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  • thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning  (6)
  • Springer International Publishing  (6)
  • English  (6)
  • French
  • Japanese
  • 2020-2024  (6)
  • 1980-1984
  • 2023  (6)
Collection
Keywords
Publisher
Language
  • English  (6)
  • French
  • Japanese
Years
  • 2020-2024  (6)
  • 1980-1984
Year
  • 1
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    Classification and Data Science in the Digital Age (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification.
    Keywords: Classification ; Data Science ; Clustering ; Statistical Learning ; Machine Learning ; Data Analysis ; Mutlivariate Analysis ; Statistical Inference ; Dimension Reduction ; Functional Data Analysis ; Time Series Analysis ; Network Analysis ; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
    Language: English
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  • 2
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    Learning to Quantify (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.
    Keywords: Information Retrieval ; Machine Learning ; Supervised Learning ; Data Mining ; Prevalence Estimation ; Class Prior Estimation ; Data Science ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNH Information retrieval ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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  • 3
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    Machine Learning and Its Application to Reacting Flows (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-11
    Description: This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation.
    Keywords: Machine Learning ; Combustion Simulations ; Combustion Modelling ; Big Data Analysis ; Dimensionality reduction ; Reduced-order modelling ; Neural Networks ; Turbulent Combustion ; Physics-based modelling ; Data-driven modelling ; Deep learning ; Thermoacoustics and its modelling ; Reactive molecular dynamics ; Simulations of reacting flows ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THF Fossil fuel technologies ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGM Materials science::TGMB Engineering thermodynamics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::P Mathematics and Science::PH Physics::PHH Thermodynamics and heat ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THF Fossil fuel technologies ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGM Materials science::TGMB Engineering thermodynamics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::P Mathematics and Science::PH Physics::PHH Thermodynamics and heat
    Language: English
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  • 4
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    Handbook of Computational Social Science for Policy (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact.
    Keywords: Computational Social Science ; Data Science ; Big Data Analytics ; Statistical Learning ; Machine Learning ; Sentiment Analysis ; Natural Language Processing ; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics ; thema EDItEUR::J Society and Social Sciences ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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  • 5
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    Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022 (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-11
    Description: This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI).
    Keywords: Assembly Processes & Systems ; Handling & Grasping ; Modelling & Simulation ; Human-robot-collaboration ; Industry 4.0 ; Industrial Robotics ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering::TJFM1 Robotics ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering::TJFM Automatic control engineering ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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  • 6
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    Foundation Models for Natural Language Processing (2023)
    Springer Nature | Springer International Publishing
    Details
    Publication Date: 2024-04-14
    Description: This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI.
    Keywords: Pre-trained Language Models ; Deep Learning ; Natural Language Processing ; Transformer Models ; BERT ; GPT ; Attention Models ; Natural Language Understanding ; Multilingual Models ; Natural Language Generation ; Chatbot ; Foundation Models ; Information Extraction ; Text Generation ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation ; thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQE Expert systems / knowledge-based systems ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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