280 results on 'LN cat08778a'
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52. Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes
- Author
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Panesar, Arjun
- Subjects
Artificial intelligence ,Big data ,Computer programming ,Open source software - Abstract
Summary: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.
- Published
- 2019
53. Machine Learning Applications Using Python : Cases Studies from Healthcare, Retail, and Finance
- Author
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Mathur, Puneet
- Subjects
Artificial intelligence ,Computer programming ,Open source software ,Python (Computer program language) ,Artificial Intelligence ,Open Source ,Python - Abstract
Summary: Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you'll see examples that demonstrate how to use machine learning as a tool for business enhancement. As a domain expert, you will not only discover how machine learning is used in finance, healthcare, and retail, but also work through practical case studies where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the domains (healthcare, finance, and retail). Each section starts with an overview of machine learning and key technological advancements in that domain. You'll then learn more by using case studies on how organizations are changing the game in their chosen markets. This book has practical case studies with Python code and domain-specific innovative ideas for monetizing machine learning. You will: Discover applied machine learning processes and principles Implement machine learning in areas of healthcare, finance, and retail Avoid the pitfalls of implementing applied machine learning Build Python machine learning examples in the three subject areas.
- Published
- 2019
54. Machine learning for computer and cyber security : principles, algorithms, and practices.
- Author
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Gupta, Brij and Sheng, Quan Z.
- Subjects
Computer networks ,Security measures ,Data processing ,Computer security ,Machine learning ,Artificial intelligence - Abstract
Summary: While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.
- Published
- 2019
55. Text Analytics with Python : A Practitioner's Guide to Natural Language Processing.
- Author
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Sarkar, Dipanjan
- Subjects
Artificial intelligence ,Python (Computer program language) ,Big data - Abstract
Summary: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.
- Published
- 2019
56. The sciences of the artificial.
- Author
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Simon, Herbert A.
- Subjects
Science -- Philosophy ,Artificial intelligence ,Social science-- Methodology - Abstract
Summary: A classic for its insights on complex systems, design, and artificial intelligence, and its contribution to our understanding of human intelligence. -- Information from publisher.
- Published
- 2019
57. Wireless AI : wireless sensing, positioning, IoT, and communications.
- Author
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Liu, K. J. Ray and Wang, Beibei
- Subjects
Wireless communication systems ,Artificial intelligence ,Internet of things ,Indoor positioning systems (Wireless localization) - Abstract
Summary: With this groundbreaking text, discover how wireless artificial intelligence (AI) can be used to determine position at centimeter level, sense motion and vital signs, and identify events and people. Using a highly innovative approach that employs existing wireless equipment and signal processing techniques to turn multipaths into virtual antennas, combined with the physical principle of time reversal and machine learning, it covers fundamental theory, extensive experimental results, and real practical use cases developed for products and applications. Topics explored include indoor positioning and tracking, wireless sensing and analytics, wireless power transfer and energy efficiency, 5G and next-generation communications, and the connection of large numbers of heterogeneous IoT devices of various bandwidths and capabilities. Demo videos accompanying the book online enhance understanding of these topics. Providing a unified framework for wireless AI, this is an excellent text for graduate students, researchers, and professionals working in wireless sensing, positioning, IoT, machine learning, signal processing and wireless communications.
- Published
- 2019
58. Advances in Soft Computing and Machine Learning in Image Processing.
- Author
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Hassanien, Aboul Ella and Oliva, Diego Alberto
- Subjects
Artificial intelligence ,Computational intelligence ,Image processing ,Signal processing ,Speech processing systems ,Computational Intelligence ,Artificial Intelligence ,Signal, Image and Speech Processing - Abstract
Summary: This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
- Published
- 2018
59. AI for data science : artificial intelligence frameworks and functionality for deep learning, optimization, and beyond.
- Author
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Voulgaris, Zacharias and Bulut, Yunus Emrah
- Subjects
Artificial intelligence ,Machine learning ,Mathematical optimization ,Swarm intelligence ,Algorithms - Abstract
Summary: Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world.
- Published
- 2018
60. Application of FPGA to real-time machine learning.
- Author
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Antonik, Piotr
- Subjects
Artificial Intelligence ,Computational intellligence ,Image processing - Abstract
Summary: This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.
- Published
- 2018
61. Applications of Artificial Intelligence in Process Systems Engineering.
- Author
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Ren, Jingzheng, Shen, Weifeng, Man, Yi, and Dong, Lichun
- Subjects
Artificial intelligence ,Systems engineering - Abstract
Summary: Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases.
