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  • thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
  • English  (27)
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  • 2020-2024  (27)
  • 1980-1984
  • 2023  (27)
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  • English  (27)
  • French
  • Japanese
  • German  (2)
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  • 2020-2024  (27)
  • 1980-1984
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  • 1
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    Process Mining Workshops (2023)
    Springer Nature | Springer Nature Switzerland
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    Publication Date: 2024-04-14
    Description: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23–28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: – 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) – 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) – 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) – 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) – 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) – 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) – 1st International Workshop on Education meets Process Mining (EduPM) – 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
    Keywords: process mining ; process discovery ; process analytics ; process querying ; conformance checking ; predictive process monitoring ; data science ; knowledge graphs ; event data ; streaming analytics ; machine learning ; deep learning ; business process management ; health informatics ; thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining ; thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJQ Business mathematics and systems ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics ; thema EDItEUR::U Computing and Information Technology::UB Information technology: general topics::UBH Digital and information technologies: Health and safety aspects
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  • 2
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    Digital Interaction and Machine Intelligence (2023)
    Springer Nature | Springer Nature Switzerland
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    Publication Date: 2024-04-14
    Description: This open access book presents the proceedings of the 10th Machine Intelligence and Digital Interaction Conference. Artificial intelligence (AI) is rapidly affecting more aspects of our lives as a result of significant advancements in its research and the widespread usage of interactive technologies. This has led to the birth of several new social phenomena. Many nations have been working to comprehend these phenomena and discover solutions for moving artificial intelligence development in the proper direction to benefit individuals and communities at large. These efforts necessitate multidisciplinary approaches, encompassing not only the scientific fields involved in the creation of artificial intelligence and human–computer interaction but also strong collaboration between academics and practitioners. Because of this, the primary objective of the MIDI conference, which was conducted online on December 13–15, 2022, is to combine two up until recently distinct disciplines of research—artificial intelligence and human–technology interaction.
    Keywords: Computational Intelligence ; AI ; MIDI 2022 ; MIDI ; Machine Intelligence ; Digital Interaction ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence ; thema EDItEUR::U Computing and Information Technology::UN Databases ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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  • 3
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    Machine Learning and Biometrics (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: "Biometrics" and "Machine Learning for Biometrics." Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang.
    Keywords: simulation, deep learning, gender, feature extraction, retina, face recognition ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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  • 4
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    Machine Learning (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.
    Keywords: neural networks, deep learning, bioinformatics, precision medicine, personalized medicine, spinal cord injury ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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  • 5
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    Hypergraph Computation (2023)
    Springer Nature | Springer Nature Singapore
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    Publication Date: 2024-04-14
    Description: This open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.
    Keywords: Hypergraph ; Hypergraph Computation ; Hypergraph Learning ; Hypergraph Modelling ; Hypergraph Neural Network ; Complex Correlation Modelling ; High-Order Correlation Modelling ; 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 ; thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures
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  • 6
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    Pattern Recognition (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: Pattern recognition, despite its relatively short history, has already found practical application in many areas of human activity. Systems of pattern recognition usually support people in performing tasks related to ensuring security, including access to premises and devices, detection of unusual changes (e.g. in medicine, cartography, geology), diagnosing technical conditions of devices, and many others. Nevertheless, pattern recognition is probably the most developing area because of the great demand for such solutions in the different areas of our lives. In this book we have collected the experience of scientists from different parts of the world who have researched diverse areas connected directly or indirectly with pattern recognition. We hope that this book will be a treasure trove of knowledge and inspiration for further research in the field of pattern recognition.
