Letteratura scientifica selezionata sul tema "Apprentissage pour la planification"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Consulta la lista di attuali articoli, libri, tesi, atti di convegni e altre fonti scientifiche attinenti al tema "Apprentissage pour la planification".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Articoli di riviste sul tema "Apprentissage pour la planification"
Proulx, Caroline. "L’élaboration d’un outil d’autoformation pour soutenir la planification d’activités lexicales au primaire". SHS Web of Conferences 191 (2024): 07010. http://dx.doi.org/10.1051/shsconf/202419107010.
Testo completoG-Héon, Audrey, Aurélie Thuot-Jolicoeur, Christophe Gagné e Catherine Turcotte. "Alliance université-communauté : l’impact du tutorat en littératie auprès de jeunes du primaire sur le développement professionnel d’étudiantes en formation initiale en enseignement". Revue hybride de l'éducation 9, n. 1 (13 gennaio 2025): 1–18. https://doi.org/10.1522/rhe.v9i1.1749.
Testo completoBouffard, Léandre, Étienne Bastin, Sylvie Lapierre e Micheline Dubé. "La gestion des buts personnels, un apprentissage significatif pour des étudiants universitaires". Articles 27, n. 3 (7 febbraio 2005): 503–22. http://dx.doi.org/10.7202/009962ar.
Testo completoBrisson, Geneviève, Magali Forte, Gwénaëlle André e Diane Dagenais. "Perspective sociomatérielle sur la pédagogie des multilittératies". OLBI Journal 11 (15 marzo 2022): 201–27. http://dx.doi.org/10.18192/olbij.v11i1.6181.
Testo completoForget-Dubois, Nadine. "L'art de ne pas réinventer la roue : Mener à bien un projet de rédaction scientifique". Psycause : revue scientifique étudiante de l'École de psychologie de l'Université Laval 10, n. 1 (30 luglio 2020): 61–71. http://dx.doi.org/10.51656/psycause.v10i1.30460.
Testo completoHabersaat, Katrine, Noni E. MacDonald e Ève Dubé. "Concevoir des interventions adaptées pour éliminer les obstacles à la vaccination". Relevé des maladies transmissibles au Canada 47, n. 3 (31 marzo 2021): 181–85. http://dx.doi.org/10.14745/ccdr.v47i03a07f.
Testo completoParhusip, Rosalina, e Nurilam Harianja. "Développement De Module D'auto Apprentissage Sur Ppt Pour Les Cours Français Écrit Introductif Basé Sur Powerpoint". Didacticofrancia Journal Didactique du FLE 12, n. 2 (31 marzo 2024): 99–116. http://dx.doi.org/10.15294/didacticofrancia.v12i2.71636.
Testo completoCartier, Sylvie. "Etude de l'apprentissage par la lecture d'étudiants en contexte d'apprentissage par problèmes (APP)". Canadian Journal of Higher Education 32, n. 1 (30 aprile 2002): 1–29. http://dx.doi.org/10.47678/cjhe.v32i1.183401.
Testo completoMinguzzi, Antonio, e Renato Passaro. "Apprentissage et culture d'entreprise dans les PME : une analyse explorative intersectorielle". Revue internationale P.M.E. 10, n. 2 (16 febbraio 2012): 45–79. http://dx.doi.org/10.7202/1009023ar.
Testo completoTremblay, Ophélie, Kathleen Sénéchal, Élaine Turgeon e Mylène Lamoureux-Duquette. "Le cercle de planification : des échanges oraux qui soutiennent la production d’idées à l’écrit". Le français aujourd'hui N° 222, n. 3 (25 agosto 2023): 41–52. http://dx.doi.org/10.3917/lfa.222.0041.
Testo completoTesi sul tema "Apprentissage pour la planification"
Hren, Jean-François. "Planification optimiste pour systèmes déterministes". Thesis, Lille 1, 2012. http://www.theses.fr/2012LIL10188/document.
