Taguchi y h (24 Ergebnisse)

Zustand: New. pp. [vii] + 324 Figures.

Zustand: New. In English.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 242 pages. 6.14x0.48x9.21 inches. In Stock.

Zustand: New. Pijush Samui is working as a professor in the civil engineering department at NIT Patna, India. He graduated in 2000, with a B.Tech. in Civil Engineering from Indian Institute of Engineering Science and Technology, Shibpur, India. He received his .

Taschenbuch. Zustand: Neu. Neuware - This book brings together leading experts from around the world to explore the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) in healthcare. It provides a platform for studying a future where healthcare becomes more precise, personalized, and accessible for all. The book covers recent advancements that will shape the future of healthcare and how artificial intelligence is revolutionizing disease detection, from analyzing chest X-rays for pneumonia to solving the secrets of our genes. It investigates the transformative potential of smart devices, real-time analysis of heart data, and personalized treatment plan creation. It shows how ML and IoT work and presents real-world examples of how they are leading to earlier and more accurate diagnoses and personalized treatments.Therefore, this edited book will be an invaluable resource for researchers, healthcare professionals, data scientists, or simply someone passionate about the future of healthcare. Readers will discover the exciting possibilities that lie ahead at the crossroads of ML, IoT, and health informatics. …

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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Unsupervised Feature Extraction Applied to Bioinformatics | A PCA Based and TD Based Approach | Y-h. Taguchi | Taschenbuch | Unsupervised and Semi-Supervised Learning | xxii | Englisch | 2025 | Springer | EAN 9783031609848 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: New. In English.

Hardcover. Zustand: Brand New. 250 pages. 9.18x6.12x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. …

Zustand: New. In English.

Zustand: New. In English.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume describes the latest developments in using Artificial Intelligence (AI) and machine learning in the field of genomics. The chapters in this book cover a variety of topics such as ways toderive the biomarker and measure biological and pathological processes from transcriptomic data sets; techniques to infer the function of epigenetics using network-based methodology; a look at how CellOracle can figure out which gene-gene interaction is important; how to find a machine learning approach to figure out how microRNA affects the cardiovascular events; protocols on ways to find how AI/ML approaches can attack somatic variant detection in normal human tissue; and a description on how gene embeddings are powerful tools for predicting unknown functions of genes and drugs. Written in the highly successfulMethods in Molecular Biologyseries format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.Thorough and comprehensive,AI/ML-Driven Gene Analysis: Methods and Protocolsis a valuable tool for researchers interested in learning more about how cutting-edge methodology for AI/ML is applied to genomics.…

Taschenbuch. Zustand: Neu. Handbook of Machine Learning Applications for Genomics | Sanjiban Sekhar Roy (u. a.) | Taschenbuch | Studies in Big Data | x | Englisch | 2023 | Springer | EAN 9789811691607 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Hardcover. Zustand: Brand New. 2nd edition. 555 pages. 9.25x6.10x9.21 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This updated book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tensor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. …

Paperback. Zustand: Brand New. 228 pages. 9.25x6.10x0.63 inches. In Stock.

Hardcover. Zustand: Brand New. 228 pages. 9.25x6.10x0.79 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.…