- Published
- 2018
62. Applied artificial intelligence : where AI can be used in business.
- Author
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Corea, Francesco
- Subjects
Artificial intelligence ,Business mathematics & systems ,COMPUTERS -- General - Abstract
Summary: This book deals with artificial intelligence (AI) and its several applications. It is not an organic text that should be read from the first page onwards, but rather a collection of articles that can be read at will (or at need). The idea of this work is indeed to provide some food for thoughts on how AI is impacting few verticals (insurance and financial services), affecting horizontal and technical applications (speech recognition and blockchain), and changing organizational structures (introducing new figures or dealing with ethical issues). The structure of the chapter is very similar, so I hope the reader won’t find difficulties in establishing comparisons or understanding the differences between specific problems AI is being used for. The first chapter of the book is indeed showing the potential and the achievements of new AI techniques in the speech recognition domain, touching upon the topics of bots and conversational interfaces. The second and thirds chapter tackle instead verticals that are historically data-intensive but not data-driven, i.e., the financial sector and the insurance one. The following part of the book is the more technical one (and probably the most innovative), because looks at AI and its intersection with another exponential technology, namely the blockchain. Finally, the last chapters are instead more operative, because they concern new figures to be hired regardless of the organization or the sector, and ethical and moral issues related to the creation and implementation of new type of algorithms. .
- Published
- 2018
63. Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
- Author
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Beysolow II, Taweh
- Subjects
Artificial intelligence ,Big data ,Computer programming ,Open source software ,Python (Computer program language) - Abstract
Summary: Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will: Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms .
- Published
- 2018
64. Applying machine learning for automated classification of biomedical data in subject-independent settings.
- Author
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Pham, Thuy T.
- Subjects
Machine learning ,Artificial intelligence ,Medical applications ,Medical informatics - Abstract
Summary: This book describes efforts to improve subject-independent automated classification techniques using a better feature extraction method and a more efficient model of classification. It evaluates three popular saliency criteria for feature selection, showing that they share common limitations, including time-consuming and subjective manual de-facto standard practice, and that existing automated efforts have been predominantly used for subject dependent setting. It then proposes a novel approach for anomaly detection, demonstrating its effectiveness and accuracy for automated classification of biomedical data, and arguing its applicability to a wider range of unsupervised machine learning applications in subject-independent settings.
- Published
- 2018
65. Architects of intelligence : the truth about AI from the people building it
- Author
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Ford, Martin
- Subjects
Artificial intelligence ,Scientists ,Computer Science - Published
- 2018
66. Artificial Intelligence : Fundamentals and Applications.
- Author
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Bhargava, Cherry and Sharma, Pradeep Kumar
- Subjects
Artificial intelligence ,Machine learning ,Applications of artificial intelligence - Abstract
Summary: This comprehensive reference text discusses the fundamental concepts of artificial intelligence and its applications in a single volume. Artificial Intelligence: Fundamentals and Applications presents a detailed discussion of basic aspects and ethics in the field of artificial intelligence and its applications in areas, including electronic devices and systems, consumer electronics, automobile engineering, manufacturing, robotics and automation, agriculture, banking, and predictive analysis. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, manufacturing engineering, pharmacy, and healthcare, this text: Discusses advances in artificial intelligence and its applications. Presents the predictive analysis and data analysis using artificial intelligence. Covers the algorithms and pseudo-codes for different domains. Discusses the latest development of artificial intelligence in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. Covers the applications of artificial intelligence in fields, including pharmacy and healthcare, electronic devices and systems, manufacturing, consumer electronics, and robotics.
- Published
- 2018
67. Artificial intelligence : with an introduction to machine learning.
- Author
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Neapolitan, Richard E. and Jiang, Xia
- Subjects
Artificial intelligence - Published
- 2018
68. Artificial intelligence and games.
- Author
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Yannakakis, Georgios N. and Togelius, Julian
- Subjects
Electronic games -- Programming ,Artificial intelligence ,Computer science - Abstract
Summary: This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.
- Published
- 2018
69. Artificial Intelligence for Big Data.
- Author
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Deshpande, Anand and Kumar, Manish
- Subjects
Artificial Intelligence ,Big data ,Ontology for Big Data - Abstract
Summary: Build next-generation Artificial Intelligence systems with Java About This Book Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java
- Published
- 2018
70. Artificial intelligence for fashion : how AI is revolutionizing the fashion industry.
- Subjects
Artificial Intelligence ,Neural Networks ,Data Mining ,Robotics & Manufacturing - Abstract
Summary: Learn how Artificial Intelligence (AI) is being applied in the fashion industry. With an application focused approach, this book provides real-world examples, breaks down technical jargon for non-technical readers, and provides an educational resource for fashion professionals. The book investigates the ways in which AI is impacting every part of the fashion value chain starting with product discovery and working backwards to manufacturing. Artificial Intelligence for Fashion walks you through concepts, such as connected retail, data mining, and artificially intelligent robotics. Each chapter contains an example of how AI is being applied in the fashion industry illustrated by one major technological theme. There are no equations, algorithms, or code. The technological explanations are cumulative so you'll discover more information about the inner workings of artificial intelligence in practical stages as the book progresses.
