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scientifique

Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 117 - 133 Titre : Machine learning and geodesy: A survey Type de document : Article/Communication Auteurs : Jemil Butt, Auteur ; Andreas Wieser, Auteur ; Zan Gojcic, Auteur ; Caifa Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 117 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse de données [Termes IGN] apprentissage automatique [Termes IGN] données géodésiques [Termes IGN] espace de Hilbert [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise. Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy. Numéro de notice : A2021-321 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0043 Date de publication en ligne : 20/02/2021 En ligne : https://doi.org/10.1515/jag-2020-0043 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97478 [article]

An introduction to ethics in robotics and AI / Christoph Bartneck (2021)  

Public Titre : An introduction to ethics in robotics and AI Type de document : Monographie Auteurs : Christoph Bartneck, Auteur ; Christoph Lütge, Auteur ; Alan Wagner, Auteur ; Sean Welsch, Auteur Editeur : Springer International Publishing Année de publication : 2021 Importance : 117 p Présentation : . Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-51110-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] données massives [Termes IGN] édition en libre accès [Termes IGN] éthique [Termes IGN] navigation autonome [Termes IGN] protection de la vie privée [Termes IGN] robotique [Termes IGN] utilisateur militaire [Termes IGN] véhicule sans pilote Résumé : (éditeur) This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further. Note de contenu : 1- What is AI? 2- What is ethics? 3- Trust and fairness in AI systems 4- Responsibility and liability in the case of AI systems 5- Risks in the business of AI 6- Psychological aspects of AI 7- Privacy issues of AI 8- Application areas of AI 9- Autonomous vehicles 10- Military uses of AI 11- Ethics in AI and Robotics: A Strategic Challenge Numéro de notice : 28570 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-51110-4 En ligne : https://doi.org/10.1007/978-3-030-51110-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97726

Artificial intelligence for a better future / Bernd Carsten Stahl (2021)  

Public Titre : Artificial intelligence for a better future : An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies Type de document : Monographie Auteurs : Bernd Carsten Stahl, Auteur Editeur : Manchester [Royaume-Uni] : Manchester University Press Année de publication : 2021 Collection : European Research Council Importance : 128 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-69978-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] bien-être collectif [Termes IGN] droit [Termes IGN] édition en libre accès [Termes IGN] éthique [Termes IGN] règle Résumé : (éditeur) This open access book proposes a novel approach to Artificial Intelligence (AI) ethics. AI offers many advantages: better and faster medical diagnoses, improved business processes and efficiency, and the automation of boring work. But undesirable and ethically problematic consequences are possible too: biases and discrimination, breaches of privacy and security, and societal distortions such as unemployment, economic exploitation and weakened democratic processes. There is even a prospect, ultimately, of super-intelligent machines replacing humans. The key question, then, is: how can we benefit from AI while addressing its ethical problems? This book presents an innovative answer to the question by presenting a different perspective on AI and its ethical consequences. Instead of looking at individual AI techniques, applications or ethical issues, we can understand AI as a system of ecosystems, consisting of numerous interdependent technologies, applications and stakeholders. Developing this idea, the book explores how AI ecosystems can be shaped to foster human flourishing. Drawing on rich empirical insights and detailed conceptual analysis, it suggests practical measures to ensure that AI is used to make the world a better place. Note de contenu : Introduction 1- Perspectives on artificial intelligence 2- Concepts of ethics and their application to AI 3- Ethical issues of AI 4- Addressing ethical issues in AI 5- AI ecosystems for human flourishing: The background 6- AI ecosystems for human flourishing: The recommendations Conclusion Numéro de notice : 28321 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-69978-9 En ligne : https://doi.org/10.1007/978-3-030-69978-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98275

Data mining / Derya Birant (2021)  

