain, Maria Gomes, Rafidul Islam, and Ramisha Raihana. "Multimodal Emotion Recognition Using Heterogeneous Ensemble Techniques." In 2022 25th International Conference on Computer and Information Technology (ICCIT). IEEE, 2022. http://dx.doi.org/10.1109/iccit57492.2022.10054720.
Full textYang, Qing, Cuina Ba, Chenlong Li, and Dongsheng Wu. "An ensemble fault diagnosis approach for multimodal process." In 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). IEEE, 2017. http://dx.doi.org/10.1109/icspcc.2017.8242383.
Full textPeng, Yang, Daisy Zhe Wang, Ishan Patwa, Dihong Gong, and Chunsheng Victor Fang. "Probabilistic Ensemble Fusion for Multimodal Word Sense Disambiguation." In 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015. http://dx.doi.org/10.1109/ism.2015.35.
Full textSpence, Lucy. "Envisioning Black Joy: A Multimodal Ensemble (Poster 27)." In AERA 2022. USA: AERA, 2022. http://dx.doi.org/10.3102/ip.22.1881089.
Full textSpence, Lucy. "Envisioning Black Joy: A Multimodal Ensemble (Poster 27)." In 2022 AERA Annual Meeting. Washington DC: AERA, 2022. http://dx.doi.org/10.3102/1881089.
Full textZhang, Xiaotong, Mingxing Xu, and Thomas Fang Zheng. "Ensemble System for Multimodal Emotion Recognition Challenge (MEC 2017)." In 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia). IEEE, 2018. http://dx.doi.org/10.1109/aciiasia.2018.8470352.
Full textHuddar, Mahesh G., Sanjeev S. Sannakki, and Vijay S. Rajpurohit. "An Ensemble Approach to Utterance Level Multimodal Sentiment Analysis." In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769162.
Full textHu, Ping, Dongqi Cai, Shandong Wang, Anbang Yao, and Yurong Chen. "Learning supervised scoring ensemble for emotion recognition in the wild." In ICMI '17: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3136755.3143009.
Full textReports on the topic "Ensemble de données multimodal"
Dudoit, Alain. Les espaces européens communs de données : une initiative structurante nécessaire et adaptable au Canada. CIRANO, October 2023. http://dx.doi.org/10.54932/ryht5065.
Full textSaulais, Laure, and Maurice Doyon. Impact du design de questions sur la perception des compensations proposées et les intentions de participation au pad: étude de préfaisabilité. CIRANO, August 2022. http://dx.doi.org/10.54932/ziga3839.
Full textDudoit, Alain. L’urgence du premier lien : la chaîne d’approvisionnement du Canada au point de rupture, un enjeu de sécurité nationale. CIRANO, June 2023. http://dx.doi.org/10.54932/zjzp6639.
Full textHunter, Janine, Lorraine van Blerk, Thomas d'Aquin Rubambura, Cold Musiwa Mubigalo, Luc Mufano, Wayne Shand, Anabelle, et al. Vie de rue dans la ville à la frontière: Des jeunes de la rue racontent leurs vies quotidiennes à Bukavu, RDC. StreetInvest, August 2022. http://dx.doi.org/10.20933/100001259.
Full textRipoll, Santiago, Tabitha Hrynick, Ashley Ouvrier, Megan Schmidt-Sane, Federico Marco Federici, and Elizabeth Storer. 10 façons dont les gouvernements locaux en milieu urbain multiculturel peuvent appuyer l’égalité vaccinale en cas de pandémie. SSHAP, January 2023. http://dx.doi.org/10.19088/sshap.2023.001.
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