- Published
- 2018
71. Artificial intelligence in value creation : improving competitive advantage.
- Author
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Wodecki, Andrzej
- Subjects
Artificial intelligence ,Industrial management - Abstract
Summary: This book analyses various models of value creation in projects and businesses by applying different forms of Artificial Intelligence in their products and services. First presenting the main concepts and ideas behind AI, Wodecki assesses different models of technology-based value creation based upon the analysis of over 400 case studies. This framework shows how AI may influence both value creation and competitive advantage (efficiency, creativity and flexibility) within a modern organization. Finally, a conceptual model is formulated to evaluate AI-supported in-company projects and new ventures and identify key managerial and technical competencies required.
- Published
- 2018
72. Artificial intelligence research and development : current challenges, new trends and applications.
- Author
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Falomir, Zoe, Gibert, Karina, and Plaza, Enric
- Subjects
Artificial intelligence -- Congresses ,Machine learning -- Congresses ,Artificial intelligence ,CCIA - Published
- 2018
73. Artificial life and intelligent agents: second international symposium, ALIA 2016, Birmingham, UK, June 14-15, 2016, revised selected papers.
- Author
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Lewis, Peter R, Headleand, Christopher J., Battle, Steve, and Ritsos, Panagiotis D.
- Subjects
Robotics ,Artificial intelligence ,Computer simulation ,Computational intelligence ,Genetic algorithms - Abstract
Summary: This book constitutes the refereed proceedings of the Second International Symposium on Artificial Life and Intelligent Agents, ALIA 2016, held in Birmingham, UK, in June 2016. The 8 revised full papers and three revised short papers presented together with two demo papers were carefully reviewed and selected from 25 submissions. The papers are organized in topical sections on modelling; robotics; bio-inspired problem solving; human-like systems; applications and games.
- Published
- 2018
74. Big Data Demystified : How to use big data, data science and AI to make better business decisions and gain competitive advantag.
- Author
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Stephenson, David
- Subjects
Big data ,Data science ,Artificial intelligence - Abstract
Summary: Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business. "--Publisher's description.
- Published
- 2018
75. Build Android-Based Smart Applications : Using Rules Engines, NLP and Automation Frameworks.
- Author
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Mukherjee, Chinmoy
- Subjects
Open source software ,Computer programming ,Artificial intelligence ,Java (Computer program language) ,Mobile Computing - Abstract
Summary: Build smart applications using cutting-edge technologies such as rules engines, code automation frameworks, and natural language processing (NLP). This book provides step-by-step instructions on how to port nine rules engines (CLIPS, JRuleEngine, DTRules, Zilonis, TermWare, Roolie, OpenRules, JxBRE, and JEOPS) to the Android platform. You'll learn how to use each rules engine to build a smart application with sample code snippets so that you can get started with programming smart applications immediately. Build Android-Based Smart Applicationsalso describes porting issues with other popular rules engines (Drools, JLisa, Take, and Jess). This book is a step-by-step guide on how to generate a working smart application from requirement specifications. It concludes by showing you how to generate a smart application from unstructured knowledge using the Stanford POS (Part of Speech) tagger NLP framework. You will: Evaluate the available rules engines to see which rules engine is best to use for building smart applications Build smart applications using rules engines Create a smart application using NLP Automatically generate smart application from requirement specifications.
- Published
- 2018
76. Build Better Chatbots : A Complete Guide to Getting Started with Chatbots.
- Author
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Khan, Rashid and Das, Anik
- Subjects
Artificial intelligence ,Computer programming ,Open source software ,Web Development - Abstract
Summary: Learn best practices for building bots by focusing on the technological implementation and UX in this practical book. You will cover key topics such as setting up a development environment for creating chatbots for multiple channels (Facebook Messenger, Skype, and KiK); building a chatbot (design to implementation); integrating to IFTT (If This Then That) and IoT (Internet of Things); carrying out analytics and metrics for chatbots; and most importantly monetizing models and business sense for chatbots. Build Better Chatbots is easy to follow with code snippets provided in the book and complete code open sourced and available to download. With Facebook opening up its Messenger platform for developers, followed by Microsoft opening up Skype for development, a new channel has emerged for brands to acquire, engage, and service customers on chat with chatbots. You will: Work with the bot development life cycle Master bot UX design Integrate into the bot ecosystem Maximize the business and monetization potential for bots.
