Artificial Intelligence and Data Analytics for Energy Exploration and Production, Fred Aminzadeh, Cenk Temizel, Yasin Hajizadeh
Автор: Koushik Ghosh, Souvik Bhattacharyya Название: Noise Filtering for Big Data Analytics ISBN: 3110697092 ISBN-13(EAN): 9783110697094 Издательство: Walter de Gruyter Рейтинг: Цена: 173490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model.
Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information.
This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Автор: Chesterman Simon Название: We, the Robots?: Regulating Artificial Intelligence and the Limits of the Law ISBN: 1316517683 ISBN-13(EAN): 9781316517680 Издательство: Cambridge Academ Рейтинг: Цена: 38010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Should we regulate artificial intelligence? Can we? From self-driving cars and high-speed trading to algorithmic decision-making, the way we live, work, and play is increasingly dependent on AI systems. This book examines how our laws are dealing with AI, as well as what additional rules and institutions are needed.
Автор: Rajshree Srivastava, Pradeep Kumar Mallick, Siddha Название: Computational Intelligence for Machine Learning and Healthcare Informatics ISBN: 3110647826 ISBN-13(EAN): 9783110647822 Издательство: Walter de Gruyter Цена: 136310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: David D. Luxton Название: Artificial Intelligence in Behavioral and Mental Health Care ISBN: 0124202489 ISBN-13(EAN): 9780124202481 Издательство: Elsevier Science Рейтинг: Цена: 61750.00 T Наличие на складе: Поставка под заказ. Описание:
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy.
In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering.
This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source.
Автор: Patel Lomit Название: Lean AI: How Innovative Startups Use Artificial Intelligence to Grow ISBN: 1492059315 ISBN-13(EAN): 9781492059318 Издательство: Wiley Рейтинг: Цена: 42230.00 T Наличие на складе: Поставка под заказ. Описание: With this practical book, author Lomit Patel shows you how to use AI and automation to provide an operational layer atop those acquisition solutions to deliver amazing results for your company.
Автор: Bohr, Adam Название: Artificial Intelligence In Healthcare ISBN: 0128184388 ISBN-13(EAN): 9780128184387 Издательство: Elsevier Science Рейтинг: Цена: 110030.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Artificial Intelligence in Healthcare Data is more than a comprehensive introduction to artificial intelligence and machine learning. The book is split into two sections with an introduction to current healthcare data challenges that is followed by specific applications and case studies. The editors explore how AI is used as a tool in the analysis of healthcare data, specifically focusing on machine learning, deep learning, natural language processing. data privacy, cybersecurity and the ethics. Other sections explore how AI tools can help to interrogate data across a range of healthcare applications, including AI driven wearables and sensors and AI assisted surgery.
This book will be useful for researchers, graduate students and practitioners in computer science, data science, bioinformatics, health informatics, biomedical engineering and clinical engineering.
If you want to learn about data science and big data, then keep reading... Two manuscripts in one book:
Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning
Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining
This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in.
There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today.
Some of the topics covered in part 1 of this book include:
What is Data Science?
What Exactly Does a Data Scientist Do?
A Look at What Data Analytics Is All About
What is Data Mining and How Does It Fit in with Data Science?
Regression Analysis
Why is Data Visualization So Important When It Comes to Understanding Your Data?
How to work with Database Querying
A Look at Artificial Intelligence
What is Machine Learning and How Is It Different from Artificial Intelligence?
What is the Future of Artificial Intelligence and Machine Learning?
And much more
Some of the topics covered in part 2 of this book include:
What is big data, and why is it important?
The five V's behind big data
How big data is already impacting your life, and where big data may be headed
How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things
How companies and governments are using predictive analytics to get ahead of the competition or improve service
How big data is used for fraud detection
How big data can train intelligent computer systems
The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics
Upcoming trends in big data that are sure to have a large impact on your future
Artificial intelligence, and how big data drives its development
What machine learning is and how it is tied to big data
The relationship between big data, data analytics, and business intelligence
Insights into how big data impacts privacy issues
The pros and cons regarding big data
And much, much more
So if you want to learn more about data science and big data, click the "add to cart" button
Автор: by Shashi Narayan, Claire Gardent Название: Deep Learning Approaches to Text Production ISBN: 1681737604 ISBN-13(EAN): 9781681737607 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 95170.00 T Наличие на складе: Нет в наличии. Описание: Text production has many applications. It is used, for instance, to generate dialogue turns from dialogue moves, verbalise the content of knowledge bases, or generate English sentences from rich linguistic representations, such as dependency trees or abstract meaning representations. Text production is also at work in text-to-text transformations such as sentence compression, sentence fusion, paraphrasing, sentence (or text) simplification, and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular, we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text, how encoders learn to produce better input representations, and how neural generators account for task-specific objectives. Indeed, each text-production task raises a slightly different challenge (e.g, how to take the dialogue context into account when producing a dialogue turn, how to detect and merge relevant information when summarising a text, or how to produce a well-formed text that correctly captures the information contained in some input data in the case of data-to-text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally, this book considers text-to-text, meaning-to-text, and data-to-text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers, graduate students, and industrials interested in text production from different forms of inputs.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734575 ISBN-13(EAN): 9781681734576 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 87780.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis Название: Data Exploration Using Example-Based Methods ISBN: 1681734559 ISBN-13(EAN): 9781681734552 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 66530.00 T Наличие на складе: Невозможна поставка. Описание: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The book also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. The book concludes with a vision for further research and applications in this area.
Автор: Alex Lui, Anna Farzinder, Mingboo Gong Название: Transforming Healthcare with Big Data and AI ISBN: 1641138971 ISBN-13(EAN): 9781641138970 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 47130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Healthcare and technology are at a convergence point where significant changes are poised to take place. The vast and complex requirements of medical record keeping, coupled with stringent patient privacy laws, create an incredibly unwieldy maze of health data needs. While the past decade has seen giant leaps in AI, machine learning, wearable technologies, and data mining capacities that have enabled quantities of data to be accumulated, processed, and shared around the globe. Transforming Healthcare with Big Data and AI examines the crossroads of these two fields and looks to the future of leveraging advanced technologies and developing data ecosystems to the healthcare field.
This book is the product of the Transforming Healthcare with Data conference, held at the University of Southern California. Many speakers and digital healthcare industry leaders contributed multidisciplinary expertise to chapters in this work. Authors’ backgrounds range from data scientists, healthcare experts, university professors, and digital healthcare entrepreneurs. If you have an understanding of data technologies and are interested in the future of Big Data and A.I. in healthcare, this book will provide a wealth of insights into the new landscape of healthcare.
As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data.
Here are just a dozen of the many questions answered within these pages:
What does quantitative analysis of a system really mean?
What is a system?
What are big data and analystics?
How do you know your numbers are good?
What will the future data science environment look like?
How do you determine data provenance?
How do you gather and process information, and then organize, store, and synthesize it?
How does an organization implement data analytics?
Do you really need to think like a Chief Information Officer?
What is the best way to protect data?
What makes a good dashboard?
What is the relationship between eating ice cream and getting attacked by a shark?
The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO).
Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 T's for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.
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