Intelligent Data Engineering and Automated Learning – IDEAL 2018

Intelligent Data Engineering and Automated Learning – IDEAL 2018 PDF Author: Hujun Yin
Publisher: Springer
ISBN: 3030034968
Category : Computers
Languages : en
Pages : 364

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Book Description
This two-volume set LNCS 11314 and 11315 constitutes the thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.

Intelligent Data Engineering and Automated Learning – IDEAL 2018

Intelligent Data Engineering and Automated Learning – IDEAL 2018 PDF Author: Hujun Yin
Publisher: Springer
ISBN: 3030034968
Category : Computers
Languages : en
Pages : 364

Get Book Here

Book Description
This two-volume set LNCS 11314 and 11315 constitutes the thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.

Intelligent Data Engineering and Automated Learning – IDEAL 2020

Intelligent Data Engineering and Automated Learning – IDEAL 2020 PDF Author: Cesar Analide
Publisher: Springer Nature
ISBN: 3030623653
Category : Computers
Languages : en
Pages : 633

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Book Description
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.

Intelligent Data Engineering and Automated Learning – IDEAL 2022

Intelligent Data Engineering and Automated Learning – IDEAL 2022 PDF Author: Hujun Yin
Publisher: Springer Nature
ISBN: 3031217535
Category : Computers
Languages : en
Pages : 564

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Book Description
This book constitutes the refereed proceedings of the 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022, which took place in Manchester, UK, during November 24-26, 2022. The 52 full papers included in this book were carefully reviewed and selected from 79 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

Intelligent Data Engineering and Automated Learning – IDEAL 2021

Intelligent Data Engineering and Automated Learning – IDEAL 2021 PDF Author: Hujun Yin
Publisher: Springer Nature
ISBN: 3030916081
Category : Computers
Languages : en
Pages : 663

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Book Description
This book constitutes the refereed proceedings of the 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, which took place during November 25-27, 2021. The conference was originally planned to take place in Manchester, UK, but was held virtually due to the COVID-19 pandemic. The 61 full papers included in this book were carefully reviewed and selected from 85 submissions. They deal with emerging and challenging topics in intelligent data analytics and associated machine learning paradigms and systems. Special sessions were held on clustering for interpretable machine learning; machine learning towards smarter multimodal systems; and computational intelligence for computer vision and image processing.

Intelligent Data Engineering and Automated Learning – IDEAL 2024

Intelligent Data Engineering and Automated Learning – IDEAL 2024 PDF Author: Vicente Julian
Publisher: Springer Nature
ISBN: 303177731X
Category :
Languages : en
Pages : 541

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Book Description


Intelligent Data Engineering and Automated Learning – IDEAL 2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019 PDF Author: Hujun Yin
Publisher: Springer Nature
ISBN: 3030336077
Category : Computers
Languages : en
Pages : 575

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Book Description
This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 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 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

Intelligent Data Engineering and Automated Learning – IDEAL 2023

Intelligent Data Engineering and Automated Learning – IDEAL 2023 PDF Author: Paulo Quaresma
Publisher: Springer Nature
ISBN: 3031482328
Category : Computers
Languages : en
Pages : 561

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Book Description
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 Data Engineering and Automated Learning – IDEAL 2018

Intelligent Data Engineering and Automated Learning – IDEAL 2018 PDF Author: Hujun Yin
Publisher: Springer
ISBN: 3030034933
Category : Computers
Languages : en
Pages : 890

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Book Description
This two-volume set LNCS 11314 and 11315 constitutes the thoroughly refereed conference proceedings of the 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, held in Madrid, Spain, in November 2018. The 125 full papers presented were carefully reviewed and selected from 204 submissions. These papers provided a timely sample of the latest advances in data engineering and automated learning, from methodologies, frameworks and techniques to applications. In addition to various topics such as evolutionary algorithms, deep learning neural networks, probabilistic modelling, particle swarm intelligence, big data analytics, and applications in image recognition, regression, classification, clustering, medical and biological modelling and prediction, text processing and social media analysis.

Proceedings of Data Analytics and Management

Proceedings of Data Analytics and Management PDF Author: Deepak Gupta
Publisher: Springer Nature
ISBN: 9811662851
Category : Technology & Engineering
Languages : en
Pages : 850

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Book Description
This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2021), held at Jan Wyzykowski University, Poland, during June 2021. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students.

Machine Learning Techniques for Assistive Robotics

Machine Learning Techniques for Assistive Robotics PDF Author: Miguel Angel Cazorla Quevedo
Publisher: MDPI
ISBN: 3039363387
Category : Technology & Engineering
Languages : en
Pages : 210

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Book Description
Assistive robots are categorized as robots that share their area of work and interact with humans. Their main goals are to help, assist, and monitor humans, especially people with disabilities. To achieve these goals, it is necessary that these robots possess a series of characteristics, namely the abilities to perceive their environment from their sensors and act consequently, to interact with people in a multimodal manner, and to navigate and make decisions autonomously. This complexity demands computationally expensive algorithms to be performed in real time. The advent of high-end embedded processors has enabled several such algorithms to be processed concurrently and in real time. All these capabilities involve, to a greater or less extent, the use of machine learning techniques. In particular, in the last few years, new deep learning techniques have enabled a very important qualitative leap in different problems related to perception, navigation, and human understanding. In this Special Issue, several works are presented involving the use of machine learning techniques for assistive technologies, in particular for assistive robots.