Testo completoIn the field of reinforcement learning, planning in the case of deterministic systems consists of doing a forward search using a generative model of the system so as to find the action to apply in its current state. In our case, the forward search leads us to build a look-ahead tree, its root being the current state of the system. If the computational resources are limited and unknown, we have to use an algorithm which tries to minimize its regret. In other words, an algorithm returning an action to apply which is as close as possible to the optimal one in term of quality and with respect to the computational resources used. We present the optimistic planing algorithm in the case of a discrete action space. We prove a lower and upper bound in the worst case and in a particular class of problems. Also we present two algorithms using the optimistic approach but in the case of a continuous action space
Padonou, Esperan. "Apprentissage Statistique en Domaine Circulaire Pour la Planification de Contrôles en Microélectronique". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEM009/document.
Testo completoDriven by industrial needs in microelectronics, this thesis is focused on probabilistic models for spatial data and Statistical Process Control. The spatial problem has the specificity of being defined on circular domains. It is addressed through a Kriging model where the deterministic part is made of orthogonal polynomials and the stochastic term represented by a Gaussian process. Defined with the Euclidean distance and the uniform measure over the disk, traditional Kriging models do not exploit knowledge on manufacturing processes. To take rotations or diffusions from the center into account, we introduce polar Gaussian processes over the disk. They embed radial and angular correlations in Kriging predictions, leading to significant improvements in the considered situations. Polar Gaussian processes are then interpreted via Sobol decomposition and generalized in higher dimensions. Different designs of experiments are developed for the proposed models. Among them, Latin cylinders reproduce in the space of polar coordinates the properties of Latin hypercubes. To model spatial and temporal data, Statistical Process Control is addressed by monitoring Kriging parameters, based on standard control charts. Furthermore, the monitored time – series contain outliers and structural changes, which cause bias in prediction and false alarms in risk management. These issues are simultaneously tackled with a robust and adaptive smoothing
Hérail, Philippe. "Apprentissage de Modèles Hiérarchiques par Démonstration pour la Planification et l'Action Délibérée". Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEI006.
Testo completoThe development of autonomous agents, especially embodied agents such as robots, requires complex architectures operating at different levels of abstractions. Given the complexity of real environments, hand-crafting all the models used at the different levels quickly becomes impractical. In recent years, there has been a growing body of work focusing on learning such models at the sensorimotor level, i.e. for perception and basic motor capabilities. However, thesame cannot be said for high-level models enabling deliberative functions.Among such high-level models, we will focus our attention on Hierarchical Task Networks (HTNs), which are a common planning formalism used in many practical applications, from video-games to robotic agents. Presently, designing HTN models remains a mostly manual task, which requires expertise both of the application domain and of the systems used for hierarchical planning. While some approaches do exist for learning HTNs, they suffer from some limitations, mainly in the structure of the domains that can be learned or in the required data annotation.In this thesis, we will propose a technique for learning HTNs with multiple hierarchy levels with minimal annotation work required. To this end, we will propose two main contributions: a procedure for learning HTN structures from demonstrations and one for learning their parameters from these demonstrations.The structure learning approach will leverage frequent pattern mining to detect interesting behavioural patterns to abstract in the demonstrations, which we couple with an existing goal regression algorithm. The quality of a given HTN structure during the search will be evaluated through a novel metric based on the Minimum Description Length (MDL) principle to use as an efficient proxy for planning performance.In addition, we propose a new method for identifying a sensible set of parameters for HTNs, relying on a MAX-SMT approach, which can be applied to most HTN models. Coupling our contributions for learning an HTN model structure and the identification of its parameters allows us to produces complete HTN models which we evaluate on standard benchmarks of the HTN planning community
Infantes, Guillaume. "Apprentissage de modèles de comportement pour le contrôle d'exécution et la planification robotique". Phd thesis, Université Paul Sabatier - Toulouse III, 2006. http://tel.archives-ouvertes.fr/tel-00129505.
Testo completoTremblet, David. "Apprentissage de contraintes pour améliorer la précision des modèles de planification et ordonnancement". Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0417.