    Keywords: renewable energy, image processing, feature extraction, segmentation, image analysis, image segmentation ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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  • 7
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    Classification and Data Science in the Digital Age (2023)
    Springer Nature | Springer International Publishing
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    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
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  • 8
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    Cognitive Robotics and Adaptive Behaviors (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: The development and use of robotics is affecting all aspects of modern life. There is a demand not only for robots that can move, interact, learn, and act in real-time dynamic and unconstrained environments but also for those that can interact smoothly and safely with the actions and movements of people within the same environments. In addition to managing complex motor coordination, these robots also require the ability to acquire and represent knowledge, deal with uncertainty at different operational levels, learn, reason, adapt, and have the autonomy to make intelligent decisions and act upon them. They should be able to learn from interaction, anticipate the outcomes of actions, acquire experiences and use them as required for future activities. Cognitive robotics is the interdisciplinary term used to describe robots that merge all these features and capabilities in their hardware and software architectures.
    Keywords: Computer science ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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  • 9
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    Learning to Quantify (2023)
    Springer Nature | Springer International Publishing
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    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
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  • 10
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    Machine Learning and Its Application to Reacting Flows (2023)
    Springer Nature | Springer International Publishing
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    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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  • 11
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    Revealing Media Bias in News Articles (2023)
    Springer Nature | Springer Nature Switzerland
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    Publication Date: 2024-04-14
    Description: This open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art in the other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint.
    Keywords: Natural Language Processing ; Deep Learning ; Media Bias ; Content Analysis ; Frame Analysis ; Social Aspects of Computing ; Information Retrieval ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies ; thema EDItEUR::C Language and Linguistics::CB Language: reference and general::CBX Language: history and general works ; thema EDItEUR::J Society and Social Sciences::JP Politics and government::JPA Political science and theory ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQL Natural language and machine translation ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies ; thema EDItEUR::C Language and Linguistics::CB Language: reference and general::CBX Language: history and general works ; thema EDItEUR::J Society and Social Sciences::JP Politics and government::JPA Political science and theory
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    Handbook of Computational Social Science for Policy (2023)
    Springer Nature | Springer International Publishing
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    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
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    Deep Learning Applications (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning architectures and the second section describes the state of the art of applications based on deep learning.
    Keywords: neural networks, machine learning, artificial intelligence, big data, risk assessment, security ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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    Recent Advances in Robotic Systems (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: This book brings together some recent advances and development in robotics. In 12 chapters, written by experts and researchers in respective fields, the book presents some up-to-date research ideas and findings in a wide range of robotics, including the design, modeling, control, learning, interaction, and navigation of robots. From an application perspective, the book covers UAVs, USVs, mobile robots, humanoid robots, graspers, and underwater robots. The unique text offers practical guidance to graduate students and researchers in research and applications in the field of robotics.
    Keywords: artificial intelligence, optimization, sensors, cognition, augmented reality, automation ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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    Hyperparameter Tuning for Machine and Deep Learning with R (2023)
    Springer Nature | Springer Nature Singapore
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    Publication Date: 2024-04-14
    Description: This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.
    Keywords: Hyperparameter Tuning ; Hyperparameters ; Tuning ; Deep Neural Networks ; Reinforcement Learning ; Machine Learning ; 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 ; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software ; thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics ; 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 ; thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software ; thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics
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    Automation and Control Trends (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: This book is an overview of the different paths automation and control engineering have taken lately, from a modern point of view. Built up with example chapters, this book provides some insight into the use of artificial intelligence and control theory on manufacturing, comfort analysis, reliability of modern digital systems, and the use of unusual reference and feedback signals as those coming from the brain. Nonetheless, some chapters are also devoted to a more traditional point of view of control theory, addressing complex problems where human intervention must be limited. Overall, this book is an effort to show that modern automation and control engineering are comprised by many diverse areas, which should interact in order to provide a complete result. In this way, as the systems become more complex and the control objectives more subjective, both, formal analytic and intelligent approaches, should be seen as complementary tools, not unrelated competitors. This book’s aim is precisely that of showing how broad and diverse the control objectives have become and how the abilities of the control engineer should be extended.
    Keywords: rehabilitation, reliability, sensors, radiation, homeostasis, labview ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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    Distributional Reinforcement Learning (2023)
    The MIT Press | The MIT Press
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    Publication Date: 2024-04-04
    Description: The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions.
