Accelerating Discoveries In Data Science And Artificial Intelligence I
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Accelerating Discoveries In Data Science And Artificial Intelligence Ii
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Author : Frank M. Lin
language : en
Publisher: Springer Nature
Release Date : 2024-05-13
Accelerating Discoveries In Data Science And Artificial Intelligence Ii written by Frank M. Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-13 with Mathematics categories.
This edited volume on machine learning and big data analytics (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, International Association of Academicians (IAASSE), and Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and Data Science. With the fascinating development of technologies in several industries, there are numerous opportunities to develop innovative intelligence technologies to solve a wide range of uncertainties in various real-life problems. Researchers and academics have been drawn to building creative AI strategies by combining data science with classic mathematical methodologies. The book brings together leading researchers who wish to continue to advance the field and create a broad knowledge about the most recent research.
Accelerating Discoveries In Data Science And Artificial Intelligence I
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Author : Frank M. Lin
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Accelerating Discoveries In Data Science And Artificial Intelligence I written by Frank M. Lin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Mathematics categories.
The Volume 1 book on Accelerating Discoveries in Data Science and Artificial Intelligence (Proceedings of ICDSAI 2023), that was held on April 24-25, 2023 by CSUSB USA, the International Association of Academicians (IAASSE), and the Lendi Institute of Engineering and Technology, Vizianagaram, India is intended to be used as a reference book for researchers and practitioners in the disciplines of AI and data science. The book introduces key topics and algorithms and explains how these contribute to healthcare, manufacturing, law, finance, retail, real estate, accounting, digital marketing, and various other fields. The book is primarily meant for academics, researchers, and engineers who want to employ data science techniques and AI applications to address real-world issues. Besides that, businesses and technology creators will also find it appealing to use in industry.
Knowledge Guided Machine Learning
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Author : Anuj Karpatne
language : en
Publisher: CRC Press
Release Date : 2022-08-15
Knowledge Guided Machine Learning written by Anuj Karpatne and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-15 with Business & Economics categories.
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines. Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios. As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks. This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing. Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field. Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers. KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Accelerating Science And Engineering Discoveries Through Integrated Research Infrastructure For Experiment Big Data Modeling And Simulation
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Author : Kothe Doug
language : en
Publisher: Springer Nature
Release Date : 2023-01-17
Accelerating Science And Engineering Discoveries Through Integrated Research Infrastructure For Experiment Big Data Modeling And Simulation written by Kothe Doug and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-17 with Computers categories.
This book constitutes the refereed proceedings of the 22nd Smoky Mountains Computational Sciences and Engineering Conference on Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, SMC 2022, held virtually, during August 23–25, 2022. The 24 full papers included in this book were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows: foundational methods enabling science in an integrated ecosystem; science and engineering applications requiring and motivating an integrated ecosystem; systems and software advances enabling an integrated science and engineering ecosystem; deploying advanced technologies for an integrated science and engineering ecosystem; and scientific data challenges.
Intersection Of Artificial Intelligence Data Science And Cutting Edge Technologies From Concepts To Applications In Smart Environment
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Author : Yousef Farhaoui
language : en
Publisher: Springer Nature
Release Date : 2025-06-30
Intersection Of Artificial Intelligence Data Science And Cutting Edge Technologies From Concepts To Applications In Smart Environment written by Yousef Farhaoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Computers categories.
Offering a comprehensive exploration, this book navigates through foundational concepts to advanced applications, providing readers with a holistic understanding of how these domains intersect to create intelligent and responsive environments. The Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies: From Concepts to Applications in Smart Environments delves into the convergence of AI, data science, and innovative technologies within the realm of smart environments. Through a blend of theoretical insights and practical examples, the book illuminates the synergies between AI and data science, showcasing their pivotal roles in shaping the future of smart environments. From sensor technologies to machine learning algorithms, the text elucidates the mechanisms driving intelligence in these environments, while also delving into the ethical considerations and societal impacts of deploying such technologies. Whether you're a researcher, practitioner, or enthusiast in the fields of AI, data science, or smart environments, this book serves as a guiding beacon, offering valuable insights and methodologies to navigate the complexities of creating and optimizing intelligent environments for the benefit of society.
