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Статті в журналах з теми "Détection de tendances émergentes"
Ogden, NH, P. AbdelMalik, and JRC Pulliam. "Maladies infectieuses émergentes : prévision et détection." Relevé des maladies transmissibles au Canada 43, no. 10 (October 5, 2017): 232–38. http://dx.doi.org/10.14745/ccdr.v43i10a03f.
Повний текст джерелаLaisney, Céline. "L'évolution de l'alimentation en France. Tendances émergentes et ruptures possibles." Futuribles, no. 372 (February 25, 2011): 5–23. http://dx.doi.org/10.1051/futur/3725.
Повний текст джерелаRoche, Mathieu. "Système de veille automatique pour la détection de maladies animales émergentes." Bulletin de l'Académie Nationale de Médecine 201, no. 7-9 (September 2017): 1197–201. http://dx.doi.org/10.1016/s0001-4079(19)30396-6.
Повний текст джерелаPoitras, Claire. "L’histoire urbaine environnementale au Québec. Un domaine de recherche en émergence." Globe 9, no. 1 (February 15, 2011): 93–111. http://dx.doi.org/10.7202/1000799ar.
Повний текст джерелаNormand, Anne-Cécile, Carole Cassagne, Lilia Hasseine, Martine Gari-Toussaint, Frédéric Gabriel, Isabelle Accoceberry, Damien Costa, et al. "Identification en ligne des agents fongiques par spectrométrie de masse et détection d’espèces émergentes." Journal de Mycologie Médicale 27, no. 3 (September 2017): e1-e2. http://dx.doi.org/10.1016/j.mycmed.2017.04.015.
Повний текст джерелаBertholom, Chantal. "Franc succès pour la détection, l’investigation et la surveillance des infections émergentes en France." Option/Bio 22, no. 462 (November 2011): 12–13. http://dx.doi.org/10.1016/s0992-5945(11)70903-5.
Повний текст джерелаCottet, V., V. Jooste, A. M. Bouvier, J. Faivre, and C. Bonithon-Kopp. "Tendances temporelles du taux de détection des adénomes colorectaux en population générale." Revue d'Épidémiologie et de Santé Publique 56, no. 2 (May 2008): 73–74. http://dx.doi.org/10.1016/j.respe.2008.03.075.
Повний текст джерелаMurphy, Patrick E. "Recherche en éthique du marketing: thèmes récurrents et émergents." Recherche et Applications en Marketing (French Edition) 32, no. 3 (March 21, 2017): 90–96. http://dx.doi.org/10.1177/0767370117699163.
Повний текст джерелаGeneau, Robert, and Margaret de Groh. "Appel à contributions - Numéro spécial 2020 - Consommation problématique de substances : tendances et questions émergentes en santé publique." Promotion de la santé et prévention des maladies chroniques au Canada 39, no. 6/7 (June 2019): 256. http://dx.doi.org/10.24095/hpcdp.39.6/7.05f.
Повний текст джерелаGeneau, Robert, Tim Stockwell, Cecilia Benoit, Kiffer Card, and Adam Sherk. "Appel à contributions - Numéro spécial 2020 - Consommation problématique de substances : tendances et questions émergentes en santé publique." Promotion de la santé et prévention des maladies chroniques au Canada 39, no. 8/9 (September 2019): 283. http://dx.doi.org/10.24095/hpcdp.39.8/9.06f.
Повний текст джерелаДисертації з теми "Détection de tendances émergentes"
Nguyen, Nhu Khoa. "Emerging Trend Detection in News Articles." Electronic Thesis or Diss., La Rochelle, 2023. http://www.theses.fr/2023LAROS003.