- Published
- 2018
77. Computer vision for assistive healthcare.
- Author
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Leo, Marco and Farinella, Giovanni Maria
- Subjects
Computer vision in medicine ,Virtual reality in medicine ,Artificial Intelligence ,Image Processing, Computer-Assisted ,Pattern Recognition, Automated - Abstract
Summary: Computer Vision for Assistive Healthcare describes how advanced computer vision techniques can provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition. In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring.-- Source other than the Library of Congress.
- Published
- 2018
78. Deep Learning with Azure : Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform.
- Author
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Salvaris, Mathew, Dean, Danielle, and Tok, Wee Hyong
- Subjects
Microsoft software ,Microsoft .NET Framework ,Artificial intelligence ,Microsoft and .NET ,Artificial Intelligence - Abstract
Summary: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn: Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure This book is for professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful. Mathew Salvaris, PhD is a senior data scientist at Microsoft in the Cloud and AI division, where he works with a team of data scientists and engineers building machine learning and AI solutions for external companies utilizing Microsoft's Cloud AI platform. Danielle Dean, PhD is a principal data science lead at Microsoft in the Cloud and AI division, where she leads a team of data scientists and engineers building artificial intelligence solutions with external companies utilizing Microsoft's Cloud AI platform. Wee Hyong Tok, PhD is a principal data science manager at Microsoft in the Cloud and AI division. He leads the AI for Earth Engineering and Data Science team, where his team of data scientists and engineers are working to advance the boundaries of state-of-the-art deep learning algorithms and systems.
- Published
- 2018
79. From AI to robotics : mobile, social, and sentient robots.
- Author
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Bhaumik, Arkapravo
- Subjects
Robotics ,Artificial intelligence ,Intelligent control systems - Abstract
Summary: "From AI to Robotics: Mobile, Social, and Sentient Robots is a journey into the world of agent-based robotics and it covers a number of interesting topics, both in the theory and practice of the discipline. The book traces the earliest ideas for autonomous machines to the mythical lore of ancient Greece and ends the last chapter with a debate on a prophecy set in the apparent future, where human beings and robots/technology may merge to create superior beings -- the era of transhumanism. Throughout the text, the work of leading researchers is presented in depth, which helps to paint the socio-economic picture of how robots are transforming our world and will continue to do so. This work is presented along with the influences and ideas from futurists, such as Asimov, Moravec, Lem, Vinge, and of course Kurzweil. The book furthers the discussion with concepts of Artificial Intelligence and how it manifests in robotic agents. Discussions across various topics are presented in the book, including control paradigm, navigation, software, multi-robot systems, swarm robotics, robots in social roles, and artificial consciousness in robots. These discussions help to provide an overall picture of current day agent-based robotics and its prospects for the future. Examples of software and implementation in hardware are covered in Chapter 5 to encourage the imagination and creativity of budding robot enthusiasts. The book addresses several broad themes, such as AI in theory versus applied AI for robots, concepts of anthropomorphism, embodiment and situatedness, extending theory of psychology and animal behavior to robots, and the proposal that in the future, AI may be the new definition of science. Behavior-based robotics is covered in Chapter 2 and retells the debate between deliberative and reactive approaches. The text reiterates that the effort of modern day robotics is to replicate human-like intelligence and behavior, and the tools that a roboticist has at his or her disposal are open source software, which is often powered by crowd-sourcing. Open source meta-projects, such as Robot Operating System (ROS), etc. are briefly discussed in Chapter 5. The ideas and themes presented in the book are supplemented with cartoons, images, schematics and a number of special sections to make the material engaging for the reader. Designed for robot enthusiasts -- researchers, students, or the hobbyist, this comprehensive book will entertain and inspire anyone interested in the exciting world of robots."
- Published
- 2018
80. Granular computing and intelligent systems : design with information granules of higher order and higher typ.
- Author
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Pedrycz, Witold and Chen, Shyi-Ming
- Subjects
Artificial intelligence ,Granular computing ,Fuzzy rule based system - Abstract
Summary: Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.
- Published
- 2018
81. How smart machines think.
- Author
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Gerrish, Sean
- Subjects
Neural networks (Computer science) ,Machine learning ,Artificial intelligence - Abstract
Summary: The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world-and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution-at least for now.
