81 results on 'LN cat08778a'
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2. Machine learning.
- Author
-
Alpaydin, Ethem
- Subjects
Machine learning ,Artificial intelligence - Abstract
Summary: "An updated introduction for generalists to this powerful technology, its applications and possible future directions"-- Provided by publisher.
- Published
- 2021
3. The law of artificial intelligence.
- Author
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Hervey, Matt and Lavy, Matthew
- Subjects
Artificial intelligence ,Machine learning - Abstract
Summary: The Law of Artificial Intelligence is an essential practitioner's reference text examining how key areas of current civil and criminal law will apply to AI and examining emerging laws specific to the use of AI. It explains the fundamentals of AI technology, its development and terminology. The book also covers regulation, ethics and the use of AI within legal services and the administration of justice.
- Published
- 2021
4. Applied Machine Learning.
- Author
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Forsyth, David
- Subjects
Neural Networks ,Artificial intelligence ,Machine Learning ,Probability and statistics - Abstract
Summary: Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that it’s worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.
- Published
- 2021
5. Artificial Intelligence by Example : Acquire Advanced AI, Machine Learning, and Deep Learning Design Skills.
- Author
-
Rothman, Denis
- Subjects
Artificial intelligence ,Machine learning ,Google Translator ,Computer Algorithms - Abstract
Summary: Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.
- Published
- 2020
6. Artificial Intelligence and machine learning applications in civil, mechanical, and industrial engineering.
- Author
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Bekdas, Gebrail, Nigdeli, Sinan Melih, and Yucel, Melda
- Subjects
Artificial intelligence ,Civil engineering -- Data processing ,Machine learning ,Mechanical engineering -- Data processing ,Industrial engineering -- Data processing - Abstract
Summary: "This book examines the application of artificial intelligence and machine learning civil, mechanical, and industrial engineering"-- Provided by publisher.
- Published
- 2020
7. Reinforcement Learning. :Industrial Applications of Intelligent Agents.
- Author
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Winder, Phil
- Subjects
Reinforcement learning ,Machine learning ,Artificial intelligence ,Programming languages - Abstract
Summary: Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website
- Published
- 2020
8. AI and machine learning for coders. : a programmer's guide to artificial intelligence.
- Author
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Moroney, Laurence and Ng, Andrew
- Subjects
Machine Learning ,TensorFlow ,Artificial Intelligence ,Engineering - Abstract
Summary: All Indian Reprints of O'Reilly are printed in Grayscale. If you’re looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.
- Published
- 2020
9. Hands-on data science and Python machine learnin. perform data mining and machine learning efficiently using Python and Spark.
- Author
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Kane, Frank
- Subjects
- Machine learning, Python (Computer program language), Artificial intelligence, Data mining, Spark (Electronic resource : Apache Software Foundation)
- Abstract
Summary: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
- Published
- 2020
10. Digital Fluency: Understanding the Basics of Artificial Intelligence, Blockchain Technology, Quantum Computing, and Their Applications for Digital Transformation.
- Author
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Lang, Volker
- Subjects
Quantum computing ,Machine learning ,Artificial Intelligence - Published
- 2020
11. Artificial intelligence : a guide for thinking humans.
- Author
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Mitchell, Melanie
- Subjects
Artificial intelligence ,Machine learning ,COMPUTERS / Machine Theory - Abstract
Summary: No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. An award-winning author and leading computer scientist reveals its turbulent history and the recent surge of successes, grand hopes, and emerging fears that surround AI.
- Published
- 2019
12. Deep Learning for the Life Sciences : Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More.
- Author
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Ramsundar, Bharath, Eastman, Peter, Walters, Patrick, and Pande, Vijay
- Subjects
Life sciences -- Data processing ,Machine learning ,Artificial intelligence - Abstract
Summary: Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.
- Published
- 2019
13. Generative deep learning : teaching machines to paint, write, compose, and play.
- Author
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Foster, David (Data scientist)
- Subjects
Machine learning ,Artificial intelligence ,Neural networks (Computer science) ,Generative programming (Computer science) - Abstract
Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.
- Published
- 2019
14. Deep learning from scratch : building with Python from first principles.
- Author
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Weidman, Seth
- Subjects
Machine learning ,Neural networks (Computer science) ,Artificial intelligence - Abstract
Summary: With the resurgence of neural networks in the 2010s, understanding deep learning has become essential for machine learning practitioners and even many software engineers. This practical book provides a thorough introduction for data scientists and software engineers with previous exposure to machine learning. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks function using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a detailed understanding of how these networks work mathematically, computationally, and conceptually, you'll be set up for success on future deep learning projects.
