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Augmented reality for scene text recognition, visualization and reading to assist visually impaired people / Imene Ouali in Procedia Computer Science, vol 207 (2022)
[article]
Titre : Augmented reality for scene text recognition, visualization and reading to assist visually impaired people Type de document : Article/Communication Auteurs : Imene Ouali, Auteur ; Mohamed Ben Halima, Auteur ; Ali Wali, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] enquête
[Termes IGN] personne malvoyante
[Termes IGN] réalité augmentée
[Termes IGN] reconnaissance de caractères
[Termes IGN] signalisation routière
[Termes IGN] visualisationRésumé : (auteur) Reading traffic signs while driving a car for visually impaired people and people with visual problems is a very difficult task for them. This task is encountered every day, sometimes incorrect reading of traffic signs can lead to very serious results. In particular, the Arabic language is very difficult, making recognizing and viewing Arabic text a difficult task. In this context, we are looking for an effective solution to remove errors and results that can sometimes end someone's life. This article aims to correctly read traffic signs with Arabic text using augmented reality technology. Our system is composed of three modules. The first is text detection and recognition. The second is Text visualization. The third is Text to speech methods conversion. With this system, the user can have two different results. The first result is visual with much-improved text and enhancement. The second result is sound, he can hear the text aloud. This system is very applicable and effective for daily life. To assess the effectiveness of our work, we offer a survey to a group of visually impaired people to give their opinion on the use of our application. The results have been good for most people. Numéro de notice : A2023-010 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article DOI : 10.1016/j.procs.2022.09.048 Date de publication en ligne : 19/10/2022 En ligne : https://doi.org/10.1016/j.procs.2022.09.048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102119
in Procedia Computer Science > vol 207 (2022) . - pp 158 - 167[article]Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)
[article]
Titre : Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques Type de document : Article/Communication Auteurs : Wang Yue, Auteur ; Changgang Yu, Auteur ; A. Antonidoss, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 593 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] bâtiment
[Termes IGN] capteur (télédétection)
[Termes IGN] économie d'énergie
[Termes IGN] internet des objets
[Termes IGN] performance énergétique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'aide à la décision
[Termes IGN] ville durable
[Termes IGN] ville intelligenteRésumé : (auteur) In modern society, energy conservation is an important consideration for sustainability. The availability of energy-efficient infrastructures and utilities depend on the sustainability of smart cities. The big streaming data generated and collected by smart building devices and systems contain useful information that needs to be used to make timely action and better decisions. The ultimate objective of these procedures is to enhance the city's sustainability and livability. The replacement of decades-old infrastructures, such as underground wiring, steam pipes, transportation tunnels, and high-speed Internet installation, is already a major problem for major urban regions. There are still certain regions in big cities where broadband wireless service is not available. The decision support system is recently acquiring increasing attention in the smart city context. In this article, a deep learning–based sustainable decision support system (DLSDSS) has been proposed for energy building in smart cities. This study proposes the integration of the Internet of Things into smart buildings for energy management, utilizing deep learning methods for sensor information decision making. Building a socially advanced environment aims to enhance city services and urban administration for residents in smart cities using remote sensing techniques. The proposed deep learning methods classify buildings based on energy efficiency. Data gathered from the sensor network to plan smart cities' development include a deep learning algorithm's structural assembly of data. The deep learning algorithm provides decision makers with a model for the big data stream. The numerical results show that the proposed method reduces energy consumption and enhances sensor data accuracy by 97.67% with better decision making in planning smart infrastructures and services. The experimental outcome of the DLSDSS enhances accuracy (97.67%), time complexity (98.7%), data distribution rate (97.1%), energy consumption rate (98.2%), load shedding ratio (95.8%), and energy efficiency (95.4%). Numéro de notice : A2022-812 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00010R2 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.14358/PERS.22-00010R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101972
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 9 (September 2022) . - pp 593 - 601[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022091 SL Revue Centre de documentation Revues en salle Disponible Smart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)
[article]
Titre : Smart city data science: Towards data-driven smart cities with open research issues Type de document : Article/Communication Auteurs : Iqbal H. Sarker, Auteur Année de publication : 2022 Article en page(s) : n° 100528 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] gestion urbaine
[Termes IGN] internet des objets
[Termes IGN] planification urbaine
[Termes IGN] science des données
[Termes IGN] sécurité