Повний текст джерелаIn the financial domain, information plays an utmost important role in making investment/business decisions as good knowledge can lead to crafting correct approaches in how to invest or if the investment is worth it. Moreover, being able to identify potential emerging themes/topics is an integral part of this field, since it can help get a head start over other investors, thus gaining a huge competitive advantage. To deduce topics that can be emerging in the future, data such as annual financial reports, stock market, and management meeting summaries are usually considered for review by professional financial experts. Reliable sources of information coming from reputable news publishers, can also be utilized for the purpose of detecting emerging themes. Unlike social media, articles from these publishers have high credibility and quality, thus when analyzed in large sums, it is likely to discover dormant/hidden information about trends or what can become future trends. However, due to the vast amount of information generated each day, it has become more demanding and difficult to analyze the data manually for the purpose of trend identification. Our research explores and analyzes data from different quality sources, such as scientific publication abstracts and a provided news article dataset from Bloomberg called Event-Driven Feed (EDF) to experiment on Emerging Trend Detection. Due to the enormous amount of available data spread over extended time periods, it encourages the use of contrastive approaches to measuring the divergence between past and present surrounding context of extracted words and phrases, thus comparing the similarity between unique vector representations of each interval to discover movement in word usage that can lead to the discovery of new trend. Experimental results reveal that the assessment of context change through time of selected terms is able to detect critical emerging trends and points of emergence. It is also discovered that assessing the evolution of context over a long time span is better than just contrasting the two most recent points in time
Altmann, Mathias. "Détection, investigation et contrôle des maladies émergentes. Expériences en santé mondiale." Thesis, Bordeaux, 2022. http://www.theses.fr/2022BORD0217.
Повний текст джерелаContext: the emergence of infectious diseases is the consequence of dynamic imbalances, within complex ecosystems distributed at a given geographical scale including humans, animals, pathogens and the environment. The increasing globalization of trade implies an increase in international flows of travelers and goods which can promote the spread of infectious diseases. From now on, a health crisis in one region or country can have very rapid repercussions on health and the economy in many parts of the world. Detecting emergences and understanding them through field investigations are essential steps to better control future epidemics and pandemics. Experience: during my professional career, my own work has allowed me to address these three dimensions through three studies that have resulted in publications in international peer-reviewed journals. Study 1) During a nationwide outbreak of Escherichia Coli O104:H4 in 2011, I explored the timeliness of the German surveillance system for detection, and recommended a review of the surveillance system by organizing reporting by doctors and heads of laboratories in a centralized and shared database with different access rights by health services at local, regional and national level. Study 2) Following the influenza pandemic in 2009, I investigated and compared the characteristics of severe pediatric cases in Germany during two epidemic seasons. The unchanged severity of influenza A(H1N1)pdm09 during the first post-pandemic season (2010-11) and the consistently high proportion of possibly hospital-acquired infections highlighted the challenge of preventing pediatric cases beyond the pandemic situation. Study 3) During the Ebola virus (EVD) outbreak in 2014, I evaluated the performance of contact tracing in Liberia as a specific control measure. Despite the unprecedented scale of contact tracing for EVD in Liberia, its ability to detect new cases was limited, especially in urban areas and during the epidemic peak. Discussion: the Covid-19 pandemic has revealed weaknesses in surveillance systems in almost all countries. Lessons learned during previous epidemics and pandemics such as those to which I had been exposed professionally and which I report here have been insufficiently considered. In Africa, estimates of incidence and mortality are respectively 100 times and 15 times higher than official reports. Explanations for these very large differences include weak surveillance systems, insufficient use of contact tracing, screening and diagnostic tests, and lack of access to care. Improving surveillance systems for emerging diseases requires: 1) accelerating the digitization and networking of health information systems at all levels, from health centers and peripheral laboratories to the international level; 2) the capture, effective use and linking of other data sources (communitybased, death registries, animal and environmental data) and the regulated use of the internet and social networks; 3) to strengthen the skills and expertise of field epidemiologists and their networking; 4) to invest in research during and between epidemics; and 5) that donors and governments recognize the inevitability of future epidemics of infectious and other disease conditions with serious consequences, our vulnerability to them and the need to invest in global health
Warns-Petit, Eva. "Modélisation de données de surveillance épidémiologique de la faune sauvage en vue de la détection de problèmes sanitaires inhabituels." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00604435.
Повний текст джерелаPetit, Eva. "Modélisation de données de surveillance épidémiologique de la faune sauvage en vue de la détection de problèmes sanitaires inhabituels." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS006/document.