- Published
- 2019
15. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : concepts, tools, and techniques to build intelligent systems.
- Author
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Géron, Aurélien
- Subjects
TensorFlow ,Python (Computer program language) ,Machine learning ,Artificial intelligence - Published
- 2019
16. Generative deep learning : teaching machines to paint, write, compose, and play.
- Author
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Foster, David
- Subjects
Machine learning ,Artificial intelligence ,Neural networks (Computer science) ,Generative programming (Computer science) - Abstract
Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.
- Published
- 2019
17. Generative deep learning : teaching machines to paint, write, compose, and play.
- Author
-
Foster, David (Data scientist)
- Subjects
Machine learning ,Artificial intelligence ,Neural networks (Computer science) ,Generative programming (Computer science) - Abstract
Summary: "Generative modeling is one of the hottest topics in AI. It's now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders, generative adversarial networks (GANs), encoder-decoder models, and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative."--Amazon.com.
- Published
- 2019
18. Deep learning.
- Author
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Kelleher, John D.
- Subjects
Machine learning ,Artificial intelligence - Abstract
Summary: "Artificial Intelligence is a disruptive technology across business and society. There are three long-term trends driving this AI revolution: the emergence of Big Data, the creation of cheaper and more powerful computers, and development of better algorithms for processing an learning from data. Deep learning is the subfield of Artificial Intelligence that focuses on creating large neural network models that are capable of making accurate data driven decisions. Modern neural networks are the most powerful computational models we have for analyzing massive and complex datasets, and consequently deep learning is ideally suited to take advantage of the rapid growth in Big Data and computational power. In the last ten years, deep learning has become the fundamental technology in computer vision systems, speech recognition on mobile phones, information retrieval systems, machine translation, game AI, and self-driving cars. It is set to have a massive impact in healthcare, finance, and smart cities over the next years. This book is designed to give an accessible and concise, but also comprehensive, introduction to the field of Deep Learning. The book explains what deep learning is, how the field has developed, what deep learning can do, and also discusses how the field is likely to develop in the next 10 years. Along the way, the most important neural network architectures are described, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and more recent developments such as Generative Adversarial Networks, transformer networks, and capsule networks. The book also covers the two more important algorithms for training a neural network, the gradient descent algorithm and Backpropagation"-- Provided by publisher.
- Published
- 2019
19. 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
20. TensorFlow machine learning cookbook : over 60 recipes to build intelligent machine learning systems with the power of Python.
- Author
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McClure, Nick
- Subjects
Machine learning ,Artificial intelligence ,Python ,Computer program language - Abstract
Summary: TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.
- Published
- 2018
21. 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
22. Quantum machine learning an applied approach : the theory and application of quantum machine learning in science and industr.
- Author
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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
23. 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
24. 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
25. 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
26. The deep learning revolution.
- Author
-
Sejnowski, Terrence J.
- Subjects
Machine learning ,Big data ,Artificial intelligence - Abstract
Summary: How deep learning-from Google Translate to driverless cars to personal cognitive assistants-is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
- Published
- 2018
27. 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
28. 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
29. 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
30. Learning systems : from theory to practice.
- Author
-
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
31. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
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Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks (Computer science) - Abstract
Summary: In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.
- Published
- 2017
32. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
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Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks ,Deep learning - Abstract
Summary: With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Understand the fundamentals of reinforcement learning.
- Published
- 2017
33. Machine Learning Paradigms : Artificial Immune Systems and their Applications in Software Personalization.
- Author
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Sotiropoulos, Dionisios N. and Tsihrintzis, George A.
- Subjects
Artificial intelligence ,Computational intelligence ,Machine Learning - Abstract
Summary: The topic of this monograph falls within the, so-called, biologically motivated computing paradigm, in which biology provides the source of models and inspiration towards the development of computational intelligence and machine learning systems. Specifically, artificial immune systems are presented as a valid metaphor towards the creation of abstract and high level representations of biological components or functions that lay the foundations for an alternative machine learning paradigm. Therefore, focus is given on addressing the primary problems of Pattern Recognition by developing Artificial Immune System-based machine learning algorithms for the problems of Clustering, Classification and One-Class Classification. Pattern Classification, in particular, is studied within the context of the Class Imbalance Problem. The main source of inspiration stems from the fact that the Adaptive Immune System constitutes one of the most sophisticated biological systems that is exceptionally evolved in order to continuously address an extremely unbalanced pattern classification problem, namely, the self / non-self discrimination process. The experimental results presented in this monograph involve a wide range of degenerate binary classification problems where the minority class of interest is to be recognized against the vast volume of the majority class of negative patterns. In this context, Artificial Immune Systems are utilized for the development of personalized software as the core mechanism behind the implementation of Recommender Systems. The book will be useful to researchers, practitioners and graduate students dealing with Pattern Recognition and Machine Learning and their applications in Personalized Software and Recommender Systems. It is intended for both the expert/researcher in these fields, as well as for the general reader in the field of Computational Intelligence and, more generally, Computer Science who wishes to learn more about the field of Intelligent Computing Systems and its applications. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.
