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Smart city data science: Towards data-driven smart cities with open research issues / Iqbal H. Sarker in Internet of Things, vol 19 (August 2022)  

Public [article]  inInternet of Things > vol 19 (August 2022) . - n° 100528 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 intelligente Résumé : (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 [article]

Metalearning / Pavel Brazdil (2022)  

Public Titre : Metalearning : Applications to automated machine learning and data mining Type de document : Monographie Auteurs : Pavel Brazdil, Auteur ; Jan N. van Rijn, Auteur ; Carlos Soares, Auteur ; Joaquin Vanschoren, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2022 Importance : 346 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-67024-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse de groupement [Termes IGN] apprentissage automatique [Termes IGN] chaîne de traitement [Termes IGN] échantillonnage [Termes IGN] modèle stochastique [Termes IGN] ontologie [Termes IGN] optimisation (mathématiques) [Termes IGN] régression [Termes IGN] science des données [Termes IGN] série temporelle Résumé : (éditeur) This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence. ; Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence. Note de contenu : 1- Basic concepts and architecture 2- Advanced techniques and methods 3- Organizing and Exploiting Metadata Numéro de notice : 28698 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-67024-5 En ligne : https://doi.org/10.1007/978-3-030-67024-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100469

Data science: Measuring uncertainties / Carlos Alberto De Bragança Pereira (2021)  

Public Titre : Data science: Measuring uncertainties Type de document : Monographie Auteurs : Carlos Alberto De Bragança Pereira, Éditeur scientifique ; Adriano Polpo, Éditeur scientifique ; Agatha Rodrigues, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 256 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-0365-0793-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique [Termes IGN] algorithme espérance-maximisation [Termes IGN] analyse de groupement [Termes IGN] données massives [Termes IGN] entropie maximale [Termes IGN] équation de Riccati [Termes IGN] estimation bayesienne [Termes IGN] filtre de Kalman [Termes IGN] inférence statistique [Termes IGN] information sémantique [Termes IGN] intelligence artificielle [Termes IGN] logique floue [Termes IGN] science des données Résumé : (éditeur) With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems. Note de contenu : 1- An integrated approach for making inference on the number of clusters in a mixture model 2- Universal sample size invariant measures for uncertainty quantification in density estimation 3- Prior sensitivity analysis in a semi-parametric integer-valued time series model 4- The decomposition and forecasting of mutual investment funds using singular spectrum analysis 5- Channels’ confirmation and predictions’ confirmation: From the medical test to the raven paradox 6- On a class of tensor Markov fields 7- Objective Bayesian inference in probit models with intrinsic priors using variational approximations 8- A new multi-attribute emergency decision-making algorithm based on intuitionistic fuzzy cross-entropy and comprehensive grey correlation analysis 9- Cointegration and unit root tests: A fully Bayesian approach 10- A novel perspective of the Kalman filter from the Renyi entropy 11- Application of cloud model in qualitative forecasting for stock market trends 12- A novel comprehensive evaluation method for estimating the bank profile shape and dimensions of stable channels using the maximum entropy principle Numéro de notice : 28636 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-0793-4 En ligne : https://doi.org/10.3390/books978-3-0365-0793-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99694

Social networks with rich edge semantics / Quan Zheng (2017)  

Public Titre : Social networks with rich edge semantics Type de document : Monographie Auteurs : Quan Zheng, Auteur ; David Skillicorn, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2017 Importance : 230 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-315-39062-8 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information [Termes IGN] intégration de données [Termes IGN] modèle relationnel [Termes IGN] modélisation sémantique [Termes IGN] réseau social [Termes IGN] réseautage social [Termes IGN] science des données Résumé : (éditeur) Social Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features: - Introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time - Presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed - Includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriate - Shows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node - Illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups Suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks. Note de contenu : 1- Introduction 2- The core model 3- Background 4- Modelling relationships of different types 5- Modelling asymmetric relationships 6- Modelling asymmetric relationships with multiple types 7- Modelling relationships that change over time 8- Modelling positive and negative relationships 9- Signed graph-based semi-supervised learning 10- Combining directed and signed embeddings 11- Summary Numéro de notice : 25843 Affiliation des auteurs : non IGN Thématique : SOCIETE NUMERIQUE Nature : Monographie En ligne : https://www.taylorfrancis.com/books/9781315390628 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95250