- Published
- 2018
82. Human + machine : reimagining work in the age of AI.
- Author
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Daugherty, Paul R.
- Subjects
Technological innovations ,Artificial intelligence ,Business -- Data processing - Abstract
Summary: Artificial intelligence (AI) is transforming how we work right now. Are you ready? In the past, robots were typically large pieces of machinery, sectioned off from human workers to perform precise, mechanical tasks on an assembly line. But now, bots and other AI technologies go far beyond this in augmenting human capabilities--not just robots on the factory floor of an auto plant, but algorithms in the back office of a healthcare insurer and chatbots interacting with retail customers. Unlike any software tool or service that's come before, artificial intelligence has the power to profoundly change the very nature of work itself--and this is happening in all kinds of enterprises and across all functions of the organization. There's a current and growing imperative: businesses that understand how to harness AI can surge ahead, while those who neglect it are in danger of being left behind. In Human + Machine, Accenture technology leaders H. James Wilson and Paul R. Daugherty vividly illustrate how AI is redefining work and the economy. At the core of this paradigm shift is the transformation of business processes--all the step-by-step tasks that take place within an organization, from operations to customer service to workers' own personal productivity habits. As humans and smart machines collaborate ever more closely, work processes become more fluid and adaptive, enabling companies to change them on the fly--or completely reimagine them.-- Provided by publisher
- Published
- 2018
83. Intuitionistic fuzziness and other intelligent theories and their applications. / M Hadjiski, K T Atanassov.
- Author
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Hadjiski, M and Atanasov, Krasimir
- Subjects
Artificial intelligence ,Computational intelligence ,Fuzzy systems - Abstract
Summary: This book gathers extended versions of the best papers presented at the 8th IEEE conference on Intelligent Systems, held in Sofia, Bulgaria on September 4-6, 2016, which are mainly related to theoretical research in the area of intelligent systems. The main focus is on novel developments in fuzzy and intuitionistic fuzzy sets, the mathematical modelling tool of generalized nets and the newly defined method of intercriteria analysis. The papers reflect a broad and diverse team of authors, including many young researchers from Australia, Bulgaria, China, the Czech Republic, Iran, Mexico, Poland, Portugal, Slovakia, South Korea and the UK.
- Published
- 2018
84. Learning systems : from theory to practice.
- Author
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Sgurev, Vasil, Piuri, Vincenzo, and Jotsov, Vladimir
- Subjects
Fuzzy sets ,Computational intelligence ,Machine learning ,Artificial intelligence - Abstract
Summary: By presenting the latest advances in fuzzy sets and computing with words from around the globe, this book disseminates recent innovations in advanced intelligent technologies and systems. From intelligent control and intuitionistic fuzzy quantifiers to various data science and industrial applications, it includes a wide range of valuable lessons learned and ideas for future intelligent products and systems.
- Published
- 2018
85. Linked Data : Storing, Querying, and Reasoning.
- Author
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Sakr, Sherif, Wylot, Marcin, Mutharaju, Raghava, Le Phuoc, Danh, and Fundulaki, Irini
- Subjects
Artificial intelligence ,Computers ,Information storage and retrieval - Abstract
Summary: This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking. To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions. Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.
- Published
- 2018
86. Linked Data : Storing, Querying, and Reasoning.
- Author
-
Sakr, Sherif, Fundulaki, Irini, Le Phuoc, Danh, Mutharaju, Raghava, and Wylot, Marcin
- Subjects
Artificial intelligence ,Computers ,Information storage and retrieval ,Models and Principles ,Artificial Intelligence ,Information Storage and Retrieval - Abstract
Summary: This book describes efficient and effective techniques for harnessing the power of Linked Data by tackling the various aspects of managing its growing volume: storing, querying, reasoning, provenance management and benchmarking. To this end, Chapter 1 introduces the main concepts of the Semantic Web and Linked Data and provides a roadmap for the book. Next, Chapter 2 briefly presents the basic concepts underpinning Linked Data technologies that are discussed in the book. Chapter 3 then offers an overview of various techniques and systems for centrally querying RDF datasets, and Chapter 4 outlines various techniques and systems for efficiently querying large RDF datasets in distributed environments. Subsequently, Chapter 5 explores how streaming requirements are addressed in current, state-of-the-art RDF stream data processing. Chapter 6 covers performance and scaling issues of distributed RDF reasoning systems, while Chapter 7 details benchmarks for RDF query engines and instance matching systems. Chapter 8 addresses the provenance management for Linked Data and presents the different provenance models developed. Lastly, Chapter 9 offers a brief summary, highlighting and providing insights into some of the open challenges and research directions. Providing an updated overview of methods, technologies and systems related to Linked Data this book is mainly intended for students and researchers who are interested in the Linked Data domain. It enables students to gain an understanding of the foundations and underpinning technologies and standards for Linked Data, while researchers benefit from the in-depth coverage of the emerging and ongoing advances in Linked Data storing, querying, reasoning, and provenance management systems. Further, it serves as a starting point to tackle the next research challenges in the domain of Linked Data management.