Testo completoManufacturing decisions often rely on mathematical models to suggest decisions to the managers in charge of production. For example, lot-sizing models are commonly used to plan factory production. The model calculates capacity usage for a plan with a rough approximation that does not account for all the complexities encountered on the shop floor. Although this approximation allows the model to be solved efficiently, the resulting decision usually leads to errors when the plan is executed on the shop floor. This thesis aims to use machine learning to improve the models traditionally used in operations research for manufacturing applications. The methodology aims to replace parts of the optimization models (constraints, objectives) with machine learning models (linear regression, neural networks, etc.) previously trained on available data. As a result, these tools can take advantage of the massive amount of data generated on the shop floor and external data sources to make better decisions. This approach is evaluated on a lot-sizing model where we learn capacity utilization constraints from the production schedule using machine learning models. The resulting model determines optimal production plans where production quantities remain feasible once sent to the shop floor. The resulting tool is also well adapted to today's production systems, which are increasingly reconfigurable and constantly evolving. The model can be retrained from shop floor data as changes occur on the shop floor, eliminating the need for an optimization expert to modify the optimization model each time the shop floor evolves
Castellanos-Paez, Sandra. "Apprentissage de routines pour la prise de décision séquentielle". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM043.
Testo completoIntuitively, a system capable of exploiting its past experiences should be able to achieve better performance. One way to build on past experiences is to learn macros (i.e. routines). They can then be used to improve the performance of the solving process of new problems. In automated planning, the challenge remains on developing powerful planning techniques capable of effectively explore the search space that grows exponentially. Learning macros from previously acquired knowledge has proven to be beneficial for improving a planner's performance. This thesis contributes mainly to the field of automated planning, and it is more specifically related to learning macros for classical planning. We focused on developing a domain-independent learning framework that identifies sequences of actions (even non-adjacent) from past solution plans and selects the most useful routines (i.e. macros), based on a priori evaluation, to enhance the planning domain.First, we studied the possibility of using sequential pattern mining for extracting frequent sequences of actions from past solution plans, and the link between the frequency of a macro and its utility. We found out that the frequency alone may not provide a consistent selection of useful macro-actions (i.e. sequences of actions with constant objects).Second, we discussed the problem of learning macro-operators (i.e. sequences of actions with variable objects) by using classic pattern mining algorithms in planning. Despite the efforts, we find ourselves in a dead-end with the selection process because the pattern mining filtering structures are not adapted to planning.Finally, we provided a novel approach called METEOR, which ensures to find the frequent sequences of operators from a set of plans without a loss of information about their characteristics. This framework was conceived for mining macro-operators from past solution plans, and for selecting the optimal set of macro-operators that maximises the node gain. It has proven to successfully mine macro-operators of different lengths for four different benchmarks domains and thanks to the selection phase, be able to deliver a positive impact on the search time without drastically decreasing the quality of the plans
Qiu, Danny. "Nouvelles méthodes d'apprentissage automatique pour la planification des réseaux mobiles". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS010.
Testo completoMobile connectivity is an important driver of our societies, which is why mobile data consumption has continued to grow steadily worldwide. To avoid global congestion, mobile network operators are bound to evolve their networks.Mobile networks are strengthened through the deployment of new base stations and antennas. As this task is very expensive, a great attention is given to identifying cost-effective and competitive deployments.In this context, the objective of this thesis is to use machine learning to improve deployment decisions.The first part of the thesis is dedicated to developing machine learning models to assist in the deployment of base stations in new locations. Assuming that network knowledge for an uncovered area is unavailable, the models are trained solely on urban fabric features.At first, models were simply trained to estimate the class of major activity of a base station.Subsequently, this work was extended to predict the typical hourly profile of weekly traffic. Since the train time could be long, several methods for reducting mobile data have been studied.The second part of the thesis focuses on the deployment of new cells to increase the capacity of existing sites. For this purpose, a cell coverage model was developed by deriving the Voronoi diagram representing the coverage of base stations.The first study examined the spectrum refarming of former generations of mobile technology for the deployment of the newest generations.Models are trained to assist in prioritizing capacity additions on sectors that can benefit from the greatest improvement in resource availability.The second study examined the deployment of a new generation of mobile technology, considering two deployment strategies: driven by profitability or by the improvement of the quality of service.Therefore, the methods developed in this thesis offer ways to train models to predict the connectivity demand of a territory as well as its evolution. These models could be integrated into a geo-marketing tool, as well as providing useful information for network dimensioning
Grand, Maxence. "Apprentissage de Modèle d'Actions basé sur l'Induction Grammaticale Régulière pour la Planification en Intelligence Artificielle". Electronic Thesis or Diss., Université Grenoble Alpes, 2022. http://www.theses.fr/2022GRALM044.
