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Annotation This book constitutes the refereed proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007, held in Birmingham, UK, in December 2007. The 170 revised full papers presented were carefully reviewed and selected from more than 270 submissions. The papers are organized in topical sections on learning and information processing, data mining and information management, bioinformatics and neuroinformatics, agents and distributed systems, financial engineering and modeling, agent-based approach to service sciences, as well as neural-evolutionary fusion algorithms and their applications.
This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications.
IDEAL 2008 was the ninth IDEAL conference to take place; earlier editions were held in Hong Kong, the UK, Australia and Spain. This was the first time, though hopefully not the last time, that it took place in Daejeon, South Korea, during November 2–5, 2008. As the name suggests, the conference attracts researchers who are involved in either data engineering or learning or, increasingly, both. The former topic involves such aspects as data mining (or intelligent knowledge discovery from databases), infor- tion retrieval systems, data warehousing, speech/image/video processing, and mul- media data analysis. There has been a traditional strand of data engineering at IDEAL conferences which has been based on financial data management such as fraud det- tion, portfolio analysis, prediction and so on. This has more recently been joined by a strand devoted to bioinformatics, particularly neuroinformatics and gene expression analysis. Learning is the other major topic for these conferences and this is addressed by - searchers in artificial neural networks, machine learning, evolutionary algorithms, artificial immune systems, ant algorithms, probabilistic modelling, fuzzy systems and agent modelling. The core of all these algorithms is adaptation.
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.
This book constitutes the refereed proceedings of the 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011, held in Norwich, UK, in September 2011. The 59 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2016, held in Yangzhou, China, in October 2016. The 68 full papers presented were carefully reviewed and selected from 115 submissions. They provide a valuable and timely sample of latest research outcomes in data engineering and automated learning ranging from methodologies, frameworks, and techniques to applications including various topics such as evolutionary algorithms; deep learning; neural networks; probabilistic modeling; particle swarm intelligence; big data analysis; applications in regression, classification, clustering, medical and biological modeling and predication; text processing and image analysis.
This book constitutes the refereed proceedings of the 16th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2015, held in Wroclaw, Poland, in October 2015. The 64 revised full papers presented were carefully reviewed and selected from 127 submissions. These papers provided a valuable collection of recent 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 modeling, swarm intelligent, multi-objective optimization, and practical applications in regression, classification, clustering, biological data processing, text processing, video analysis, IDEAL 2015 also featured a number of special sessions on several emerging topics such as computational intelligence for optimization of communication networks, discovering knowledge from data, simulation-driven DES-like modeling and performance evaluation, and intelligent applications in real-world problems.
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.
This book constitutes the refereed proceedings of the 11th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2010, held in Paisley, Scotland, in September 2010. The 47 revised full papers presented were carefully reviewed and selected from many submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
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.