Public Titre : Data mining : methods, applications and systems Type de document : Monographie Auteurs : Derya Birant, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 200 p. ISBN/ISSN/EAN : 978-1-83968-319-0 Note générale : Print ISBN 978-1-83968-318-3 eBook (PDF) ISBN 978-1-83968-320-6 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] acquisition de données [Termes IGN] apprentissage profond [Termes IGN] arbre de décision [Termes IGN] exploration de données [Termes IGN] Python (langage de programmation) [Termes IGN] série temporelle Index. décimale : 26.40 Intelligence artificielle Résumé : (Editeur) Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment. Note de contenu : 1. Deep Learning: Exemplar Studies in Natural Language Processing and Computer Vision / Selma Tekir and Yalin Bastanlar 2. Contribution to Decision Tree Induction with Python: A Review / Bouchra Lamrini 3. Association Rule Mining on Big Data Sets / Oguz Celik, Muruvvet Hasanbasoglu, Mehmet S. Aktas and Oya Kalipsiz 4. Data Mining in Banking Sector Using Weighted Decision Jungle Method / Derya Birant 5. Analytical Statistics Techniques of Classification and Regression in Machine Learning / Pramod Kumar, Sameer Ambekar, Manish Kumar and Subarna Roy 6. Clustering of Time-Series Data / Esma Ergüner Özkoç 7. Weather Nowcasting Using Deep Learning Techniques / Makhamisa Senekane, Mhlambululi Mafu and Molibeli Benedict Taele 8. Data Mining and Machine Learning for Software Engineering / Elife Ozturk Kiyak 9. Data Mining for Student Performance Prediction in Education / Ferda Ünal 10. Tracer Transport in a Homogeneous Porous Medium: Experimental Study and Acquisition Data with LabVIEW / Sana Dardouri and Jalila Sghaier 11. Data Mining and Fuzzy Data Mining Using MapReduce Algorithms / Poli Venkata Subba Reddy Numéro de notice : 26539 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87784 En ligne : http://doi.org/10.5772/intechopen.87784 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97753

Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)  

Public Titre : Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control Titre original : Exploration des algorithmes d'apprentissage par renforcement pour la perception et le controle d'un véhicule autonome par vision Type de document : Thèse/HDR Auteurs : Florence Carton, Auteur ; David Filliat, Directeur de thèse Editeur : Paris : Ecole Nationale Supérieure des Techniques Avancées ENSTA Année de publication : 2021 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l’Institut Polytechnique de Paris, Spécialité : Informatique, Données, IA Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage par renforcement [Termes IGN] classification dirigée [Termes IGN] instrument embarqué [Termes IGN] navigation autonome [Termes IGN] reconnaissance de formes [Termes IGN] réseau neuronal profond [Termes IGN] robot mobile [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) Reinforcement learning is an approach to solve a sequential decision making problem. In this formalism, an autonomous agent interacts with an environment and receives rewards based on the decisions it makes. The goal of the agent is to maximize the total amount of rewards it receives. In the reinforcement learning paradigm, the agent learns by trial and error the policy (sequence of actions) that yields the best rewards.In this thesis, we focus on its application to the perception and control of an autonomous vehicle. To stay close to human driving, only the onboard camera is used as input sensor. We focus in particular on end-to-end training, i.e. a direct mapping between information from the environment and the action chosen by the agent. However, training end-to-end reinforcement learning for autonomous driving poses some challenges: the large dimensions of the state and action spaces as well as the instability and weakness of the reinforcement learning signal to train deep neural networks.The approaches we implemented are based on the use of semantic information (image segmentation). In particular, this work explores the joint training of semantic information and navigation.We show that these methods are promising and allow to overcome some limitations. On the one hand, combining segmentation supervised learning with navigation reinforcement learning improves the performance of the agent and its ability to generalize to an unknown environment. On the other hand, it enables to train an agent that will be more robust to unexpected events and able to make decisions limiting the risks.Experiments are conducted in simulation, and numerous comparisons with state of the art methods are made. Note de contenu : 1- Introduction 2- Supervised learning and reinforcement learning background 3- State of the art 4- End-to-end autonomous driving on circuit with reinforcement learning 5- From lane following to robust conditional driving 6- Exploration of methods to reduce overfit 7- Conclusion Numéro de notice : 28325 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Informatique, Données, IA : ENSTA : 2021 DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03273748/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98363

SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-5 (August 2020)  

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GeoNat 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)  

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Advances and applications in deep learning / Marco Antonio Aceves-Fernandez (2020)  

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Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 / Michael R. Berthold (2020)  

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AI based robot safe learning and control / Xuefeng Zhou · (2020)  

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Comparing supervised learning algorithms for Spatial Nominal Entity recognition / Amine Medad (2020)  

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Introduction to data science and machine learning / Keshav Sud (2020)  

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Multi-agent systems / Ricardo Lopez-Ruiz (2020)  

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Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou · (2020)  

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On the path to AI / Thomas D. Grant (2020)  

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