Testo completoThe field of artificial intelligence aims to design and build autonomous agents able to perceive, learn and act without any human intervention to perform complex tasks. To perform complex tasks, the autonomous agent must plan the best possible actions and execute them. To do this, the autonomous agent needs an action model. An action model is a semantic representation of the actions it can execute. In an action model, an action is represented using (1) a precondition: the set of conditions that must be satisfied for the action to be executed and (2) the effects: the set of properties of the world that will be altered by the execution of the action. STRIPS planning is a classical method to design these action models. However, STRIPS action models are generally too restrictive to be used in real-world applications. There are other forms of action models: temporal action models allowing to represent actions that can be executed concurrently, HTN action models allowing to represent actions as tasks and subtasks, etc. These models are less restrictive, but the less restrictive the models are the more difficult they are to design. In this thesis, we are interested in approaches facilitating the acquisition of these action models based on machine learning techniques.In this thesis, we present AMLSI (Action Model Learning with State machine Interaction), an approach for action model learning based on Regular Grammatical Induction. First, we show that the AMLSI approach allows to learn (STRIPS) action models. We will show the different properties of the approach proving its efficiency: robustness, convergence, require few learning data, quality of the learned models. In a second step, we propose two extensions for temporal action model learning and HTN action model learning
Arora, Ankuj. "Apprentissage du modèle d'action pour une interaction socio-communicative des hommes-robots". Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM081/document.
Testo completoDriven with the objective of rendering robots as socio-communicative, there has been a heightened interest towards researching techniques to endow robots with social skills and ``commonsense'' to render them acceptable. This social intelligence or ``commonsense'' of the robot is what eventually determines its social acceptability in the long run.Commonsense, however, is not that common. Robots can, thus, only learn to be acceptable with experience. However, teaching a humanoid the subtleties of a social interaction is not evident. Even a standard dialogue exchange integrates the widest possible panel of signs which intervene in the communication and are difficult to codify (synchronization between the expression of the body, the face, the tone of the voice, etc.). In such a scenario, learning the behavioral model of the robot is a promising approach. This learning can be performed with the help of AI techniques. This study tries to solve the problem of learning robot behavioral models in the Automated Planning and Scheduling (APS) paradigm of AI. In the domain of Automated Planning and Scheduling (APS), intelligent agents by virtue require an action model (blueprints of actions whose interleaved executions effectuates transitions of the system state) in order to plan and solve real world problems. During the course of this thesis, we introduce two new learning systems which facilitate the learning of action models, and extend the scope of these new systems to learn robot behavioral models. These techniques can be classified into the categories of non-optimal and optimal. Non-optimal techniques are more classical in the domain, have been worked upon for years, and are symbolic in nature. However, they have their share of quirks, resulting in a less-than-desired learning rate. The optimal techniques are pivoted on the recent advances in deep learning, in particular the Long Short Term Memory (LSTM) family of recurrent neural networks. These techniques are more cutting edge by virtue, and produce higher learning rates as well. This study brings into the limelight these two aforementioned techniques which are tested on AI benchmarks to evaluate their prowess. They are then applied to HRI traces to estimate the quality of the learnt robot behavioral model. This is in the interest of a long term objective to introduce behavioral autonomy in robots, such that they can communicate autonomously with humans without the need of ``wizard'' intervention
Cuperlier, Nicolas. "Apprentissage et prédiction de séquences sensori-motrices : architecture neuromimétique pour la navigation et la planification d'un robot mobile". Cergy-Pontoise, 2006. http://www.theses.fr/2006CERG0316.