[Termes IGN] télédétection
[Termes IGN] ville intelligenteRésumé : (auteur) Cities are undergoing huge shifts in technology and operations in recent days, and ‘data science’ is driving the change in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting useful knowledge or actionable insights from city data and building a corresponding data-driven model is the key to making a city system automated and intelligent. Data science is typically the scientific study and analysis of actual happenings with historical data using a variety of scientific methodologies, machine learning techniques, processes, and systems. In this paper, we concentrate on and explore “Smart City Data Science”, where city data collected from various sources such as sensors, Internet-connected devices, or other external sources, is being mined for insights and hidden correlations to enhance decision-making processes and deliver better and more intelligent services to citizens. To achieve this goal, artificial intelligence, particularly, machine learning analytical modeling can be employed to provide deeper knowledge about city data, which makes the computing process more actionable and intelligent in various real-world city services. Finally, we identify and highlight ten open research issues for future development and research in the context of data-driven smart cities. Overall, we aim to provide an insight into smart city data science conceptualization on a broad scale, which can be used as a reference guide for the researchers, industry professionals, as well as policy-makers of a country, particularly, from the technological point of view. Numéro de notice : A2022-383 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Article DOI : 10.1016/j.iot.2022.100528 Date de publication en ligne : 20/04/2022 En ligne : https://doi.org/10.1016/j.iot.2022.100528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100660
in Internet of Things > vol 19 (August 2022) . - n° 100528[article]Spatial-temporal attentive LSTM for vehicle-trajectory prediction / Rui Jiang in ISPRS International journal of geo-information, vol 11 n° 7 (July 2022)
[article]
Titre : Spatial-temporal attentive LSTM for vehicle-trajectory prediction Type de document : Article/Communication Auteurs : Rui Jiang, Auteur ; Hongyun Xu, Auteur ; Gelian Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] données spatiotemporelles
[Termes IGN] navigation autonome
[Termes IGN] relation spatiale
[Termes IGN] système de transport intelligent
[Termes IGN] trajectoire (véhicule non spatial)
[Termes IGN] vision par ordinateurRésumé : (auteur) Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task because existing studies have mainly focused on the spatial interactions of adjacent vehicles regardless of the temporal dependencies. In this paper, we propose a spatial-temporal attentive LSTM encoder–decoder model (STAM-LSTM) to predict vehicle trajectories. Specifically, the spatial attention mechanism is used to capture the spatial relationships among neighboring vehicles and then obtain the global spatial feature. Meanwhile, the temporal attention mechanism is designed to distinguish the effects of different historical time steps on future trajectory prediction. In addition, the motion feature of vehicles is extracted to reveal the influence of dynamic information on vehicle-trajectory prediction, and is combined with the local and global spatial features to represent the integrated features of the target vehicle at each historical moment. The experiments were conducted on public highway trajectory datasets—US-101 and I-80 in NGSIM—and the results demonstrate that our model achieves state-of-the-art prediction performance. Numéro de notice : A2022-549 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi11070354 Date de publication en ligne : 21/06/2022 En ligne : https://doi.org/10.3390/ijgi11070354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101150
in ISPRS International journal of geo-information > vol 11 n° 7 (July 2022) . - n° 354[article]DiffusionNet: discretization agnostic learning on surfaces / Nicholas Sharp in ACM Transactions on Graphics, TOG, Vol 41 n° 3 (June 2022)
[article]
Titre : DiffusionNet: discretization agnostic learning on surfaces Type de document : Article/Communication Auteurs : Nicholas Sharp, Auteur ; Souhaib Attaiki, Auteur ; K. Crane, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] discrétisation
[Termes IGN] maillage
[Termes IGN] Perceptron multicouche
[Termes IGN] semis de points
[Termes IGN] Triangular Regular Network
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) We introduce a new general-purpose approach to deep learning on three-dimensional surfaces based on the insight that a simple diffusion layer is highly effective for spatial communication. The resulting networks are automatically robust to changes in resolution and sampling of a surface—a basic property that is crucial for practical applications. Our networks can be discretized on various geometric representations, such as triangle meshes or point clouds, and can even be trained on one representation and then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes. The only other ingredients in the method are a multi-layer perceptron applied independently at each point and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces and demonstrate state-of-the-art results for a variety of tasks, including surface classification, segmentation, and non-rigid correspondence. Numéro de notice : A2022-321 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1145/3507905 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1145/3507905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100369
