Название: Text Data Mining
Автор: Chengqing Zong, Rui Xia
Издательство: Springer, Tsinghua University Press
Год: 2021
Страниц: 363
Язык: английский
Формат: pdf (true), epub
Размер: 25.7 MB

Focuses on text data mining from an NLP perspective. This book discusses various aspects of text data mining. Unlike other books that focus on Machine Learning (ML) or databases, it approaches text data mining from a natural language processing (NLP) perspective. Text mining is a confluence of natural language processing, data mining, Machine Learning, and statistics used to mine knowledge from unstructured text. There have already been multiple textbooks dedicated to data mining, Machine Learning, statistics, and natural language processing. However, we seriously lack textbooks on text mining that systematically introduce important topics and up-to-date methods for text mining.

Название: The Democratization of Artificial Intelligence: Net Politics in the Era of Learning Algorithms
Автор: Andreas Sudmann
Издательство: Transcript Publishing
Год: 2020
Страниц: 332
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

The recent explosion in interest in both the democratization of data and artificial intelligence (AI) brings important questions to the fore: How democratic is AI? How to explore the political aspects of modern machine learning if many computer scientists consider AI technologies a black box fundamentally opaque to human understanding? And how does this affect questions of accountability and political agency?

Название: Web Semantics: Cutting Edge and Future Directions in Healthcare
Автор: Sarika Jain, Vishal Jain, Valentina Emilia Balas
Издательство: Academic Press, Elsevier
Год: 2021
Страниц: 271
Язык: английский
Формат: pdf (true)
Размер: 14.8 MB

The Semantic Web is a web of linked and integrated data that converts data to meaningful information by making use of metadata, providing engineers, researchers and clinicians with richer, more meaningful representations of data, which in turn offer more insight and powerful decision-making capabilities. Web Semantics strengthen the description of web resources in order to exploit them better and make them more meaningful for both human and machines. Being highly interdisciplinary, Semantic Web is an emerging field of research facilitating integration of information from disparate sources. The generic web is composed primarily of unstructured data, such as HTML pages and keyword searches.

Название: A Primer on Machine Learning in Subsurface Geosciences
Автор: Shuvajit Bhattacharya
Издательство: Springer
Год: 2021
Страниц: 182
Язык: английский
Формат: pdf (true)
Размер: 12.0 MB

This book provides readers with a timely review and discussion of the success, promise, and perils of Machine Learning (ML) in geosciences. It explores the fundamentals of data science and Machine Learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, Machine Learning will ultimately benefit these users. The book is written by a practitioner of Machine Learning and statistics, keeping geoscientists in mind.

Название: Probabilistic Machine Learning: An Introduction
Автор: Murphy K.P.
Издательство: The MIT Press
Год: 2021
Страниц: 863
Язык: английский
Формат: pdf (true)
Размер: 72.4 MB

A comprehensive introduction to Machine Learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and Deep Learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

Название: Mathematical Modeling and Soft Computing in Epidemiology
Автор: Jyoti Mishra, Ritu Agarwal
Издательство: CRC Press
Серия: Information Technology, Management and Operations Research Practices
Год: 2021
Страниц: 441
Язык: английский
Формат: pdf (true)
Размер: 22.5 MB

This book describes the uses of different mathematical modeling and soft computing techniques used in epidemiology for experiential research in projects such as how infectious diseases progress to show the likely outcome of an epidemic, and to contribute to public health interventions. This book covers mathematical modeling and soft computing techniques used to study the spread of diseases, predict the future course of an outbreak, and evaluate epidemic control strategies. This book explores the applications covering numerical and analytical solutions, presents basic and advanced concepts for beginners and industry professionals, and incorporates the latest methodologies and challenges using mathematical modeling and soft computing techniques in epidemiology.