Détail de l'éditeur
IntechOpen
localisé à :
London
|
Documents disponibles chez cet éditeur
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Titre : Data mining : methods, applications and systems Type de document : Monographie Auteurs : Derya Birant, Editeur 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 descripteurs IGN] acquisition de données
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] arbre de décision
[Termes descripteurs IGN] exploration de données
[Termes descripteurs IGN] Python (langage de programmation)
[Termes descripteurs 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
Titre : Advances and applications in deep learning Type de document : Monographie Auteurs : Marco Antonio Aceves-Fernandez, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 122 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-1-83962-879-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] constante diélectrique
[Termes descripteurs IGN] méthode fondée sur le noyau
[Termes descripteurs IGN] programmation stochastique
[Termes descripteurs IGN] temps réel
[Termes descripteurs IGN] vision par ordinateurRésumé : (auteur) Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike. Note de contenu : 1- Advancements in deep learning theory and applications: Perspective in 2020 and beyond
2- Advances in convolutional neural networks
3- Transfer learning and deep domain adaptation
4- Deep learning enabled nanophotonics
5- Explainable artificial intelligence (xAI) approaches and deep meta-learning models
6- Dynamic decision-making for stabilized deep learning software platformsNuméro de notice : 28565 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87786 En ligne : https://doi.org/10.5772/intechopen.87786 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97647
Titre : Advances in forest management under global change Type de document : Monographie Auteurs : Ling Zhang, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 180 p. ISBN/ISSN/EAN : 978-1-83968-307-7 Note générale : Bibliographie
Print ISBN: 978-1-83968-306-0
eBook (PDF) ISBN: 978-1-83968-308-4Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] aire protégée
[Termes descripteurs IGN] analyse fractale
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] azote
[Termes descripteurs IGN] conservation des ressources forestières
[Termes descripteurs IGN] déboisement
[Termes descripteurs IGN] gestion forestière
[Termes descripteurs IGN] image SWOT
[Termes descripteurs IGN] incendie de forêt
[Termes descripteurs IGN] maladie phytosanitaire
[Termes descripteurs IGN] risque naturel
[Vedettes matières IGN] Végétation et changement climatiqueIndex. décimale : 48.30 Végétation et changement climatique Résumé : (Editeur) Advances in forest management will enhance the sustainable development of human society, and should be focused on. Under the context of global change, soil nutrients, especially nitrogen, should be carefully managed and monitored in plantations experiencing intensive nitrogen input, and forests with exotic plant invasion disturbance, considering its substantial contribution to global nitrous oxide. One negative effect of global change could be loss of biodiversity, which could be maintained by forest management. In addition, advanced technologies should also be developed to prevent fire in forests considering its increased frequency. Importantly, policies and technologies should also be developed for advanced forest management, such as deep learning in plant disease prevention, and quantitative strategic planning matrix in management of forest conservation. Note de contenu : 1. Nitrogen Cycling and Soil Amelioration in Camellia oleifera Plantations / Bangliang Deng and Ling Zhang
2. Research Progress of Forest Land Nutrient Management in China / Zhi Li, Yanmei Wang, Xiaodong Geng, Qifei Cai and Xiaoyan Xue
3. Plant Invasion and N2O Emission in Forest Ecosystems / Nasir Shad, Ling Zhang, Ghulam Mujtaba Shah, Fang Haifu, Muhammad Ilyas, Abbas Ali and Salman Ali Khan
4. Increasing Biodiversity of Russian Taiga Forests by Creating Mixed Forest Cultures of Scots Pine and Siberian Larch / Elena Runova
5. Sustainable Management of National Parks and Protected Areas for Conserving Biodiversity in India / Abhishek Kumar, Rajni Yadav, Meenu Patil, Pardeep Kumar, Ling Zhang, Amandeep Kaur, Sheenu Sharma, Sabir Hussain, Diksha Tokas and Anand Narain Singh