in ACM Transactions on Graphics, TOG > Vol 41 n° 3 (June 2022) . - pp 1 - 16[article]Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica, vol 26 n° 2 (April 2022)PermalinkAnalysis of pedestrian movements and gestures using an on-board camera to predict their intentions / Joseph Gesnouin (2022)PermalinkApport de l’intelligence artificielle au domaine des villes intelligentes : application à l’assistance des déplacements des personnes à mobilité réduite / Nathan Aky (2022)PermalinkApprentissage de représentations et modèles génératifs profonds dans les systèmes dynamiques / Jean-Yves Franceschi (2022)PermalinkPermalinkFrom artificial intelligence to artificial human interaction : understand consumer acceptance of smart objects via mental representations of future interactions / Mohamed Hakimi (2022)PermalinkPermalinkPermalinkPermalinkPermalinkTowards expressive graph neural networks : Theory, algorithms, and applications / Georgios Dasoulas (2022)PermalinkTowards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)PermalinkUnsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)PermalinkPermalinkMachine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkExploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)PermalinkIntelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)PermalinkPermalinkLeveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)PermalinkPermalinkModélisation et simulation de comportements piétons réalistes en espace partagé avec un véhicule autonome / manon Prédhumeau (2021)PermalinkSemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)PermalinkModéliser ce qui résiste à la modélisation / Aurélien Bénel in Revue ouverte d'intelligence artificielle, ROIA, vol 1 n° 1 ([01/07/2020])PermalinkRencontre entre une philologue et un terminologue au pays des ontologies / Christophe Roche in Revue ouverte d'intelligence artificielle, ROIA, vol 1 n° 1 ([01/07/2020])PermalinkGeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)PermalinkPermalinkPermalinkAdvances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 / Michael R. Berthold (2020)PermalinkPermalinkPermalinkComparing supervised learning algorithms for Spatial Nominal Entity recognition / Amine Medad (2020)PermalinkPermalinkPermalinkPermalinkNonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkValidating the correct wearing of protection mask by taking a selfie: design of a mobile application "CheckYourMask" to limit the spread of COVID-19 / Karim Hammoudi (2020)PermalinkPermalinkEstimation de profondeur à partir d'images monoculaires par apprentissage profond / Michel Moukari (2019)PermalinkPermalinkHyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)PermalinkMachine learning techniques applied to geoscience information system and remote sensing / Saro Lee (2019)PermalinkProjection sur l’évolution de la distribution future de la population en utilisant du Machine Learning et de la géosimulation / Julie Grosmaire (2019)PermalinkPermalinkDo semantic parts emerge in convolutional neural networks? / Abel Gonzalez-Garcia in International journal of computer vision, vol 126 n° 5 (May 2018)PermalinkDésambiguïsation des entités spatiales par apprentissage actif / Amal Chihaoui in Revue internationale de géomatique, vol 28 n° 2 (avril - juin 2018)PermalinkPermalinkCut-Pursuit algorithm for regularizing nonsmooth functionals with graph total variation / Hugo Raguet (2018)PermalinkDeep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)PermalinkPermalinkPermalinkPermalinkMachine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018)PermalinkRéseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)PermalinkPermalinkPermalinkConstrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)PermalinkPermalinkPermalinkPermalinkSparsity, redundancy and robustness in artificial neural networks for learning and memory / Philippe Tigréat (2017)PermalinkPrediction of categorical spatial data via Bayesian updating / Xiang Huang in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)PermalinkPermalinkPermalinkUn environnement collaboratif pour l’acquisition de compétences en conception-développement d’applications centrées utilisateur. Application aux systèmes d'assitance à la santé et au bien-être / Maha Khemaja in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 4 (juillet - août 2015)PermalinkHow much past to see the future: a computational study in calibrating urban cellular automata / Ivan Blecic in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)PermalinkPermalinkEn route vers le Web sémantique ? / Françoise de Blomac in DécryptaGéo le mag, n° 161 (01/11/2014)PermalinkPermalinkJFSMA'14 systèmes multi-agents : principe de parcimonie, 8-10 octobre 2014, Loriol-sur-Drôme / Rémy Courdier (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 1. Représentation des connaissances et formalisation des raisonnements / Pierre Marquis (2014)PermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 2. 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Adam (2011)PermalinkDesigning agent behaviour in agent-based simulation through participatory method / Patrick Taillandier (2010)PermalinkIC 2010, 21es journées francophones d'Ingénierie des Connaissances, 8 - 11 juin 2010, Nîmes, France / Sylvie Desprès (2010)PermalinkIC 2010, Ingénierie des Connaissances 2010, 21es journées francophones, 9 - 10 juin 2010, Nîmes, France / Sylvie Desprès (2010)PermalinkJFSMA'10, dix-huitièmes journées francophones sur les systèmes multi-agents / Michel Occello (2010)PermalinkKnowledge engineering and management by the masses, 17th international conference, EKAW 2010, Lisbon, Portugal, October 2010 / Philipp Cimiano (2010)PermalinkE-11 - Extraction et gestion des connaissances EGC'2008 (2 volumes) (Bulletin de Revue des Nouvelles Technologies de l'Information, E-11 [01/04/2008]) / Fabrice GuilletPermalink8es rencontres nationales des jeunes chercheurs en intelligence artificielle, RJCIA 2007, 4 - 6 juillet 2007, Grenoble, France / Bruno Zanuttini (2007)PermalinkInteraction et pragmatique / Jean Caelen (2007)Permalink