Patterns Identification And Data Mining In Weather And Climate
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Patterns Identification And Data Mining In Weather And Climate
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Author : Abdelwaheb Hannachi
language : en
Publisher:
Release Date : 2021
Patterns Identification And Data Mining In Weather And Climate written by Abdelwaheb Hannachi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.
Patterns Identification And Data Mining In Weather And Climate
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Author : Abdelwaheb Hannachi
language : en
Publisher: Springer Nature
Release Date : 2021-05-06
Patterns Identification And Data Mining In Weather And Climate written by Abdelwaheb Hannachi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-06 with Science categories.
Advances in computer power and observing systems has led to the generation and accumulation of large scale weather & climate data begging for exploration and analysis. Pattern Identification and Data Mining in Weather and Climate presents, from different perspectives, most available, novel and conventional, approaches used to analyze multivariate time series in climate science to identify patterns of variability, teleconnections, and reduce dimensionality. The book discusses different methods to identify patterns of spatiotemporal fields. The book also presents machine learning with a particular focus on the main methods used in climate science. Applications to atmospheric and oceanographic data are also presented and discussed in most chapters. To help guide students and beginners in the field of weather & climate data analysis, basic Matlab skeleton codes are given is some chapters, complemented with a list of software links toward the end of the text. A number of technical appendices are also provided, making the text particularly suitable for didactic purposes. The topic of EOFs and associated pattern identification in space-time data sets has gone through an extraordinary fast development, both in terms of new insights and the breadth of applications. We welcome this text by Abdel Hannachi who not only has a deep insight in the field but has himself made several contributions to new developments in the last 15 years. - Huug van den Dool, Climate Prediction Center, NCEP, College Park, MD, U.S.A. Now that weather and climate science is producing ever larger and richer data sets, the topic of pattern extraction and interpretation has become an essential part. This book provides an up to date overview of the latest techniques and developments in this area. - Maarten Ambaum, Department of Meteorology, University of Reading, U.K. This nicely and expertly written book covers a lot of ground, ranging from classical linear pattern identification techniques to more modern machine learning, illustrated with examples from weather & climate science. It will be very valuable both as a tutorial for graduate and postgraduate students and as a reference text for researchers and practitioners in the field. - Frank Kwasniok, College of Engineering, University of Exeter, U.K.
International Conference On Applied Technologies
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Author : Miguel Botto-Tobar
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
International Conference On Applied Technologies written by Miguel Botto-Tobar 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 Computers categories.
The three-volume proceedings set CCIS 2049, 2050 and 2051 constitutes the refereed proceedings of the 5th International Conference on Applied Technologies on International Conference on Applied Technologies, ICAT 2023, held in Samborondon, Ecuador, November 22–24, 2023. The 66 papers included in these proceedings were carefully reviewed and selected from 250 submissions. They are organized in sections by topics as follows: Intelligent Systems, Communications, e-Commerce, e-Government, e-Learning, Electronics, Machine Vision, Security, Technology Trends, and Z AT for Engineering Aplications.
New Frontiers In Mining Complex Patterns
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Author : Annalisa Appice
language : en
Publisher: Springer
Release Date : 2017-07-01
New Frontiers In Mining Complex Patterns written by Annalisa Appice and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-01 with Computers categories.
This book features a collection of revised and significantly extended versions of the papers accepted for presentation at the 5th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2016, held in conjunction with ECML-PKDD 2016 in Riva del Garda, Italy, in September 2016. The book is composed of five parts: feature selection and induction; classification prediction; clustering; pattern discovery; applications.
Deep Learning For The Earth Sciences
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Author : Gustau Camps-Valls
language : en
Publisher: John Wiley & Sons
Release Date : 2021-08-18
Deep Learning For The Earth Sciences written by Gustau Camps-Valls 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 2021-08-18 with Technology & Engineering categories.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Intelligent Data Engineering And Automated Learning Ideal 2012
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Author : Hujun Yin
language : en
Publisher: Springer
Release Date : 2012-08-01
Intelligent Data Engineering And Automated Learning Ideal 2012 written by Hujun Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-01 with Computers categories.
This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.
Visualization Techniques For Climate Change With Machine Learning And Artificial Intelligence
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Author : Ashutosh Kumar Dubey
language : en
Publisher: Elsevier
Release Date : 2022-11-11
Visualization Techniques For Climate Change With Machine Learning And Artificial Intelligence written by Ashutosh Kumar Dubey and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-11 with Science categories.
Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world. This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action. - Includes case studies on the application of AI and machine learning for monitoring climate change effects and management - Features applications of software and algorithms for modeling and forecasting climate change - Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability
Recent Trends In Computational Sciences
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Author : Gururaj H L
language : en
Publisher: CRC Press
Release Date : 2023-11-17
Recent Trends In Computational Sciences written by Gururaj H L and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-17 with Computers categories.
This book is a compilation of research papers and presentations from the Fourth Annual International Conference on Data Science, Machine Learning and Blockchain Technology (AICDMB 2023, Mysuru, India, 16-17 March 2023). The book covers a wide range of topics, including data mining, natural language processing, deep learning, computer vision, big data analytics, cryptography, smart contracts, decentralized applications, and blockchain-based solutions for various industries such as healthcare, finance, and supply chain management. The research papers presented in this book highlight the latest advancements and practical applications in data science, machine learning, and blockchain technology, and provide insights into the future direction of these fields. The book serves as a valuable resource for researchers, students, and professionals in the areas of data science, machine learning, and blockchain technology.
Data Mining
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2015-04-13
Data Mining written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-13 with Computers categories.
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago
Establishing Ai Specific Cloud Computing Infrastructure
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2025-04-08
Establishing Ai Specific Cloud Computing Infrastructure written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-08 with Computers categories.
As artificial intelligence (AI) continues to drive innovation across industries, the need for specialized cloud computing infrastructure to support AI workloads is critical. Traditional cloud platforms often struggle to meet the high computational demands and storage requirements of AI models, especially as they grow in complexity and scale. Establishing AI-specific cloud computing infrastructure involves designing systems optimized for the needs of AI, such as powerful processing capabilities, massive data storage, and real-time processing. With advancements in hardware like graphics processing units and tensor processing units, along with sophisticated data management solutions, businesses can better harness the full potential of AI technologies. This specialized infrastructure enhances the performance and scalability of AI applications while enabling faster innovation and more efficient deployment of AI-driven solutions across sectors. Establishing AI-Specific Cloud Computing Infrastructure explores how AI has evolved as a transformative new technology, capable of delivering large incremental value to a wide range of sectors. It examines recent advances in innovation, specifically how computing power, data storage, and digitized data have led to AI-based applications for business and governance. This book covers topics such as digital technology, sustainable development, and artificial intelligence, and is a useful resource for computer engineers, business owners, academicians, data scientists, and researchers.