- Published
- 2017
34. Machine ethics and robot ethics.
- Subjects
Robotics -- Moral and ethical aspects ,Machine learning ,Artificial intelligence - Abstract
Summary: Once the stuff of science fiction, recent progress in artificial intelligence, robotics, and machine learning means that these rapidly advancing technologies are finally coming into widespread use within everyday life. Such rapid development in these areas also brings with it a host of social, political and legal issues, as well as a rise in public concern and academic interest in the ethical challenges these new technologies pose. This volume is a collection of scholarly work from leading figures in the development of both robot ethics and machine ethics; it includes essays of historical significance which have become foundational for research in these two new areas of study, as well as important recent articles. The research articles selected focus on the control and governance of computational systems; the exploration of ethical and moral theories using software and robots as laboratories or simulations; inquiry into the necessary requirements for moral agency and the basis and boundaries of rights; and questions of how best to design systems that are both useful and morally sound. Collectively the articles ask what the practical ethical and legal issues, arising from the development of robots, will be over the next twenty years and how best to address these future considerations.
- Published
- 2017
35. Artificial Intelligence and Machine Learning and Marketing Management.
- Author
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Seligman, James
- Subjects
Artificial intelligence ,Machine Learning ,Cognitive functions - Abstract
Summary: OBJECTIVES The book objectives provide a full delivery of information on the fields of artificial intelligence (AI) and artificial intelligence (AI)to educators, students and practitioners of marketing. By explaining AI and ML terminology and its applications including marketing, the book is designed to inform and educate. Marketing use of AI and ML has exploded in recent decades as marketers have seen the considerable benefits of these two technologies. It is understood and explained that AI deals with 'Intelligent behaviour' by machines rather than natural intelligence found in humans and animals, it is the machine mimicking ' cognitive functions' that humans associate with the mind in learning, expression and problem solving and much more.
- Published
- 2016
36. Machine learning in medicine.
- Author
-
Cleophas, Ton J. M. and Zwinderman, Aeilko H.
- Subjects
Medicine -- Data processing ,Machine learning ,Artificial Intelligence ,Medical Informatics - Abstract
Summary: Machine learning is a novel discipline concerned with the analysis of large and multiple variables data. It involves computationally intensive methods, like factor analysis, cluster analysis, and discriminant analysis. It is currently mainly the domain of computer scientists, and is already commonly used in social sciences, marketing research, operational research and applied sciences. It is virtually unused in clinical research. This is probably due to the traditional belief of clinicians in clinical trials where multiple variables are equally balanced by the randomization process and are not further taken into account. In contrast, modern computer data files often involve hundreds of variables like genes and other laboratory values, and computationally intensive methods are required. This book was written as a hand-hold presentation accessible to clinicians, and as a must-read publication for those new to the methods
- Published
- 2013
37. Machine learning for computer vision.
- Author
-
Cipolla, Roberto, Battiato, Sebastiano, and Farinella Maria, Giovanni
- Subjects
Computer vision ,Machine Learning ,Artificial intelligence - Abstract
Summary: Computer vision is the science and technology of making machines that see. It is concerned with the theory, design and implementation of algorithms that can automatically process visual data to recognize objects, track and recover their shape and spatial layout. The International Computer Vision Summer School - ICVSS was established in 2007 to provide both an objective and clear overview and an in-depth analysis of the state-of-the-art research in Computer Vision. The courses are delivered by world renowned experts in the field, from both academia and industry, and cover both theoretical and practical aspects of real Computer Vision problems. The school is organized every year by University of Cambridge (Computer Vision and Robotics Group) and University of Catania (Image Processing Lab). Different topics are covered each year. A summary of the past Computer Vision Summer Schools can be found at: http://www.dmi.unict.it/icvss This edited volume contains a selection of articles covering some of the talks and tutorials held during the last editions of the school. The chapters provide an in-depth overview of challenging areas with key references to the existing literature.