    Keywords: Computer Science/Machine Learning & Neural Networks ; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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    Computational Formalism (2023)
    The MIT Press | The MIT Press
    Details
    Publication Date: 2024-03-23
    Description: How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.Though formalism is an essential tool for art historians, much recent art history has focused on the social and political aspects of art. But now art historians are adopting machine learning methods to develop new ways to analyze the purely visual in datasets of art images. Amanda Wasielewski uses the term “computational formalism” todescribe this use of machine learning and computer vision technique in art historical research. At the same time that art historians are analyzing art images in new ways, computer scientists are using art images for experiments in machine learning and computer vision. Their research, says Wasielewski, would be greatly enriched by the inclusion of humanistic issues.The main purpose in applying computational techniques such as machine learning to art datasets is to automate the process of categorization using metrics such as style, a historically fraught concept in art history. After examining a fifteen-year trajectory in image categorization and art dataset creation in the fields of machine learning and computer vision, Wasielewski considers deep learning techniques that both create and detect forgeries and fakes in art. She investigates examples of art historical analysis in the fields of computer and information sciences, placing this research in the context of art historiography. She also raises questions as which artworks are chosen for digitization, and of those artworks that are born digital, which works gain acceptance into the canon of high art.
    Keywords: Art history ; artificial intelligence ; machine learning ; formalism ; digital humanities ; connoisseurship ; image database ; authentication ; style ; thema EDItEUR::A The Arts::AG The Arts: treatments and subjects::AGA History of art ; thema EDItEUR::3 Time period qualifiers::3M c 1500 onwards to present day::3MN 19th century, c 1800 to c 1899 ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::A The Arts::AF The Arts: art forms::AFK Non-graphic and electronic art forms::AFKV Digital, video and new media arts
    Language: English
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    Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022 (2023)
    Springer Nature | Springer International Publishing
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    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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    Foundation Models for Natural Language Processing (2023)
    Springer Nature | Springer International Publishing
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    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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    Multi-Agent Technologies and Machine Learning (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: This book discusses multi-agent technologies (MATs) and machine learning (ML). These tools can be integrated and applied in industry, commerce, energy, medicine, psychology, and other areas. This volume consists of six chapters in three sections that discuss the integration, applications, and advanced results of MATs and ML.
    Keywords: Computer science ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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    Gradient Expectations (2023)
    The MIT Press | The MIT Press
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    Publication Date: 2024-04-14
    Description: An insightful investigation into the mechanisms underlying the predictive functions of neural networks—and their ability to chart a new path for AI.Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world? Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems.Downing delves into the known neural architecture of the mammalian brain to illuminate the structure of predictive networks and determine more precisely how the ability to predict might have evolved from more primitive neural circuits. He then surveys past and present computational neural models that leverage predictive mechanisms with biological plausibility, identifying elements, such as gradients, that natural and artificial networks share. Behind well-founded predictions lie gradients, Downing finds, but of a different scope than those that belong to today's deep learning. Digging into the connections between predictions and gradients, and their manifestation in the brain and neural networks, is one compelling example of how Downing enriches both our understanding of such relationships and their role in strengthening AI tools. Synthesizing critical research in neuroscience, cognitive science, and connectionism, Gradient Expectations offers unique depth and breadth of perspective on predictive neural-network models, including a grasp of predictive neural circuits that enables the integration of computational models of prediction with evolutionary algorithms.
    Keywords: Computer Science/Artificial Intelligence ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQN Neural networks and fuzzy systems ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GT Interdisciplinary studies::GTK Cognitive studies
    Language: English
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    Applications of Pattern Recognition (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: Nowadays, technological advances allow the development of many applications in different fields. In this book, “Applications of Pattern Recognition”, two important fields are shown. The first field, data analysis, is a good tool to identify patterns; in particular, it is observed by a stereoscopic calculation model based on fixation eye movement, a visual interactive programming learning system, an approach based on color analysis of Habanero chili pepper, an approach for the visualization and analysis of inconsistent data, and finally, a system for building 3D abstractions with wireframes. On the other hand, automatic systems help to detect or identify different kinds of patterns. It is applying to incomplete data analysis a retinal biometric approach based on crossing and bifurcation, an Arabic handwritten signature identification system, and finally, the use of clustering methods for gene expression data with RNA-seq.
