Intelligent Science And Intelligent Data Engineering
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Intelligent Data Engineering And Automated Learning Ideal 2023
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Author : Paulo Quaresma
language : en
Publisher: Springer
Release Date : 2023-12-24
Intelligent Data Engineering And Automated Learning Ideal 2023 written by Paulo Quaresma and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-24 with Computers categories.
This book constitutes the proceedings of the 24th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023, held in Évora, Portugal, during November 22–24, 2023. The 45 full papers and 4 short papers presented in this book were carefully reviewed and selected from 77 submissions. IDEAL 2023 is focusing on big data challenges, machine learning, deep learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models, agents and hybrid intelligent systems, and real-world applications of intelligence techniques and AI. The papers are organized in the following topical sections: main track; special session on federated learning and (pre) aggregation in machine learning; special session on intelligent techniques for real-world applications of renewable energy and green transport; and special session on data selection in machine learning.
Intelligent Science And Intelligent Data Engineering
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Author : Jian Yang
language : en
Publisher: Springer
Release Date : 2013-02-11
Intelligent Science And Intelligent Data Engineering written by Jian Yang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-11 with Computers categories.
This book constitutes the proceedings of the third Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, IScIDE 2012, held in Nanjing, China, in October 2012. The 105 papers presented were carefully peer-reviewed and selected from 429 submissions. Topics covered include pattern recognition; computer vision and image processing; machine learning and computational intelligence; knowledge discovery, data mining, and web mining; graphics and computer visualization; and multimedia processing and applications.
Intelligent Science And Intelligent Data Engineering
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Author : Yanning Zhang
language : en
Publisher: Springer
Release Date : 2012-07-23
Intelligent Science And Intelligent Data Engineering written by Yanning Zhang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-23 with Computers categories.
This book constitutes the proceedings of the Sino-foreign-interchange Workshop on Intelligence Science and Intelligent Data Engineering, IScIDE 2011, held in Xi'an, China, in October 2011. The 97 papers presented were carefully peer-reviewed and selected from 389 submissions. The IScIDE papers in this volume are organized in topical sections on machine learning and computational intelligence; pattern recognition; computer vision and image processing; graphics and computer visualization; knowledge discovering, data mining, web mining; multimedia processing and application.
Intelligent Data Engineering And Analytics
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Author : Suresh Chandra Satapathy
language : en
Publisher: Springer Nature
Release Date : 2020-08-29
Intelligent Data Engineering And Analytics written by Suresh Chandra Satapathy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-29 with Technology & Engineering categories.
This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 4–5 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines.
Intelligent Data Engineering And Automated Learning
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Author : Jiming Liu
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-07-29
Intelligent Data Engineering And Automated Learning written by Jiming Liu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-07-29 with Business & Economics categories.
This book constitutes the throughly refereed post-proceedings of the 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, held in Hong Kong, China in March 2003. The 164 revised papers presented were carefully reviewed and selected from 321 submissions; for inclusion in this post-proceedings another round of revision was imposed. The papers are organized in topical sections an agents, automated learning, bioinformatics, data mining, multimedia information, and financial engineering.
Intelligent Data Engineering And Automated Learning Ideal 2020
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Author : Cesar Analide
language : en
Publisher: Springer Nature
Release Date : 2020-10-29
Intelligent Data Engineering And Automated Learning Ideal 2020 written by Cesar Analide and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-29 with Computers categories.
This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.
Guide To Intelligent Data Science
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Author : Michael R. Berthold
language : en
Publisher: Springer Nature
Release Date : 2020-08-06
Guide To Intelligent Data Science written by Michael R. Berthold and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-06 with Computers categories.
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
Intelligent Data Engineering And Automated Learning Ideal 2013
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Author : Hujun Yin
language : en
Publisher: Springer
Release Date : 2013-10-16
Intelligent Data Engineering And Automated Learning Ideal 2013 written by Hujun Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-16 with Computers categories.
This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2013, held in Hefei, China, in October 2013. The 76 revised full papers presented were carefully reviewed and selected from more than 130 submissions. These papers provided a valuable collection of latest research outcomes in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, neural networks, probabilistic modelling, swarm intelligent, multi-objective optimisation, and practical applications in regression, classification, clustering, biological data processing, text processing, video analysis, including a number of special sessions on emerging topics such as adaptation and learning multi-agent systems, big data, swarm intelligence and data mining, and combining learning and optimisation in intelligent data engineering.
Intelligence Science And Big Data Engineering Big Data And Machine Learning
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Author : Zhen Cui
language : en
Publisher: Springer Nature
Release Date : 2019-11-28
Intelligence Science And Big Data Engineering Big Data And Machine Learning written by Zhen Cui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-28 with Computers categories.
The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019. The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.
Intelligence Science And Big Data Engineering
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Author : Yi Sun
language : en
Publisher: Springer
Release Date : 2017-09-14
Intelligence Science And Big Data Engineering written by Yi Sun and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-14 with Computers categories.
This book constitutes the proceedings of the 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017, held in Dalian, China, in September 2017.The 48 full papers and 14 short papers presented in this volume were carefully reviewed and selected from 121 submissions. They deal with statistics and learning; deep neural networks; faces and people; objects; classification and clustering; imaging; biomedical signal processing; and recommendation.