- Published
- 2018
87. Machine learning and artificial intelligence.
- Author
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Joshi, Ameet V.
- Subjects
Artificial Intelligence ,Machine Learning ,Azure ,Machine Learning Studio - Abstract
Summary: This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.
- Published
- 2018
88. Machine learning and human intelligence : the future of education for the 21st century.
- Author
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Luckin, Rosemary
- Subjects
Artificial intelligence ,Machine learning ,Human-computer interaction ,Education -- Effect of technological innovations on - Abstract
Summary: Intelligence is at the heart of what makes us human, but the methods we use for identifying, talking about and valuing human intelligence are impoverished. We invest artificial intelligence (AI) with qualities it does not have and, in so doing, risk losing the capacity for education to pass on the emotional, collaborative, sensory and self-effective aspects of human intelligence that define us. To address this, Rosemary Luckin--leading expert in the application of AI in education - proposes a framework for understanding the complexity of human intelligence. She identifies the comparative limitation of AI when analyzed using the same framework, and offers clear-sighted recommendations for how educators can draw on what AI does best to nurture and expand our human capabilities.
- Published
- 2018
89. Machine Learning for Text.
- Author
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Aggarwal, Charu C
- Subjects
Artificial intelligence ,Data mining ,Data Mining and Knowledge Discovery ,Artificial Intelligence - Abstract
Summary: Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.
- Published
- 2018
90. Machine Learning for Text.
- Author
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Aggarwal, Charu C.
- Subjects
Artificial intelligence ,Data mining ,Data Mining and Knowledge Discovery ,Artificial Intelligence - Abstract
Summary: Text analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.
- Published
- 2018
91. Microsoft Computer Vision APIs Distilled: Getting Started with Cognitive Service.
- Author
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Del Sole, Alessandro
- Subjects
Artificial Intelligence ,Computer Programming ,Computer Science - Abstract
Summary: Dive headfirst into Microsofts Computer Vision APIs through sample-driven scenarios! Imagine an app that describes to the visually impaired the objects around them, or reads the Sunday paper, a favorite magazine, or a street sign. Or an app that is capable of monitoring what is happening inside an area without human control, and then makes a decision based on interpreting an occurrence detected with a live camera. This book teaches developers Microsoft's Computer Vision APIs, a service capable of understanding and interpreting the content of any image. Author Del Sole begins by providing a succinct need to knowoverview of the service with descriptions. You then learn from hands-on demonstrations that show how basic C# code examples can be re-used across platforms. From there you will be guided through two different kinds of applications that interact with the service in two different ways: the more common means of calling a REST service to get back JSON data, and via the .NET libraries that Microsoft has been building to simplify the job (this latter one with Xamarin).רat Youll Learn Understand AIs role and how devices and applications use sophisticated algorithms to improve peoples lives and business tasks. Analyze images for Optical Character Recognition to detect written words and sentences Think about the next-generation applications in relation to your customersneeds Get up-to-speed on the latest version of the Computer Vision service, which now comes through Azure Set up an Azure subscription in order to access the Cognitive Services within the portal After reading this book, you will be able to get started with AI services from Microsoft in order to begin building powerful new apps for your company or customers.רo This Book Is Forĥvelopers just getting familiar with artificial intelligence. A minimal knowledge of C# is required.
- Published
- 2018
92. Mobile Big Data.
- Author
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Cheng, Xiang, Cui, Shuguang, Fang, Luoyang, and Yang, Liuqing
- Subjects
Artificial intelligence ,Computer communication systems ,Data mining ,Electrical engineering ,Computer Communication Networks ,Artificial Intelligence ,Communications Engineering, Networks ,Data Mining and Knowledge Discovery - Abstract
Summary: This book provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile big data, and the case studies based on real-world data recently collected by one of the largest mobile network carriers in China. In the first component, four areas of mobile big data life cycle are surveyed: data source and collection, transmission, computing platform and applications. In the second component, two case studies are provided, based on the signaling data collected in the cellular core network in terms of subscriber privacy evaluation and demand forecasting for network management. These cases respectively give a vivid demonstration of what mobile big data looks like, and how it can be analyzed and mined to generate useful and meaningful information and knowledge. This book targets researchers, practitioners and professors relevant to this field. Advanced-level students studying computer science and electrical engineering will also be interested in this book as supplemental reading.
