Описание: Showcases applications of machine learning that are leading us to the next generation of computing and living standards. The book explores the application of machine learning to cutting-edge technologies that are playing a prominent role in improving the quality of life and the progress of civilization.
Автор: Funda Atun Название: Improving Societal Resilience to Disasters ISBN: 3319046535 ISBN-13(EAN): 9783319046532 Издательство: Springer Рейтинг: Цена: 7182.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book documents the outcomes of a study designed to explore ways of increasing resilience in a complex city system against disasters by focusing on the transportation system in London.
Описание: Social robots not only work with humans in collaborative workspaces - we meet them in shopping malls and even more personal settings like health and care. Especially for developments with a high societal impact like robots in health and care settings, the authors discuss not only technology, design and usage but also ethical aspects.
Описание: It also helps find ways in which we can measure possible consequences and societal impacts by taking the following factors into account: open public health issues and common AI solutions (with multiple case studies, such as TB and SARS: COVID-19), AI in sustainable health care, AI in precision medicine and data privacy issues.
Описание: Showcases the groundbreaking applications of machine learning that are leading to the next generation of computing and living standards. The focus of the book is machine learning and its application to specific domains, which is resulting in substantial civilizational progress.
Описание: This book contains thirty-five selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2017). This was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS).Topics treated in the various chapters reflect the state of the art in theoretical and numerical methods and tools for optimization, and engineering design and societal applications. The volume focuses particularly on intelligent systems for multidisciplinary design optimization (mdo) problems based on multi-hybridized software, adjoint-based and one-shot methods, uncertainty quantification and optimization, multidisciplinary design optimization, applications of game theory to industrial optimization problems, applications in structural and civil engineering optimum design and surrogate models based optimization methods in aerodynamic design.
Описание: Preface.- PART I: Adjoint Methods for Optimisation, Mesh Adaptation and Uncertainty Quantification.- Gradient projection, constraints and surface regularization methods in adjoint shape optimization, by Pavlos P. Alexias and Eugene De Villiers.- Adjoint Shape Optimisation using model Boundary Representation, by E. Andres-Perez et al..- CAD and Adjoint based Multipoint Optimization of an Axial Turbine Profile, by Ismael Sanchez Torreguitart, Tom Verstraete, and Lasse Mueller.- A Comparative Study of Two Different CAD-Based Mesh Deformation Methods for Structural Shape Optimization, by Marc Schwalbach, Tom Verstraete, Jens-Dominik Miller, and Nicolas R. Gauger.- Node-based Adjoint Surface Optimization of U-bend duct for pressure loss reduction, by Giacomo Alessi, Lilla Koloszar, Tom Verstraete, and J. van Beeck.- On the Properties of Solutions of the 2D Adjoint Euler Equations, by Carlos Lozano.- Finite Transformation Rigid Motion Mesh Morpher, by Athanasios G. Liatsikouras, Guillaume Pierrot, Gabriel Fougeron, and George S. Eleftheriou.- The Unsteady Continuous Adjoint Method Assisted by the Proper Generalized Decomposition Method, by V. S. Papageorgiou, K. D. Samouchos and K. C. Giannakoglou.- A Two-Step Mesh Adaptation Tool Based on RBF with application to turbomachinery Optimization Loops, by Flavio Gagliardi, Konstantinos T. Tsiakas and Kyriakos C. Giannakoglou.- Adjoint-based Aerodynamic Optimisation of Wing Shape Using Non-Uniform Rational B-splines, by Xingchen Zhang, Rejish Jesudasan and Jens-Dominik Mьller.- PART II: Surrogate-assisted Optimization of Real World problems.- A comparative evaluation of surrogate models for transonic wing shape optimization, by Emiliano Iuliano.- Study of the influence of the initial a-priori training dataset size in the efficiency and convergence of surrogate-based evolutionary optimization, by Daniel Gonzalez Juarez and Esther Andres Perez.- Garteur AD/AG52: Surrogate-based global optimization methods in preliminary aerodynamic design, by E. Andres-Perez et al..- A Response Surface Based Strategy for Accelerated Compressor Map Computation, by Dmitrij Ivanov, Dieter Bestle, and Christian Janke.- Surrogate-Based Shape Optimization of the ERCOFTAC Centrifugal Pump Impeller, by Remo De Donno and Stefano Rebay and Antonio Ghidoni.- CFD based Design Optimization of a Cabinet Nitrogen Generator, by Bбrbara Arizmendi Gutiйrrez and Edmondo Minisci.- Delaunay-based global optimization in nonconvex domains defined by hidden constraints, by Shahrouz Ryan Alimo, Pooriya Beyhaghi, and Thomas R. Bewley.- PART III: Applications of optimization in engineering design automation.- Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter, by Manuel Acosta and Stratis Kanarachos.- On Combinatorial Problem Representation based Ascent Assembly Design Optimization, by Michael Hellwig, Doris Entner, Thorsten Prante, Alexandru-Ciprian Zavoianu, Martin Schwarz, and Klara Fink.- On the Optimization of 2D Path Network Layouts in Engineering Designs via Evolutionary Computation Techniques, by Alexandru-Ciprian Ziivoianu, Susanne Saminger-Platz, Doris Entner, Thorsten Prante, Michael Hellwig, Martin Schwarz, and Klara Fink.- Taking Advantage of 3D Printing so as to Simultaneously Reduce Weight and Mechanical Bonding Stress, by Markus Schatz, Robert Schweikle, Christian Lausch, Michael Jentsch and Werner Konrad.- Interactive Optimization of Path Planning for a Robot Enabled by Virtual Commissioning, by Ruth Fleisch, Doris Entner, Thorsten Prante, Reinhard Pfefferkorn.- Box-Type Boom Design using Surrogate Modeling: Introducing an Industrial Optimization Benchmark, by Philipp Fleck, Doris Entner, Clemens Munzer, Michael Kommenda, Thorsten Prante, Martin Schwarz, Martin Hachl, and Michael Affenzeller.- Knowledge Objects Enable Mass-Individualization, by Joel Johansson and Fredrik Elgh.- Free-form Optimization of a Shell Structure with Curvature Constraint,
Описание: This book constitutes the proceedings of the 8th International Conference on Decision Support Systems Technologies, ICDSST 2022, held during May 23-25, 2022. The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST) is planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities. The main aim of this year’s conference is to investigate the role DSS and related technologies can play in mitigating the impact of pandemics and post-crisis recovery. The 15 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: decision support addressing modern industry; decision support addressing business and societal needs, and multiple criteria approaches.