    Keywords: machine learning, deep learning, gene expression, image processing, clustering, biometrics ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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    Pattern Recognition (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: Pattern recognition continued to be one of the important research fields in computer science and electrical engineering. Lots of new applications are emerging, and hence pattern analysis and synthesis become significant subfields in pattern recognition. This book is an edited volume and has six chapters arranged into two sections, namely, pattern recognition analysis and pattern recognition applications. This book will be useful for graduate students, researchers, and practicing engineers working in the field of machine vision and computer science and engineering.
    Keywords: machine learning, stroke, bioinformatics, computed tomography, face recognition, structural health monitoring ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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    Machine Learning (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: The volume of data that is generated, stored, and communicated across different industrial sections, business units, and scientific research communities has been rapidly expanding. The recent developments in cellular telecommunications and distributed/parallel computation technology have enabled real-time collection and processing of the generated data across different sections. On the one hand, the internet of things (IoT) enabled by cellular telecommunication industry connects various types of sensors that can collect heterogeneous data. On the other hand, the recent advances in computational capabilities such as parallel processing in graphical processing units (GPUs) and distributed processing over cloud computing clusters enabled the processing of a vast amount of data. There has been a vital need to discover important patterns and infer trends from a large volume of data (so-called Big Data) to empower data-driven decision-making processes. Tools and techniques have been developed in machine learning to draw insightful conclusions from available data in a structured and automated fashion. Machine learning algorithms are based on concepts and tools developed in several fields including statistics, artificial intelligence, information theory, cognitive science, and control theory. The recent advances in machine learning have had a broad range of applications in different scientific disciplines. This book covers recent advances of machine learning techniques in a broad range of applications in smart cities, automated industry, and emerging businesses.
    Keywords: deep learning, big data, malaria, data mining, cloud computing, fpga ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
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    Artificial Neural Networks (2023)
    IntechOpen | IntechOpen
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    Publication Date: 2024-04-14
    Description: The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.
    Keywords: pattern recognition, artificial intelligence, air pollution, fpga, solar energy, sensitivity analysis ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning
    Language: English
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    Safety in the Digital Age (2023)
    Springer Nature | Springer Nature Switzerland
    Details
    Publication Date: 2024-04-11
    Description: This open access book gathers authors from a wide range of social-scientific and engineering disciplines to review challenges from their respective fields that arise from the processes of social and technological transformation taking place worldwide. The result is a much-needed collection of knowledge about the integration of social, organizational and technical challenges that need to be tackled to uphold safety in the digital age. The contributors whose work features in this book help their readers to navigate the massive increase in the capability to generate and use data in developing algorithms intended for automation of work, machine learning and next-generation artificial intelligence and the blockchain technology already in such extensive use in real-world organizations. This book deals with such issues as: · How can high-risk and safety-critical systems be affected by these developments, in terms of their activities, their organization, management and regulation? · What are the sociotechnical challenges of the proliferation of big data, algorithmic influence and cyber-security challenges in health care, transport, energy production/distribution and production of goods? Understanding the ways these systems operate in the rapidly changing digital context has become a core issue for academic researchers and other experts in safety science, security and critical-infrastructure protection. The research presented here offers a lens through which the reader can grasp the way such systems evolve and the implications for safety—an increasingly multidisciplinary challenge that this book does not shrink from addressing.
    Keywords: Critical Infrastructure Protection ; Cybersecurity ; Blockchain Technologies ; Risk Assessment ; Machine Learning ; Safety Management in High-hazard Industrial Sectors ; Digitalisation ; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBC Engineering: general ; thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning ; thema EDItEUR::J Society and Social Sciences::JH Sociology and anthropology::JHB Sociology
    Language: English
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