Testo completoNavigation of an autonomous mobile robot in an unknown environment is a complex task that raises numerous issues in perception, categorisation, planning, and motor control. Solving all these problems in an integrated manner remains a challenge for roboticians. Thus, we propose a unified neuronal framework, based on the modeling of different parts of the mammalian brain’s functionalities: the hippocampus, the prefrontal cortex and the basal ganglia. Key topics are the multi-modal data integration like vision (the prevailing input), path integration, motivation, and also the inner and outer interactions between the structures. A first part of our work consists in modeling neural networks able to learn and predict sensory-motor combinations (transition cells) which are inputs of a cognitive map used to plan according to conflicting motivations. The cognitive map is learned without using any Cartesian coordinates nor occupancy grids. Already known transitions are used in exploration in order to preferentially explore unknown zones to reduce exploration time and enhance the completion of the cognitive map. Links of this map are learned or reinforced according to the behavior and enable to take into account dynamical changes of the environment. Exploration periods may be alternated with planning periods. The second part of this thesis brings an interesting solution for computing and selecting the final movement to perform. It also gives a stable motor control. Instead of using a (( Winner Takes All )) mechanism to select the movement, we increase the planned movement accuracy via a soft competition. Hence several movements are proposed and fed in another layer where the final motor command is obtained as the stable solution of a dynamical system: a one dimensional neural field coding for the heading direction. This field allows to endow the system with a final movement selection leading to a better movement generalization and consequently to a more reliable movement while planning. Our model gives a control architecture allowing to exhibit on a mobile robot navigation behaviors inspired from biology. This architecture can be considered as an attempt to explain underlying mechanisms implemented by mammals for these kind of behaviors. Furthermore, we can list the following benefits of our model: on-line localization, active exploration, planning and mapping in an uncompletely explored environment. These benifits cast an original light on the S. L. A. M problem (Simultaneous Localization and Map building of an unknown environment)
Libri sul tema "Apprentissage pour la planification"
Ontario. Esquisse de cours 12e année: Planification d'une entreprise bdv4c cours précollégial. Vanier, Ont: CFORP, 2002.
Cerca il testo completoMaurice, Harvey. Pour une société en apprentissage. Sainte-Foy, Québec: Institut québécois de recherche sur la culture, 1997.
Cerca il testo completoSalins, Geneviève-Dominique de. Grammaire pour l'enseignement/apprentissage du FLE. Paris: Didier, 1998.
Cerca il testo completoFiator, Joan A. Planification pour la rentabilité de l'éducation. Yaoundé, Cameroun: USAID, 1991.
Cerca il testo completoClavet, Jean-Claude. L' apprentissage philosophique: Notes pour une introduction. Sainte-Foy, Qué: Éditions Le Griffon d'argile, 1996.
Cerca il testo completoQuarré, François. La stratégie pour gagner. Paris: Masson, 1987.
Cerca il testo completoCentre international de formation en politique énergétique., ENDA (Organisation) e Commission des Communautés européennes, a cura di. Leçons pour une planification énergétique en Afrique. Paris: Technip, 1993.
Cerca il testo completoCenter for Health Promotion and Education (U.S.). Division of Reproductive Health e United States. Agency for International Development, a cura di. Planification familiale: Methodes et pratiques pour l'Afrique. Atlanta, Ga: Les Centres pour le Contrôle des Maladies Transmissibles (CDC), Le Centre de la Promotion et de l'Education pour la Santé, La Division de la Santé Reproductive, 1985.
Cerca il testo completoSousa, David A. Un cerveau pour apprendre. Montréal, Qué: Éditions de la Chenelière, 2002.
Cerca il testo completoBertin, Jean-Claude. Des outils pour des langues: Multimédia et apprentissage. Paris: Ellipses, 2001.