Повний текст джерелаRecent studies have shown that amongst emerging infectious disease events in humans, about 40% were zoonoses linked to wildlife. Disease surveillance of wildlife should help to improve health protection of these animals and also of domestic animals and humans that are exposed to these pathogenic agents. Our aim was to develop tools capable of detecting unusual disease events in free ranging wildlife, by adopting a syndromic approach, as it is used for human health surveillance, with pathological profiles as early unspecific health indicators. We used the information registered by a national network monitoring causes of death in wildlife in France since 1986, called SAGIR. More than 50.000 cases of mortality in wildlife were recorded up to 2007, representing 244 species of terrestrial mammals and birds, and were attributed to 220 different causes of death. The network was first evaluated for its capacity to detect early unusual events. Syndromic classes were then defined by a statistical typology of the lesions observed on the carcasses. Syndrome time series were analyzed, using two complimentary methods of detection, one robust detection algorithm developed by Farrington and another generalized linear model with periodic terms. Historical trends of occurrence of these syndromes and greater-than-expected counts (signals) were identified. Reporting of unusual mortality events in the network bulletin was used to interpret these signals. The study analyses the relevance of the use of syndromic surveillance on this type of data and gives elements for future improvements
Guérin, Philippe Jean. "Utilisation de la surveillance sanitaire des voyageurs comme système sentinelle de détection de maladies émergentes : exemple des maladies entériques." Paris 6, 2006. http://www.theses.fr/2006PA066366.
Повний текст джерелаWilcox, Catherine. "Evaluation de changements hydrologiques en Afrique de l'Ouest : Détection de tendances et cadre de modélisation pour projections futures." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAU016/document.
Повний текст джерелаThe semi-arid regions of West Africa are known for their dry conditions which have predominated since the 1970s. In recent years, however, West Africa has witnessed a series of severe flooding events which caused widespread fatalities and socioeconomic damages. The emergence of this new problem demonstrates the sensitivity of the region to changes in the hydroclimatic system and calls for an improved characterization of flood hazard and the mechanisms that generate it. It also signals the need to develop projections for how flood hazard may evolve in the future in order to inform appropriate adaptation measures.In this context, the following PhD thesis seeks to answer three main questions:1) Is there a significant trend in extreme streamflow in West Africa, or are the documented flooding events isolated incidences?2) How can one model mesoscale convective systems, the primary driver of runoff in the region, in order to explore the properties of precipitation that drive streamflow?3) Based on potential climate change in the region, what trends might be observed in streamflow in the future?First, changes in extreme hydrological events West Africa over the past 60 years are evaluated by applying non-stationary methods based on extreme value theory. Results show a strong increasing trend in extreme hydrological events since the 1970s in the Sahelian Niger River basin and since the 1980s in the Sudano-Guinean catchments in the Senegal River basin. Return levels calculated from non-stationary models are determined to exceed those calculated from a stationary model with over 95% certainty for shorter return periods (<10 years).Next, recent developments are presented for a stochastic precipitation simulator (Stochastorm) designed for modeling mesoscale convective storms, the main rainfall source in the Sahel. Developments include a model for storm occurrence, the explicit representation of extreme rainfall values, and an improvement in the modeling of sub-event intensities. Using high-resolution data from the AMMA-CATCH observatory, simulation outputs were confirmed to realistically represent key characteristics of MCSs, showing the simulator’s potential for use in impact studies.Finally, a modeling chain for producing future hydrological projections is developed and implemented in a Sahelian river basin (Dargol, 7000km2). The chain is original as it is the first attempt in West Africa to encompass the continuum of scales from global climate to convective storms, whose properties have major impacts on hydrological response and as a result local flood risk. The modeling chain components include the convection-permitting regional climate model (RCM) CP4-Africa, the only RCM (to date) explicitly resolving convection and providing long-term simulations in Africa; a bias correction approach; the stochastic precipitation generator Stochastorm; and a rainfall-runoff model specifically developed for Sahelian hydrological processes. The modeling chain is evaluated for a control period (1997-2006) then for future projections (ten years at the end of the 21st century). Hydrological projections show that peak annual flow may become 1.5-2 times greater and streamflow volumes may double or triple on average near the end of the 21st century compared to 1997-2006 in response to projected changes in precipitation.The results raise critical issues notably for hydrological engineering. Current methods used to evaluate flood risk in the region do not take non-stationarity into account, leading to a major risk of underestimating potential floods and undersizing the hydraulic infrastructure designed for protecting against them. It is also suggested to not only consider rainfall changes but also societal and environmental changes, interactions, and feedbacks in order to better attribute past hydrological hazards and their future trajectories to related causes
Bonneterre, Vincent. "Détection et investigation de maladies professionnelles potentiellement émergentes à partir du Réseau National de Vigilance et de Prévention des Pathologies Professionnelles (RNV3P)." Phd thesis, Université de Grenoble, 2010. http://tel.archives-ouvertes.fr/tel-00508967.
