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Titre : Introduction to data science and machine learning Type de document : Monographie Auteurs : Keshav Sud, Éditeur scientifique ; Pakize Erdogmus, Éditeur scientifique ; Seifedine Kadry, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 236 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-83880-371-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] langage à objets
[Termes IGN] logique floue
[Termes IGN] métadonnées
[Termes IGN] optimisation (mathématiques)
[Termes IGN] Python (langage de programmation)
[Termes IGN] segmentation d'imageRésumé : (éditeur) “Introduction to Data Science and Machine Learning” has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications. Note de contenu : 1- Introductory chapter: clustering with nature-inspired optimization algorithms
2- Best practices in accelerating the data science process in python
3- Software design for success
4- Embedded systems based on open source platforms
5- The K-means algorithm evolution
6- “Set of strings” framework for big data modeling
7- Investigation of fuzzy inductive modeling method in forecasting problems
8- Segmenting images using hybridization of K-means and fuzzy C-means algorithms
9- The software to the soft target assessment
10- The methodological standard to the assessment of the traffic simulation in real time
11- Augmented post systems: Syntax, semantics, and applications
12- Serialization in object-oriented programming languagesNuméro de notice : 28388 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77469 En ligne : https://doi.org/10.5772/intechopen.77469 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98687
Titre : Recent trends in artificial neural networks Type de document : Monographie Auteurs : Ali Sadollah, Éditeur scientifique ; Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 150 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-78985-859-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification floue
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] logique floue
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) Artificial intelligence (AI) is everywhere and it's here to stay. Most aspects of our lives are now touched by artificial intelligence in one way or another, from deciding what books or flights to buy online to whether our job applications are successful, whether we receive a bank loan, and even what treatment we receive for cancer. Artificial Neural Networks (ANNs) as a part of AI maintains the capacity to solve problems such as regression and classification with high levels of accuracy. This book aims to discuss the usage of ANNs for optimal solving of time series applications and clustering. Bounding of optimization methods particularly metaheuristics considered as global optimizers with ANNs make a strong and reliable prediction tool for handling real-life application. This book also demonstrates how different fields of studies utilize ANNs proving its wide reach and relevance. Note de contenu : 1- Time series from clustering: An approach to forecast crime patterns
2- Encountered problems of time series with neural networks: Models and architectures
3- Metaheuristics and artificial neural networks
4- An improved algorithm for optimising the production of biochemical systems
5- Object recognition using convolutional neural networks
6- Prediction of wave energy potential in India: A fuzzy-ANN approach
7- Deep learning training and benchmarks for Earth observation images: Data sets, features, and procedures
8- Data mining technology for structural control systems: Concept, development, and comparisonNuméro de notice : 28497 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77409 En ligne : https://doi.org/10.5772/intechopen.77409 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99247 Saliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)
[article]
Titre : Saliency-guided deep neural networks for SAR image change detection Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Xiaorui Ma, Auteur ; Xiaojun Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7365 - 7377 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changement
[Termes IGN] échantillonnage d'image
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] logique floue
[Termes IGN] occupation du sol
[Termes IGN] saillance
[Termes IGN] télédétection en hyperfréquenceMots-clés libres : hierarchical fuzzy C-means clustering (HFCM) Résumé : (auteur) Change detection is an important task to identify land-cover changes between the acquisitions at different times. For synthetic aperture radar (SAR) images, inherent speckle noise of the images can lead to false changed points, which affects the change detection performance. Besides, the supervised classifier in change detection framework requires numerous training samples, which are generally obtained by manual labeling. In this paper, a novel unsupervised method named saliency-guided deep neural networks (SGDNNs) is proposed for SAR image change detection. In the proposed method, to weaken the influence of speckle noise, a salient region that probably belongs to the changed object is extracted from the difference image. To obtain pseudotraining samples automatically, hierarchical fuzzy C-means (HFCM) clustering is developed to select samples with higher probabilities to be changed and unchanged. Moreover, to enhance the discrimination of sample features, DNNs based on the nonnegative- and Fisher-constrained autoencoder are applied for final detection. Experimental results on five real SAR data sets demonstrate the effectiveness of the proposed approach. Numéro de notice : A2019-536 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2913095 Date de publication en ligne : 19/05/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2913095 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94154
in IEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7365 - 7377[article]“Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap / Clancy Wilmott in Cartographic perspectives, n° 92 (2019)
[article]
Titre : “Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap Type de document : Article/Communication Auteurs : Clancy Wilmott, Auteur Année de publication : 2019 Article en page(s) : 15 p Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cartographie collaborative
[Termes IGN] classification
[Termes IGN] conception cartographique
[Termes IGN] données localisées des bénévoles
[Termes IGN] logique
[Termes IGN] OpenStreetMap
[Termes IGN] segmentation sémantique
[Termes IGN] taxinomie
[Vedettes matières IGN] CartologieRésumé : (auteur) In this paper I consider how debates in critical cartography about the classificatory and calculative logics of the map might be renegotiated through the concepts of “making-kin,” “sympoesis,” and the chthonic. Between Haraway’s (2014) Staying With The Trouble and Foucault’s (2002) writings on mathesis and taxinomia in The Order of Things, I argue that a more situated understanding of mapping—as an entanglement between people, tools, landscapes, cultures—might realise a more open, and more attentive, way of mapping. I return to the popular case study, OpenStreetMap, to excavate how the use and misuse of taxonomic and mathematical logics through its collaborative and amateur affordabilities shed light on different ways of sorting-with the world. I argue that, in the unexpected emergence of proposed classifications (and despite the disciplining power of cartographic discourses), roots of a new and more inclusive cartography linger in the archive, waiting to be fertilised. Numéro de notice : A2019-419 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14714/CP92.1451 Date de publication en ligne : 24/07/2019 En ligne : https://doi.org/10.14714/CP92.1451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93548
in Cartographic perspectives > n° 92 (2019) . - 15 p[article]A fuzzy formal concept analysis-based approach to uncovering spatial hierarchies among vague places extracted from user-generated data / Xiaoyu Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)
[article]
Titre : A fuzzy formal concept analysis-based approach to uncovering spatial hierarchies among vague places extracted from user-generated data Type de document : Article/Communication Auteurs : Xiaoyu Wu, Auteur ; Jianying Wang, Auteur ; Li Shi, Auteur ; Yong Gao, Auteur ; Yu Liu, Auteur Année de publication : 2019 Article en page(s) : pp 991 - 1016 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] incertitude géométrique
[Termes IGN] logique floue
[Termes IGN] processus de hiérarchisation analytique floueRésumé : (Auteur) The spatial hierarchy of part-whole relationships is an essential characteristic of the platial world. Constructing spatial hierarchies of places is valuable in association analysis and qualitative spatial reasoning. The emergence of large amounts of geotagged user-generated content provides strong support for modelling places. However, the vague nature of places and the complex spatial relationships among places make it intractable to understand and represent the hierarchies among places. In this paper, we introduce a fuzzy formal concept analysis-based approach to uncovering the spatial hierarchies among vague places. Each place is represented as a concept that consists of its extent and its intent. Based on the place concepts, the spatial hierarchies are generated and expressed as a graph that is easy to comprehend and contains abundant information on spatial relations. We also demonstrate the rationality of our result by comparing it with the result of a questionnaire survey. Numéro de notice : A2019-442 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1566550 Date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1080/13658816.2019.1566550 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92776
in International journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 991 - 1016[article]Réservation
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