Cerca il testo completoCapitoli di libri sul tema "Apprentissage pour la planification"
de Boissieu, Christian. "Quel avenir pour la planification ?" In Jean Monnet et Charles de Gaulle, 163–70. Rennes: Presses universitaires de Rennes, 2024. http://dx.doi.org/10.4000/12ovr.
Testo completoSchober, Madrean. "La planification stratégique pour la pratique avancée infirmière". In Advanced Practice in Nursing, 9–38. Cham: Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-39717-2_2.
Testo completoVINET, Freddy, Alain CHEVALLIER, Hoilid LAMSSALAK e Dimitri LAPIERRE. "Les apprentissages de la gestion de crise". In Gestion des crises territoriales, 63–86. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9080.ch3.
Testo completoBrien, Robert. "Apprentissage collaboratif :". In Collaborer pour apprendre et faire apprendre, 55–74. Presses de l'Université du Québec, 2003. http://dx.doi.org/10.2307/j.ctv18pgvgg.7.
Testo completoBa, Djibrirou Daouda. "Analyse didactique et docimologique du commentaire historique dans le cadre du baccalauréat-UEMOA". In Aux carrefours de la langue, de la littérature, de la didactique et de la société : la recherche francophone en action, 93–107. Observatoire européen du plurilinguisme, 2021. http://dx.doi.org/10.3917/oep.agbef.2021.01.0093.
Testo completo"MÉTACOGNITION ET APPRENTISSAGE". In Pour guider la métacognition, 7–22. Presses de l'Université du Québec, 2000. http://dx.doi.org/10.2307/j.ctv18ph4w0.6.
Testo completoDurocher, Éric. "Apprentissage collaboratif". In Repères contemporains pour l’éducation aux sciences et à la technologie, 7–14. Presses de l'Université Laval, 2020. http://dx.doi.org/10.2307/j.ctv1h0p2jg.4.
Testo completoDurocher, Éric. "Apprentissage collaboratif". In Repères contemporains pour l’éducation aux sciences et à la technologie, 7–13. Les Presses de l’Université de Laval, 2020. http://dx.doi.org/10.1515/9782763749570-002.
Testo completoDRIF, Ahlem, Saad Eddine SELMANI e Hocine CHERIFI. "Réseau interactif et apprentissage automatique pour les recommandations". In Optimisation et apprentissage, 123–51. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch5.
Testo completoYAHYAOUI, Khadidja. "Approche hybride pour la navigation autonome des robots mobiles". In Optimisation et apprentissage, 173–209. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch7.
Testo completoAtti di convegni sul tema "Apprentissage pour la planification"
Delisle, Sylvain, Sylvain Létourneau e Stan Matwin. "Expérimentation en apprentissage d'heuristiques pour l'analyse syntaxique". In the 17th international conference. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/980451.980896.
Testo completoDelisle, Sylvain, Sylvain Létourneau e Stan Matwin. "Expérimentation en apprentissage d'heuristiques pour l'analyse syntaxique". In the 36th annual meeting. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/980845.980896.
Testo completoFourcade, A. "Apprentissage profond : un troisième oeil pour les praticiens". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601014.
Testo completoCHTIOUI, Researcher Jamila. "HOW CAN STUDY GROUPS BE MADE EFFECTIVE AT UNIVERSITY?" In IV. International research Scientific Congress of Humanities and Social Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/ist.con4-2.
Testo completoElmoutawakkil, N., S. Bouzoubaa, S. Bellemkhannate e I. Benyahya. "Flux de travail du guidage tridimensionnel en chirurgie orale". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206602005.
Testo completoPajot, T., S. Ketoff e L. Bénichou. "Chirurgie implantaire guidée : acquisition, planification, modélisation et production d'un guide chirurgical. Mise en place d'une chaine numérique pour la création interne et l'utilisation de guides chirurgicaux". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206602006.