- Published
- 2018
93. Nature-Inspired Computation in Data Mining and Machine Learning.
- Author
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Yang, Xin-She and He, Xing-Shi
- Subjects
Data mining ,Machine learning ,Natural computation ,Artificial intelligence - Abstract
Summary: This book reviews the latest developments in nature-inspired computation, with a focus on the cross-disciplinary applications in data mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their importance in both theory and practical applications. Adopting an application-focused approach, each chapter introduces a specific topic, with detailed descriptions of relevant algorithms, extensive literature reviews and implementation details. Covering topics such as nature-inspired algorithms, swarm intelligence, classification, clustering, feature selection, cybersecurity, learning algorithms over cloud, extreme learning machines, object categorization, particle swarm optimization, flower pollination and firefly algorithms, and neural networks, it also presents case studies and applications, including classifications of crisis-related tweets, extraction of named entities in the Tamil language, performance-based prediction of diseases, and healthcare services. This book is both a valuable a reference resource and a practical guide for students, researchers and professionals in computer science, data and management sciences, artificial intelligence and machine learning.
- Published
- 2018
94. Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI.
- Author
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Abellera, Rosendo and Bulusu, Lakshman
- Subjects
Artificial intelligence ,Computer programming ,Database management ,Open source software ,Artificial Intelligence ,Database Management ,Open Source - Abstract
Summary: Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps involved in utilizing the artificial intelligence components now available for a successful OBIEE implementation. Oracle Business Intelligence with Machine Learning covers various technologies including using Oracle OBIEE, R Enterprise, Spatial Maps, and machine learning for advanced visualization and analytics. The machine learning material focuses on learning representations of input data suitable for a given prediction problem. This book focuses on the practical aspects of implementing machine learning solutions using the rich Oracle BI ecosystem. The primary objective of this book is to bridge the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to machine learning with OBIEE. You will: See machine learning in OBIEE Master the fundamentals of machine learning and how it pertains to BI and advanced analytics Gain an introduction to Oracle R Enterprise Discover the practical considerations of implementing machine learning with OBIEE.
- Published
- 2018
95. Pairwise Comparison Matrices and their Fuzzy Extension : Multi-criteria Decision Making with a New Fuzzy Approach.
- Author
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Krejci, Jana
- Subjects
Artificial intelligence ,Computational intelligence ,Decision making ,Management science ,Operations research - Abstract
Summary: This book offers the first comprehensive and critical literature review of fuzzy pairwise comparison methods derived from methods originally developed for crisp pairwise comparison matrices. It proposes new fuzzy extensions of these methods and provides a detailed study of the differences and analogies between all the reviewed methods, as well as a detailed description of their drawbacks, with the help of many numerical examples. In order to prevent the drawbacks related to the reviewed fuzzy pairwise comparison methods, the book introduces constrained fuzzy arithmetic in fuzzy extension of the pairwise comparison methods. It proposes new fuzzy pairwise comparison methods based on constrained fuzzy arithmetic and critically compares them with the reviewed methods. It describes the application of the newly developed methods to incomplete large-dimensional pairwise comparison matrices showcased in a real-life case study. Written for researchers, graduate and PhD students interested in multi-criteria decision making methods based on both crisp and fuzzy pairwise comparison matrices, this self-contained book offers an overview of cutting-edge research and all necessary information to understand the described tools and use them in real-world applications.
- Published
- 2018
96. Patents and artificial intelligence : thinking computers.
- Author
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Dochniak, Michael J.
- Subjects
Artificial intelligence - Abstract
Summary: The best hope for peace and prosperity in our world is the expansion of information, and, as such, Artificial Intelligence (AI) was created to process an infinite amount of information. As men and women continue to perfect AI, monitoring its evolution can be both enlightening and unnerving. This book showcases the immense utility of AI and its superhuman characteristics. Without a doubt, patents play an important role in the remarkable progression of AI, exposing pioneering innovations that stimulate future improvements. From 1987 to 2017, at least one hundred fifty patents with the phrase artificial intelligence in the title were granted by the United States Patent and Trademark Office. This important book provides an easy-to-read summary of such patents. Within many of the summaries, there are inventor profiles and news articles that are insightful and thought-provoking. Pioneering inventors hail from China, Denmark, France, Germany, Italy, Japan, Korea, New Zealand, Russia, and Taiwan. Prominent organizations include Amazon, Disney, Ford, IBM, Intel, Microsoft, and Sony. Throughout the book, diverse quotes present the emotional impact of Artificial Intelligence. In reverence to Alan Mathison Turing (1912-1954), widely considered the father of AI, this book explores fascinating aspects of computing machinery that can process information to the nth power in a blink.