Novel strategies for data-driven evolutionary optimization
Machine learning using distance-based methods
Counting cells and predicting immunoscore using gradient boosted convolutional neural networks
Kubelka-Munk model and stochastic model comparison in skin physical parameter retrieval using neural networks
A combined approach of neural networks and graphical models in skin cancer inference using spectral imaging
Using wave propagation simulations and convolutional neural networks to retrieve thin coating's thickness from hyperspectral images
Predicting future overweight and obesity from childhood growth data: A case study
Variable selection under a value acquisition budget
Stochastic approximation by successive piecewise linearization
Non-convex robust low-rank matrix recovery
Neural network learning via successive piecewise linearization
Learning for scientific computing purposes
Computational intelligence in design of new nanomaterials
Modeling flow, reactive transport and geomechanics in porous media
Physics constrained machine learning for industrial applications
Parameter and type identification in partial differential equations using deep neural networks
Stability maximization for layered moving web with total mass constraint
Similarity solutions for condensation on a non-isothermal vertical plate
Enhanced topology optimization approach using moving morphable components coupled with NURBS curves
Combined model order reduction and artificial neural network for data assimilation and damage detection in structures
Towards the optimization of fuzzy pattern trees by abs - linearization
Support vector machines in clusterwise linear regression
A Second-order method with enriched hessian information for composite sparse optimization problems
Missing value imputation via nonsmooth optimization and clusterwise linear regression
Parsimonious neural networks
Nobody can stop advancing artificial intelligence (AI) where developing
Computational sciences, physics field theories and geometry
Mini-symposium on ethics in AI
Essentializing software engineering practices for ethically designing and developing artificial intelligence systems 30 Ethics is important, but how can we implement it? Survey on software developers' views on AI ethics
Industrial IoT capabilities in reducing the LCOE of offshore wind energy: A review
High-Performance data analysis with the Helmholtz Analytics Toolkit (HeAT)
Dynamic data-driven application systems based on tensor factorization: learning the physics of model evolution
Predicting customer experience
Puhti-AI: Finland's new AI supercomputer
Using Artificial Intelligence to Classify Textual Applications for Reporting Purposes Application of machine learning methods to error control of approximate solutions
Iterative data selection strategy in offline data-driven evolutionary multiobjective optimization
On surrogate management in interactive multiobjective building energy system design
A modified deep neural network for the rapid inversion of geo-physical resistivity measurements
Using agents for automatic meta-modelling algorithm selection in data-driven multiobjective optimization problems
Future cooperation between Computational Science and AI in Industrial and Societal Applications - challenges, impact and expectations?
Artificial Intelligence, Deep Lea
Автор: Abhay Saxena, Ashutosh Kumar Bhatt, Rajeev Kumar Название: Artificial Intelligence for Societal Development and Global Well-Being ISBN: 1668424444 ISBN-13(EAN): 9781668424445 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31878.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Considers the various uses, best practices, and success factors of artificial intelligence across fields and industries and discusses critical ways in which the technology must be developed further for the good of society. The book covers a range of topics, including smart devices, artificial neural networks, and natural intelligence.
Описание: This book provides an analysis of the role of fog computing, cloud computing, and Internet of Things in providing uninterrupted context-aware services as they relate to Healthcare 4.0. It also considers aspects required to manage the complexity of fog computing for Healthcare 4.0 and also develops a comprehensive taxonomy.
Описание: Social robots not only work with humans in collaborative workspaces – we meet them in shopping malls and even more personal settings like health and care. Does this imply they should become more human, able to interpret and adequately respond to human emotions? Do we want them to help elderly people? Do we want them to support us when we are old ourselves? Do we want them to just clean and keep things orderly – or would we accept them helping us to go to the toilet, or even feed us if we suffer from Parkinson’s disease?The answers to these questions differ from person to person. They depend on cultural background, personal experiences – but probably most of all on the robot in question. This book covers the phenomenon of social robots from the historic roots to today’s best practices and future perspectives. To achieve this, we used a hands-on, interdisciplinary approach, incorporating findings from computer scientists, engineers, designers, psychologists, doctors, nurses, historians and many more. The book also covers a vast spectrum of applications, from collaborative industrial work over education to sales. Especially for developments with a high societal impact like robots in health and care settings, the authors discuss not only technology, design and usage but also ethical aspects. Thus this book creates both a compendium and a guideline, helping to navigate the design space for future developments in social robotics.
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