Artificial Intelligence In Drug Discovery
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Author : Nathan Brown
language : en
Publisher: Royal Society of Chemistry
Release Date : 2020-11-04
Artificial Intelligence In Drug Discovery written by Nathan Brown and has been published by Royal Society of Chemistry this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Computers categories.
Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia.
Accelerated Materials Discovery
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Author : Phil De Luna
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-02-21
Accelerated Materials Discovery written by Phil De Luna and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-21 with Computers categories.
Typical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).
Deep Learning For Physical Scientists
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Author : Edward O. Pyzer-Knapp
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-21
Deep Learning For Physical Scientists written by Edward O. Pyzer-Knapp and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-21 with Science categories.
Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the field Deep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome. Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader. From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy: A thorough introduction to the basic classification and regression with perceptrons An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training An examination of multi-layer perceptrons for learning from descriptors and de-noising data Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images A treatment of Bayesian optimization for tuning deep learning architectures Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access.
Artificial Intelligence In Science
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Author : Alistair Nolan
language : en
Publisher: Fondation Ipsen BookLab
Release Date : 2024-01-05
Artificial Intelligence In Science written by Alistair Nolan and has been published by Fondation Ipsen BookLab this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-05 with Science categories.
The rapid advances of artificial intelligence (AI) in recent years have led to numerous creative applications in science. Accelerating the productivity of science could be the most economically and socially valuable of all the uses of AI. Utilising AI to accelerate scientific productivity will support the ability of OECD countries to grow, innovate and meet global challenges, from climate change to new contagions. This publication is aimed at a broad readership, including policy makers, the public, and stakeholders in all areas of science. It is written in non-technical language and gathers the perspectives of prominent researchers and practitioners. The book examines various topics, including the current, emerging, and potential future uses of AI in science, where progress is needed to better serve scientific advancements, and changes in scientific productivity. Additionally, it explores measures to expedite the integration of AI into research in developing countries. A distinctive contribution is the book’s examination of policies for AI in science. Policy makers and actors across research systems can do much to deepen AI’s use in science, magnifying its positive effects, while adapting to the fast-changing implications of AI for research governance. ABOUT THE AUTHOR Alistair Nolan is a Senior Policy Analyst in the OECD’s Directorate for Science, Technology and Innovation. Prior to the OECD, Mr. Nolan led a range of industry-related analytic and technical assistance projects with the United Nations. Over a number of years at the OECD Alistair has been involved in work on skills and education assessment, entrepreneurship, private sector development and policy evaluation. Alistair is currently coordinating various streams of OECD work on artificial intelligence, and is overseeing the work on AI diffusion under the AI-WIPS project. Mr. Nolan oversaw preparation of the 2017 publication "The Next Production Revolution: Implications for Governments and Business", which examines a variety of emerging technologies, their impacts and policy implications, and which was referenced at the start of the 2017 G7 Taormina Action Plan. Mr. Nolan led work on 2020 publication "The Digitalisation of Science, Technology and Innovation : Key Developments and Policies", which among other topics addresses the role of AI in advanced production.
Artificial Intelligence For Medicine
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Author : Yoshiki Oshida
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2021-10-11
Artificial Intelligence For Medicine written by Yoshiki Oshida and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-11 with Technology & Engineering categories.
The use of artificial intelligence (AI) in various fields is of major importance to improve the use of resourses and time. This book provides an analysis of how AI is used in both the medical field and beyond. Topics that will be covered are bioinformatics, biostatistics, dentistry, diagnosis and prognosis, smart materials, and drug discovery as they intersect with AI. Also, an outlook of the future of an AI-assisted society will be explored.