- Published
- 2018
97. Playing smart : on games, intelligence and Artificial Intelligence.
- Author
-
Togelius, Julian
- Subjects
Video games -- Psychological aspects ,Video games -- Design ,Intellect ,Thought and thinking ,Artificial intelligence - Abstract
Summary: Can games measure intelligence? How will artificial intelligence inform games of the future? In Playing Smart, Julian Togelius explores the connections between games and intelligence to offer a new vision of future games and game design. Video games already depend on AI. We use games to test AI algorithms, challenge our thinking, and better understand both natural and artificial intelligence. In the future, Togelius argues, game designers will be able to create smarter games that make us smarter in turn, applying advanced AI to help design games. In this book, he tells us how. Games are the past, present, and future of artificial intelligence. In 1948, Alan Turing, one of the founding fathers of computer science and artificial intelligence, handwrote a program for chess. Today we have IBM's Deep Blue and DeepMind's AlphaGo, and huge efforts go into developing AI that can play such arcade games as Pac-Man. Programmers continue to use games to test and develop AI, creating new benchmarks for AI while also challenging human assumptions and cognitive abilities. Game design is at heart a cognitive science, Togelius reminds us—when we play or design a game, we plan, think spatially, make predictions, move, and assess ourselves and our performance. By studying how we play and design games, Togelius writes, we can better understand how humans and machines think. AI can do more for game design than providing a skillful opponent. We can harness it to build game-playing and game-designing AI agents, enabling a new generation of AI-augmented games. With AI, we can explore new frontiers in learning and play.
- Published
- 2018
98. Practical Artificial Intelligence : Machine Learning, Bots, and Agent Solutions Using C#
- Author
-
Perez Castano, Arnaldo
- Subjects
Artificial intelligence ,Computer communication systems ,Artificial Intelligence ,Computer Communication Networks - Abstract
Summary: Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses.
- Published
- 2018
99. Quantum machine learning an applied approach : the theory and application of quantum machine learning in science and industr.
- Author
-
Ganguly, Santanu
- Subjects
Machine learning ,Quantum computing ,Machine learning--Industrial applications ,Artificial intelligence ,Data structures (Computer science) - Abstract
Summary: Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research. The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learning, where concepts of classical information technology meet the power of physics. Subsequent chapters follow a systematic deep dive into various quantum machine learning algorithms, quantum optimization, applications of advanced QML algorithms (quantum k-means, quantum k-medians, quantum neural networks, etc.), qubit state preparation for specific QML algorithms, inference, polynomial Hamiltonian simulation, and more, finishing with advanced and up-to-date research areas such as quantum walks, QML via Tensor Networks, and QBoost. Hands-on exercises from open source libraries regularly used today in industry and research are included, such as Qiskit, Rigetti's Forest, D-Wave's dOcean, Google's Cirq and brand new TensorFlow Quantum, and Xanadu's PennyLane, accompanied by guided implementation instructions. Wherever applicable, the book also shares various options of accessing quantum computing and machine learning ecosystems as may be relevant to specific algorithms. The book offers a hands-on approach to the field of QML using updated libraries and algorithms in this emerging field. You will benefit from the concrete examples and understanding of tools and concepts for building intelligent systems boosted by the quantum computing ecosystem. This work leverages the authors active research in the field and is accompanied by a constantly updated website for the book which provides all of the code examples. You will: Understand and explore quantum computing and quantum machine learning, and their application in science and industry Explore various data training models utilizing quantum machine learning algorithms and Python libraries Get hands-on and familiar with applied quantum computing, including freely available cloud-based access Be familiar with techniques for training and scaling quantum neural networks Gain insight into the application of practical code examples without needing to acquire excessive machine learning theory or take a quantum mechanics deep dive.
- Published
- 2018
100. Responsible artificial intelligence : how to develop and use AI in a responsible way.
- Author
-
Dignum, Virginia
- Subjects
Internet of things ,Microelectronics ,User interfaces (Computer systems) ,Electrical engineering ,Artificial Intelligence - Abstract
Summary: In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens.
- Published
- 2018
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