Artificial intelligence is a word that carries with it heavy connotations. Although artificial intelligence is nothing more than the capacity for logic and understanding that machines can exhibit, in the minds of most people artificial intelligence is almost a Pandora's box that, when opened, will eventually signal the human race's doom..
The idea that machines pose an existential threat to human beings has been around for at least 60 years. It goes something like this: intelligent machines eventually realize the uselessness of human beings and turn against their creators. Or this: intelligent machines reduce human to cattle or even food after a dramatic war that human beings lose.
Human beings have created countless languages and writing systems that have allowed us to expand collective human knowledge over a period of thousands of years. Much of the knowledge that we utilized today, knowledge about the math, science, and the stars, originates from observations made thousands of years ago but which were recorded by writing systems, allowing this knowledge to be preserved and passed down.
Artificial intelligence has been used for many business, financial, medical, and other applications, and scientists and researchers are actively studying how these applications can be expanded to make human life simpler.
The applications of AI will be explored in this book, both the real applications to business, finance, medicine, and health and the theoretical applications. Even the sensational, perhaps exaggerated applications of AI will be explored in the context of taking a look at how AI may potentially be applied in the future. The purpose of this discussion is for the reader to understand what AI is by understanding how it is used.
Artificial intelligence is certainly a blessing at this point, but the reality that it may become a curse is not lost on some people. Understanding the full implications of AI requires a deep knowledge of what it is and where it came from.
For companies and businesses to take advantage of AI-powered and improved interactions, the conversation has to begin inside the organization. Leaders are supposed to start with the available channels and improve their smartness. From that point, they are supposed to ask key questions about engagements with customers and employees.
Here is a preview of what you will learn...
Brief history of artificial intelligence
The state of art of machine learning
Artificial neural networks applied to machine learning
Описание: A comprehensive guide to learning technologies that unlock the value in big data Cognitive Computing provides detailed guidance toward building a new class of systems that learn from experience and derive insights to unlock the value of big data.
Описание: The two volumes LNCS 11935 and 11936 constitute the proceedings of the 9th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2019, held in Nanjing, China, in October 2019.The 84 full papers presented were carefully reviewed and selected from 252 submissions.The papers are organized in two parts: visual data engineering; and big data and machine learning. They cover a large range of topics including information theoretic and Bayesian approaches, probabilistic graphical models, big data analysis, neural networks and neuro-informatics, bioinformatics, computational biology and brain-computer interfaces, as well as advances in fundamental pattern recognition techniques relevant to image processing, computer vision and machine learning.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 35897.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 27027.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Dominik Ry?ko; Piotr Gawrysiak; Marzena Kryszkiewi Название: Machine Intelligence and Big Data in Industry ISBN: 3319303147 ISBN-13(EAN): 9783319303147 Издательство: Springer Рейтинг: Цена: 18284.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents valuable contributions devoted topractical applications of Machine Intelligence and Big Data in various branchesof the industry.
Showcases the latest trends in new virtual/augmented reality healthcare and medical applications and provides an overview of the economic, psychological, educational and organizational impacts of these new applications and how we work, teach, learn and provide care.
With the current advances in technology innovation, the field of medicine and healthcare is rapidly expanding and, as a result, many different areas of human health diagnostics, treatment and care are emerging. Wireless technology is getting faster and 5G mobile technology allows the Internet of Medical Things (IoMT) to greatly improve patient care and more effectively prevent illness from developing.
This book provides an overview and review of the current and anticipated changes in medicine and healthcare due to new technologies and faster communication between users and devices.
The groundbreaking book presents state-of-the-art chapters on many subjects including:
A review of the implications of Virtual Reality (VR) and Augmented Reality (AR) healthcare applications
A review of current augmenting dental care
An overview of typical human-computer interaction (HCI) that can help inform the development of user interface designs and novel ways to evaluate human behavior to responses in VR and other new technologies
A review of telemedicine technologies
Building empathy in young children using augmented reality
AI technologies for mobile health of stroke monitoring & rehabilitation robotics control
Mobile doctor brain AI App
An artificial intelligence mobile cloud computing tool
Development of a robotic teaching aid for disabled children
Training system design of lower limb rehabilitation robot based on virtual reality
Автор: Saifullah Khalid Название: Applications of Artificial Intelligence in Electrical Engineering ISBN: 1799827186 ISBN-13(EAN): 9781799827184 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 42451.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies.
Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
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: Pablo Duboue Название: The Art of Feature Engineering: Essentials for Machine Learning ISBN: 1108709389 ISBN-13(EAN): 9781108709385 Издательство: Cambridge Academ Рейтинг: Цена: 6970.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.
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