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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 978-1-83968-320-6Langues : 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 temporelleIndex. 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 ReddyNuméro 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)
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, IALangues : 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 ordinateurIndex. 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- ConclusionNuméro 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.hal.science/tel-03273748/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98363 Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities / Li Tiancheng (2021)
Titre : Intelligent sensors for positioning, tracking, monitoring, navigation and smart sensing in smart cities Type de document : Monographie Auteurs : Li Tiancheng, Éditeur scientifique ; Jan Junkun, Éditeur scientifique ; Cao Yue, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 266 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0123-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] capteur (télédétection)
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de cible
[Termes IGN] exploration de données
[Termes IGN] Extreme Gradient Machine
[Termes IGN] fusion de données
[Termes IGN] Inférence floue
[Termes IGN] internet des objets
[Termes IGN] logique floue
[Termes IGN] navigation autonome
[Termes IGN] odomètre
[Termes IGN] positionnement en intérieur
[Termes IGN] réseau de capteurs
[Termes IGN] simulation de signal
[Termes IGN] ville intelligenteRésumé : (éditeur) The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing. Note de contenu : 1- MIMU/odometer fusion with state constraints for vehicle positioning during BeiDou signal outage: Testing and results
2- Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion
3- An elaborated signal model for simultaneous range and vector velocity estimation in FMCW radar
4- Hybrid solution combining Kalman filtering with Takagi–Sugeno fuzzy inference system for online car-following model calibration
5- Computationally efficient cooperative dynamic range-only SLAM based on sum of Gaussian filter
6- LoRaWAN geo-tracking using map matching and compass sensor fusion
7- A robust multi-sensor data fusion clustering algorithm based on density peaks
8- Extended target marginal distribution Poisson multi-Bernoulli mixture filter
9- A multi-core object detection coprocessor for multi-scale/type classification applicable to IoT devices
10- Leveraging uncertainties in softmax decision-making models for low-power IoT devices
11- Implementing deep learning techniques in 5G IoT networks for 3D indoor positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)
12- A novel hybrid algorithm based on Grey Wolf optimizer and fireworks algorithm
13- Passenger flow forecasting in metro transfer station based on the combination of singular spectrum analysis and AdaBoost-weighted extreme learning machine
14- A unified fourth-order tensor-based smart community systemNuméro de notice : 28609 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0123-9 En ligne : https://doi.org/10.3390/books978-3-0365-0123-9 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99453
Titre : Knowledge graph management and streaming in the context of edge computing Type de document : Thèse/HDR Auteurs : Weiqin Xu, Auteur ; Olivier Curé, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2021 Importance : 122 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Gustave Eiffel, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] flux continu
[Termes IGN] informatique en nuage
[Termes IGN] internet des objets
[Termes IGN] langage de requête
[Termes IGN] module d'extension
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] RDF
[Termes IGN] réseau sémantique
[Termes IGN] SPARQL
[Termes IGN] stockage de données
[Termes IGN] web sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Edge Computing proposes to distribute computation and data storage closer to original data sources. This technology is becoming an important trend in IT. This is mainly due to the emergence of the Internet of Things and its set of compact devices, eg sensors, actuators or gateways, whose computing and storing capacities are ever-increasing. Different from Cloud Computing, which targets large data centers, Edge Computing's computation distribution strategy can potentially reduce network pressure and make full use of computation power of edge devices.In order to support smart data processing at the edge of the network, a knowledge representation strategy is needed. In 2021, technologies belonging to the so-called Semantic Web are mature and robust enough to bring intelligence to Edge computing. These technologies correspond to the RDF (Resource Description Framework) data model, the RDFS (RDF Schema) and OWL (Web ontology Language) ontology languages and their associated reasoning services, the SPARQL query language. A cornerstone of such an approach is an Edge device compliant RDF database management system. However, most RDF stores are designed for powerful servers or Cloud Computing. These systems partly owe their efficiency to costly indexing strategies, ie based on multiples indexes.In the context of Edge computing, characterised by relatively limited memory footprint and computing power, it is not reasonable to use any of these RDF stores. Hence, a novel kind of RDF store is needed. In this work, we consider that some of its features must be an in-memory approach, low-memory footprint for both the system and its managed data, adapted query optimization techniques to make query processing as fast as possible. Moreover, reasoning at query run-time and stream processing are required by several of the use cases that we have identified in real-world situations.For the aim of compressing RDF data while maintaining querying speed, we make an extensive use of Succinct Data Structure (SDS) data structures to benefit from its data compression and high data retrieving speed simultaneously. This help us to get a self-indexed compact RDF store which does not require decompression operation. Our query processing approach is adapted to our storage layout and to standard SDS operations, namely access, rank and select. We prove the efficiency of our approach with thorough evaluation.In order to help the acceleration of RDFS reasoning, we have designed our system based on a semantic-aware encoding strategy named LiteMat. This encoding scheme, which has been developed and maintained by our research team, has been extended in the PhD thesis to support multiple inheritance, transitive and inverse properties. It thus extends the expressive power of addressed ontologies.In real IoT use cases, data are usually continuously coming from sensors or actuators. To address this issue, an extension of SuccinctEdge has been designed to handle those streaming data. This extension includes an extra data structure in our RDF store to process numeric data with time-based aggregations and an adapted streaming-SPARQL extension processor to permit the querying of streaming data. With the help of this extra data structure and the adapted query processor, one can easily query the dynamic RDF graph by a streaming-SPARQL query. However, query execution on a dynamic graph may have many repeating graph searching, which may heavily slow down the system. In order to solve this problem, we separate a query into dynamic part and static part. The result of the static part is computed once and stored all along the duration of the continuous query processing. Concerning the dynamic part, the corresponding result is combined with the static part result to generate the final result of each query execution. We prove that our streaming extension system is of low latency and of high throughput with good robustness and correctness properties. Note de contenu : 1- Introduction
2- Background knowledge
3- LiteMat, an encoding scheme for RDFS++
4- SuccinctEdge
5- Streaming SuccinctEdge
6- ConclusionNuméro de notice : 24026 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Gustave Eiffel : 2021 Organisme de stage : Laboratoire d’Informatique Gaspard Monge DOI : sans En ligne : https://tel.hal.science/tel-03697222/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101824 Leveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)
Titre : Leveraging class hierarchies with metric-guided prototype learning Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : 1-Pas de projet / Conférence : BMVC 2021, 32nd British Machine Vision Conference 22/11/2021 25/11/2021 online Royaume-Uni OA Proceedings Importance : 31 p. Note générale : bibliographie
préprint déposé sur ArXivLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] matrice d'erreur
[Termes IGN] prototype
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Not all errors are created equal. This is especially true for many key machine learning applications. In the case of classification tasks, the severity of errors can be summarized under the form of a cost matrix, which assesses the gravity of confusing each pair of classes. When the target classes are organized into a hierarchical structure, this matrix defines a metric. We propose to integrate this metric in a new and versatile classification layer in order to model the disparity of errors. Our method relies on jointly learning a feature-extracting network and a set of class representations, or prototypes, which incorporate the error metric into their relative arrangement in the embedding space. Our approach allows for consistent improvement of the severity of the network's errors with regard to the cost matrix. Furthermore, when the induced metric contains insight on the data structure, our approach improves the overall precision as well. Experiments on four different public datasets -- from agricultural time series classification to depth image semantic segmentation -- validate our approach. Numéro de notice : C2021-027 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.48550/arXiv.2007.03047 En ligne : https://www.bmvc2021-virtualconference.com/assets/papers/0084.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98983 PermalinkModélisation 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)PermalinkPermalinkPermalinkPermalinkPermalink