Повний текст джерелаKassab, Randa. "Analyse des propriétés stationnaires et des propriétés émergentes dans les flux d'information changeant au cours du temps." Thesis, Nancy 1, 2009. http://www.theses.fr/2009NAN10027/document.
Повний текст джерелаMany applications produce and receive continuous, unlimited, and high-speed data streams. This raises obvious problems of storage, treatment and analysis of data, which are only just beginning to be treated in the domain of data streams. On the one hand, it is a question of treating data streams on the fly without having to memorize all the data. On the other hand, it is also a question of analyzing, in a simultaneous and concurrent manner, the regularities inherent in the data stream as well as the novelties, exceptions, or changes occurring in this stream over time. The main contribution of this thesis concerns the development of a new machine learning approach - called ILoNDF - which is based on novelty detection principle. The learning of this model is, contrary to that of its former self, driven not only by the novelty part in the input data but also by the data itself. Thereby, ILoNDF can continuously extract new knowledge relating to the relative frequencies of the data and their variables. This makes it more robust against noise. Being operated in an on-line mode without repeated training, ILoNDF can further address the primary challenges for managing data streams. Firstly, we focus on the study of ILoNDF's behavior for one-class classification when dealing with high-dimensional noisy data. This study enabled us to highlight the pure learning capacities of ILoNDF with respect to the key classification methods suggested until now. Next, we are particularly involved in the adaptation of ILoNDF to the specific context of information filtering. Our goal is to set up user-oriented filtering strategies rather than system-oriented in following two types of directions. The first direction concerns user modeling relying on the model ILoNDF. This provides a new way of looking at user's need in terms of specificity, exhaustivity and contradictory profile-contributing criteria. These criteria go on to estimate the relative importance the user might attach to precision and recall. The filtering threshold can then be adjusted taking into account this knowledge about user's need. The second direction, complementary to the first one, concerns the refinement of ILoNDF's functionality in order to confer it the capacity of tracking drifting user's need over time. Finally, we consider the generalization of our previous work to the case where streaming data can be divided into multiple classes
Kassab, Randa. "Analyse des propriétés stationnaires et des propriétés émergentes dans les flux d'informations changeant au cours du temps." Phd thesis, Université Henri Poincaré - Nancy I, 2009. http://tel.archives-ouvertes.fr/tel-00402644.
Повний текст джерелаL'apport de ce travail de thèse réside principalement dans le développement d'un modèle d'apprentissage - nommé ILoNDF - fondé sur le principe de la détection de nouveauté. L'apprentissage de ce modèle est, contrairement à sa version de départ, guidé non seulement par la nouveauté qu'apporte une donnée d'entrée mais également par la donnée elle-même. De ce fait, le modèle ILoNDF peut acquérir constamment de nouvelles connaissances relatives aux fréquences d'occurrence des données et de leurs variables, ce qui le rend moins sensible au bruit. De plus, doté d'un fonctionnement en ligne sans répétition d'apprentissage, ce modèle répond aux exigences les plus fortes liées au traitement des flux de données.
Dans un premier temps, notre travail se focalise sur l'étude du comportement du modèle ILoNDF dans le cadre général de la classification à partir d'une seule classe en partant de l'exploitation des données fortement multidimensionnelles et bruitées. Ce type d'étude nous a permis de mettre en évidence les capacités d'apprentissage pures du modèle ILoNDF vis-à-vis de l'ensemble des méthodes proposées jusqu'à présent. Dans un deuxième temps, nous nous intéressons plus particulièrement à l'adaptation fine du modèle au cadre précis du filtrage d'informations. Notre objectif est de mettre en place une stratégie de filtrage orientée-utilisateur plutôt qu'orientée-système, et ceci notamment en suivant deux types de directions. La première direction concerne la modélisation utilisateur à l'aide du modèle ILoNDF. Cette modélisation fournit une nouvelle manière de regarder le profil utilisateur en termes de critères de spécificité, d'exhaustivité et de contradiction. Ceci permet, entre autres, d'optimiser le seuil de filtrage en tenant compte de l'importance que pourrait donner l'utilisateur à la précision et au rappel. La seconde direction, complémentaire de la première, concerne le raffinement des fonctionnalités du modèle ILoNDF en le dotant d'une capacité à s'adapter à la dérive du besoin de l'utilisateur au cours du temps. Enfin, nous nous attachons à la généralisation de notre travail antérieur au cas où les données arrivant en flux peuvent être réparties en classes multiples.
