Название: Machine Learning in Healthcare: Fundamentals and Recent Applications
Автор: Bikesh Kumar Singh, G.R. Sinha
Издательство: CRC Press
Год: 2022
Страниц: 253
Язык: английский
Формат: pdf (true)
Размер: 13.2 MB

Artificial intelligence (AI) and Machine Learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods, social media, etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions, it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology, variability in medical data, small datasets, and lack of standard guidelines for application of AI. Therefore, the development of new AI/ML tools for various domains of medicine is an ongoing field of research.

Название: C# 10 in a Nutshell: The Definitive Reference (Final)
Автор: Joseph Albahari
Издательство: O’Reilly Media, Inc.
Год: 2022
Страниц: 1052
Язык: английский
Формат: epub
Размер: 10.2 MB

When you have questions about C# 10 or .NET 6, this best-selling guide has the answers you need. C# is a language of unusual flexibility and breadth, and with its continual growth, there's always so much more to learn. In the tradition of O'Reilly's Nutshell guides, this thoroughly updated edition is simply the best one-volume reference to the C# language available today.

Название: Computational Analysis of Storylines: Making Sense of Events (Studies in Natural Language Processing)
Автор: Tommaso Caselli, Eduard Hovy
Издательство: Cambridge University Press
Год: 2021
Страниц: 275
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Event structures are central in Linguistics and Artificial Intelligence research: people can easily refer to changes in the world, identify their participants, distinguish relevant information, and have expectations of what can happen next. Part of this process is based on mechanisms similar to narratives, which are at the heart of information sharing. But it remains difficult to automatically detect events or automatically construct stories from such event representations. This book explores how to handle today's massive news streams and provides multidimensional, multimodal, and distributed approaches, like automated deep learning, to capture events and narrative structures involved in a 'story'. This overview of the current state-of-the-art on event extraction, temporal and casual relations, and storyline extraction aims to establish a new multidisciplinary research community with a common terminology and research agenda. Graduate students and researchers in natural language processing, computational linguistics, and media studies will benefit from this book.

Название: Black Hat Python: программирование для хакеров и пентестеров, 2-е изд
Автор: Зейтц Д., Арнольд Т.
Издательство: Питep
Год: 2022
Формат: True PDF
Страниц: 224
Размер: 10 Mb
Язык: Русский

Когда речь идет о создании мощных и эффективных хакерских инструментов, большинство аналитиков по безопасности выбирают Python. Во втором издании бестселлера Black Hat Python вы исследуете темную сторону возможностей Python — все от написания сетевых снифферов, похищения учетных данных электронной почты и брутфорса каталогов до разработки мутационных фаззеров, анализа виртуальных машин и создания скрытых троянов.

Название: Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications
Автор: Om Prakash Jena, Bharat Bhushan, Utku Kose
Издательство: CRC Press
Год: 2022
Страниц: 292
Язык: английский
Формат: pdf (true)
Размер: 14.3 MB

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary Machine Learning (ML) and Deep Learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments.

Название: Prediction and Analysis for Knowledge Representation and Machine Learning
Автор: Avadhesh Kumar, Shrddha Sagar, T. Ganesh Kumar
Издательство: CRC Press
Год: 2022
Страниц: 232
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Number of approaches are being defined for statistics and Machine Learning. These approaches are used for the identification of the process of the system and the models created from the system's perceived data, assisting scientists in the generation or refinement of current models. Machine Learning (ML) is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to Machine Learning algorithms. It has a significant impact on comprehending difficult scientific problems.

Название: Regression Models for Data Science in R: Statistical inference for Data Science
Автор: Yassine Mousaif
Издательство: Independently published
Год: 2022
Страниц: 132
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB

What's Special about this Book: The ideal reader for this book will be quantitatively literate and has a basic understanding of statistical concepts and R programming. The student should have a basic understanding of statistical inference such as contained in "Statistical inference for Data Science". The book gives a rigorous treatment of the elementary concepts of regression models from a practical perspective. After reading the book and watching the associated videos, students will be able to perform multivariable regression models and understand their interpretations. Regression models are the workhorse of Data Science. They are the most well described, practical and theoretically understood models in statistics. A data scientist well versed in regression models will be able to solve and incredible array of problems. Perhaps the key insight for regression models is that they produce highly interpretable model fits. This is unlike Machine Learning algorithms, which often sacrifice interpretability for improved prediction performance or automation.

Название: Handbook on Computational Intelligence (In 2 Volumes)
Автор: Plamen Parvanov Angelov
Издательство: World Scientific Publishing
Серия: Series on Computational Intelligence
Год: 2016
Страниц: 957
Язык: английский
Формат: pdf, epub
Размер: 30.5 MB

With the Internet, the proliferation of Big Data, and autonomous systems, mankind has entered into an era of 'digital obesity'. In this century, Computational Intelligence (CI), such as thinking machines, have been brought forth to process complex human problems in a wide scope of areas - from social sciences, economics and biology, medicine and social networks, to cyber security. The Handbook of Computational Intelligence (in two volumes) prompts readers to look at these problems from a non-traditional angle. It takes a step by step approach, supported by case studies, to explore the issues that have arisen in the process. The Handbook covers many classic paradigms, as well as recent achievements and future promising developments to solve some of these very complex problems. Volume one explores the subjects of fuzzy logic and systems, artificial neural networks (ANNs), and learning systems. Volume two delves into evolutionary computation, hybrid systems, as well as the applications of Computational Intelligence in decision making, the process industry, robotics, and autonomous systems. This work is a ""one-stop-shop"" for beginners, as well as an inspirational source for more advanced researchers. It is a useful resource for lecturers and learners alike.

Название: Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications
Автор: Sujata Dash, Subhendu Kumar Pani, Joel Rodrigues
Издательство: CRC Press
Год: 2022
Страниц: 382
Язык: английский
Формат: pdf (true)
Размер: 72.2 MB

The novel applications of Internet of Things (IoT) in health care data analytics can be regarded as an emerging field in Computer Science, medicine, biology application, natural environmental engineering, and pattern recognition. Deep Learning (DL) has been showing tremendous improvement in accuracy, robustness, and cross- language generalizability over conventional approaches. Machine Learning (ML) can provide computational methods for accumulating, updating, and changing knowledge in the intelligent systems, especially learning mechanisms that help us induce knowledge from the data. ML is helpful in the cases where direct algorithmic solutions are unavailable, lack formal models, or require knowledge about the application domain that is skimpy. IoT is anticipated to have the capability to change the way we work and live. These computing methods also play a significant role in the design and optimization of diverse engineering disciplines.

Название: Computer Vision and Recognition Systems: Research Innovations and Trends
Автор: Chiranji Lal Chowdhary (PhD), G. Thippa Reddy (PhD)
Издательство: Apple Academic Press, CRC Press
Год: 2022
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 23.6 MB

This cutting-edge volume, Computer Vision and Recognition Systems: Research Innovations and Trends, focuses on how Artificial Intelligence (AI) can be used to give computers the ability to imitate human sight. It explains the essential modules that are necessary for comprehending Artificial Intelligence experiences to provide machines with the power of vision. With contributions from researchers in diverse countries, including Thailand, Spain, Japan, Turkey, Australia, and India, the book discusses how Machine Learning (ML) and Deep Learning (DL) are important aspects in computer vision and recognition systems. The volume also presents a number of innovative research developments, applications, and current trends in the field.