Testo completoCastanier, Bruno, Wenjin Zhu e Belgacem Bettayeb. "Un outil d'aide à la décision pour la planification des opérations de maintenance d'une éolienne offshore". In Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, 11-13 Octobre 2016, Saint Malo, France. IMdR, 2016. http://dx.doi.org/10.4267/2042/61731.
Testo completoGhedhahem, Zeineb. "Cap sur le premier MOOC FOFLE en Afrique francophone pour se (re)mettre à flot". In XXV Coloquio AFUE. Palabras e imaginarios del agua. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/xxvcoloquioafue.2016.3049.
Testo completoMarnet, Béatrice. "Les expressions idiomatiques et l’approche actionnelle – L'apprentissage du français langue étrangère à travers les unités phraséologiques qui ont pour thème l'eau". In XXV Coloquio AFUE. Palabras e imaginarios del agua. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/xxvcoloquioafue.2016.3799.
Testo completoAlande, C., e C. Landric. "Autotransplantation de germes dentaires au centre hospitalier de Pau : une série de cas". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206603008.
Testo completoRapporti di organizzazioni sul tema "Apprentissage pour la planification"
Brinkerhoff, Derick W., Sarah Frazer e Lisa McGregor. S'adapter pour apprendre et apprendre pour s'adapter : conseils pratiques tirés de projets de développement internationaux. RTI Press, gennaio 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.fr.
Testo completoChambers, Robert, Naomi Vernon e Jamie Myers. L’apprentissage rapide par l’action pour la programmation de l’assainissement et l’hygiène. The Sanitation Learning Hub, Institute of Development Studies, settembre 2020. http://dx.doi.org/10.19088/slh.2020.010.
Testo completoAndreas, Balthasar, e Schalcher Hans-Rudolf. Recherche pour l’avenir énergétique de la Suisse. Swiss National Science Foundation (SNSF), gennaio 2020. http://dx.doi.org/10.46446/publication_pnr70_pnr71.2020.1.fr.
Testo completoMotulsky, Aude, Jean Noel Nikiema, Philippe Després, Alexandre Castonguay, Martin Cousineau, Joé T. Martineau, Cécile Petitgand e Catherine Régis. Promesses de l’IA en santé - Fiche 2. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, marzo 2022. http://dx.doi.org/10.61737/votf6751.
Testo completoMelloni, Gian. Le leadership des autorités locales en matière d'assainissement et d'hygiène : expériences et apprentissage de l'Afrique de l'Ouest. Institute of Development Studies (IDS), gennaio 2022. http://dx.doi.org/10.19088/slh.2022.002.
Testo completoMiller, Robert, Andrew Fisher, Kate Miller, Lewis Ndhlovu, Baker Maggwa, Ian Askew, Diouratie Sanogo e Placide Tapsoba. L'Approche de l'Analyse Situationnelle pour l'évaluation des services de planification familiale et de santé de la reproduction: Manuel de recherche. Population Council, 1999. http://dx.doi.org/10.31899/rh11.1060.
Testo completoStrumpf, Erin C., e Tiffanie Perrault. Et si l’accès à des données fiables sur le cancer du sein pouvait sauver des vies ? CIRANO, ottobre 2024. http://dx.doi.org/10.54932/ccjc4217.
Testo completoVilain, Vincent. Protéger la vie privée via un réseau adversarial d’attaque de réidentification. Observatoire international sur les impacts sociétaux de l'intelligence artificielle et du numérique, settembre 2024. http://dx.doi.org/10.61737/tabe1427.
Testo completoResearch Institute (IFPRI), International Food Policy. Renforcer les capacités en Afrique pour une planification et une mise en oeuvre de politiques fondées sur des données empiriques Soutien de l’IFPRI au PDDAA en 2018-2019. Washington, DC: International Food Policy Research Institute, 2019. http://dx.doi.org/10.2499/p15738coll2.133452.
Testo completoAudet, René, e Tom Lebrun. Livre blanc : L'intelligence artificielle et le monde du livre. Observatoire international sur les impacts sociétaux de l’intelligence artificielle et du numérique, settembre 2020. http://dx.doi.org/10.61737/zhxd1856.
Testo completo