Louzada, Pinto Julio Cesar. "Information diffusion and opinion dynamics in social networks." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0001/document.
Повний текст джерелаOur aim in this Ph. D. thesis is to study the diffusion of information as well as the opinion dynamics of users in social networks. Information diffusion models explore the paths taken by information being transmitted through a social network in order to understand and analyze the relationships between users in such network, leading to a better comprehension of human relations and dynamics. This thesis is based on both sides of information diffusion: first by developing mathematical theories and models to study the relationships between people and information, and in a second time by creating tools to better exploit the hidden patterns in these relationships. The theoretical tools developed in this thesis are opinion dynamics models and information diffusion models, where we study the information flow from users in social networks, and the practical tools developed in this thesis are a novel community detection algorithm and a novel trend detection algorithm. We start by introducing an opinion dynamics model in which agents interact with each other about several distinct opinions/contents. In our framework, agents do not exchange all their opinions with each other, they communicate about randomly chosen opinions at each time. We show, using stochastic approximation algorithms, that under mild assumptions this opinion dynamics algorithm converges as time increases, whose behavior is ruled by how users choose the opinions to broadcast at each time. We develop next a community detection algorithm which is a direct application of this opinion dynamics model: when agents broadcast the content they appreciate the most. Communities are thus formed, where they are defined as groups of users that appreciate mostly the same content. This algorithm, which is distributed by nature, has the remarkable property that the discovered communities can be studied from a solid mathematical standpoint. In addition to the theoretical advantage over heuristic community detection methods, the presented algorithm is able to accommodate weighted networks, parametric and nonparametric versions, with the discovery of overlapping communities a byproduct with no mathematical overhead. In a second part, we define a general framework to model information diffusion in social networks. The proposed framework takes into consideration not only the hidden interactions between users, but as well the interactions between contents and multiple social networks. It also accommodates dynamic networks and various temporal effects of the diffusion. This framework can be combined with topic modeling, for which several estimation techniques are derived, which are based on nonnegative tensor factorization techniques. Together with a dimensionality reduction argument, this techniques discover, in addition, the latent community structure of the users in the social networks. At last, we use one instance of the previous framework to develop a trend detection algorithm designed to find trendy topics in a social network. We take into consideration the interaction between users and topics, we formally define trendiness and derive trend indices for each topic being disseminated in the social network. These indices take into consideration the distance between the real broadcast intensity and the maximum expected broadcast intensity and the social network topology. The proposed trend detection algorithm uses stochastic control techniques in order calculate the trend indices, is fast and aggregates all the information of the broadcasts into a simple one-dimensional process, thus reducing its complexity and the quantity of necessary data to the detection. To the best of our knowledge, this is the first trend detection algorithm that is based solely on the individual performances of topics
Книги з теми "Détection de tendances émergentes"
Food and Agriculture Organization of the United Nations. Montagnes et le Droit: Tendances émergentes. Food & Agriculture Organization of the United Nations, 2010.
Знайти повний текст джерелаЧастини книг з теми "Détection de tendances émergentes"
DIOP, El Hadji Ibrahima. "Faire de l’identitaire pour ne pas parler d’identité(s)." In Voix africaines, voies émergentes, 39–50. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5571.
Повний текст джерелаSICARD, S., M. TANTI, C. FICKO, S. WATIER, R. MICHEL, and G. BÉDUBOURG. "Risques infectieux émergents ou ré-émergents pour les militaires en opération." In Médecine et Armées Vol. 46 No.1, 37–44. Editions des archives contemporaines, 2018. http://dx.doi.org/10.17184/eac.7367.
Повний текст джерелаЗвіти організацій з теми "Détection de tendances émergentes"
Recensement des Priorités de Recherche sur L’extrémisme Violent en Afrique du Nord et au Sahel 2018. RESOLVE Network, January 2021. http://dx.doi.org/10.37805/rp2021.2.lcb.
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