6. Gypsum/Desulfurization Fly Ash/Activated Shale Char/Claystone of Şırnak with Popped Biochar Composite Granules as Fire Inhibitor for Fire Hazard Risk in Forest Management / Yıldırım Ismail Tosun
7. Use of Fractal Analysis in the Evaluation of Deforested Areas in Romania / Daniel Constantin Diaconu, Răzvan Mihail Papuc, Daniel Peptenatu, Ion Andronache, Marian Marin, Răzvan Cătălin Dobrea, Cristian Constantin Drăghici, Radu-Daniel Pintilii and Alexandra Grecu
8. Automatic Recognition of Tea Diseases Based on Deep Learning / Jing Chen and Junying Jia
9. Forest Conservation Management Using SWOT Analysis and QSPM Matrix (Case Study in the Baluran National Park, East Java, Indonesia) / Adil SiswantoNuméro de notice : 26540 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87525 En ligne : http://doi.org/10.5772/intechopen.87525 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97758
Titre : Multi-agent systems : Strategies and applications Type de document : Monographie Auteurs : Ricardo Lopez-Ruiz, Editeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 170 p. Format : 19 x 27 cm ISBN/ISSN/EAN : 978-1-78985-394-0 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] apprentissage par renforcement
[Termes descripteurs IGN] reconstruction 3D
[Termes descripteurs IGN] reconstruction d'image
[Termes descripteurs IGN] réseau neuronal artificiel
[Termes descripteurs IGN] système multi-agentsRésumé : (éditeur) Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems. Note de contenu : 1- Cooperative adaptive learning control for a group of nonholonomic UGVs by output feedback
2- Multiagent systems for 3D reconstruction applications
3- A Q-learning-based approach for simple and multi-agent systems
4- Multi-Agent systems, simulation and nanotechnology
5- Applications of multi-agent system in power system engineering
6- Architecture of a microgrid and optimal energy management system
7- Multi-agent systems based advanced energy management of smart micro-grid
8- Smart learning environment: Paradigm shift for onlint learning
9- ICT: Vehicle for educational development and social TransformationNuméro de notice : 28572 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.81766 date de publication en ligne : 22/04/2020 En ligne : https://doi.org/10.5772/intechopen.81766 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97871
Titre : Open scientific data : why choosing and reusing the RIGHT DATA matters Type de document : Monographie Auteurs : Vera J. Lipton, Auteur Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 232 p. ISBN/ISSN/EAN : 978-1-83880-986-7 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes descripteurs IGN] chercheur
[Termes descripteurs IGN] Conseil européen pour la recherche nucléaire
[Termes descripteurs IGN] données ouvertes
[Termes descripteurs IGN] recherche scientifique
[Termes descripteurs IGN] traitement de donnéesRésumé : (Editeur) This book shows how the vision for open access to scientific data can be more readily achieved through a staged model that research funders, policy makers, scientists, and research organizations can adopt in their practice. Drawing on her own experiences with data processing, on early findings with open scientific data at CERN (the European Organization for Nuclear Research), and from case studies of shared clinical trial data, the author updates our understanding of research data - what it is; how it dynamically evolves across different scientific disciplines and across various stages of research practice; and how it can, and indeed should, be shared at any of those stages. The result is a flexible and pragmatic path for implementing open scientific data. Note de contenu :
Chapter 1. Introduction: Opening Up Data in Scientific Research
Chapter 2. The Case for Open Scientific Data: Theory, Benefits, Costs and Opportunities
Chapter 3. The Current Policies of Research Funders and Publishers
Chapter 4. The Unclear Meaning of Open Scientific Data
Chapter 5. Research Data Management at CERN
Chapter 6. Open Sharing of Clinical Trial Data
Chapter 7. Legal Issues Arising in Open Scientific Data
Chapter 8. The Staged Model for Open Scientific Data
Chapter 9. Conclusion: Towards Achievable and Sustainable Open Scientific DataNuméro de notice : 26517 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie DOI : 10.5772/intechopen.87201 En ligne : https://doi.org/10.5772/intechopen.87201 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97307 Geographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)
PermalinkPermalinkPermalinkPermalinkPermalink