- Published
- 2013
38. AI Meets BI : Artificial Intelligence and Business Intelligence.
- Author
-
Lakshman, Bulusu and Abellera, Rosendo
- Subjects
Artificial Intelligence ,Business Intelligence ,Machine learning ,Deep learning - Abstract
Summary: With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.
- Published
- 2013
39. Machine learning : a probabilistic perspective.
- Author
-
Murphy, Kevin P.
- Subjects
Machine learning ,Artificial Intelligence ,Semantics ,Probabilities - Abstract
Summary: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
- Published
- 2012
40. Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008 : proceedings.
- Author
-
Daelemans, Walter, Goethals, Bart, and Morik, Katharina
- Subjects
Data mining ,Machine learning ,Artificial intelligence - Abstract
Summary: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
- Published
- 2008
41. Learning classifier systems in data mining.
- Author
-
Bull, Larry, Bernadó-Mansilla, Ester, and Holmes, John
- Subjects
Data mining ,Learning classifier systems ,Machine learning ,Artificial intelligence ,Engineering mathematics - Abstract
Annotation Just over thirty years after Holland first presented the outline for Learning Classifier System paradigm, the ability of LCS to solve complex real-world problems is becoming clear. In particular, their capability for rule induction in data mining has sparked renewed interest in LCS. This book brings together work by a number of individuals who are demonstrating their good performance in a variety of domains. The first contribution is arranged as follows: Firstly, the main forms of LCS are described in some detail. A number of historical uses of LCS in data mining are then reviewed before an overview of the rest of the volume is presented. The rest of this book describes recent research on the use of LCS in the main areas of machine learning data mining: classification, clustering, time-series and numerical prediction, feature selection, ensembles, and knowledge discovery.
- Published
- 2008
42. Efficiency and Optimization of Buildings Energy Consumption: Volume II.
- Author
-
Orosa, José A. and Orosa, José A.
- Subjects
Research & information: general ,Physics ,ventilation ,energy ,COVID-19 ,procedure ,building ,ISO ,energy consumption ,building construction ,Passivhaus ,affordable housing ,neural network ,LSTM ,MLP ,thermal inertia ,building performance ,artificial intelligence ,artificial neural network ,demand-side management ,evolutionary computing ,non-intrusive appliance load monitoring ,parallel computing ,smart grid ,smart house ,cellular concrete ,lightweight materials ,thermal conductivity ,electricity ,dynamic simulation ,housing ,climate change ,weather controlled central system ,energy saving ,thermal improved of buildings ,new energy technologies ,sustainable buildings ,energy efficiency ,heat loss coefficient ,machine learning ,XGBoost ,SVR ,load disaggregation ,multi-scale ,attention mechanism ,residual network ,energy savings ,daylighting ,photovoltaic system ,EnergyPlus ,Homer PRO ,Net Zero Energy Buildings ,solar radiation ,support vector machine ,heuristic algorithm ,renewable energy ,solar energy systems ,n/a - Abstract
Summary: This reprint, as a continuation of a previous Special Issue entitled "Efficiency and Optimization of Buildings Energy Consumption", gives an up-to-date overview of new technologies based on Machine Learning (ML) and Internet of Things (IoT) procedures to improve the mathematical approach of algorithms that allow control systems to be improved with the aim of reducing housing sector energy consumption.
43. The Convergence of Human and Artificial Intelligence on Clinical Care - Part I.
- Author
-
Abedi, Vida and Abedi, Vida
- Subjects
Medicine ,machine learning-enabled decision support system ,improving diagnosis accuracy ,Bayesian network ,bariatric surgery ,health-related quality of life ,comorbidity ,voice change ,larynx cancer ,machine learning ,deep learning ,voice pathology classification ,imputation ,electronic health records ,EHR ,laboratory measures ,medical informatics ,inflammatory bowel disease ,C. difficile infection ,osteoarthritis ,complex diseases ,healthcare ,artificial intelligence ,interpretable machine learning ,explainable machine learning ,septic shock ,clinical decision support system ,electronic health record ,cerebrovascular disorders ,stroke ,SARS-CoV-2 ,COVID-19 ,cluster analysis ,risk factors ,ischemic stroke ,outcome prediction ,recurrent stroke ,cardiac ultrasound ,echocardiography ,portable ultrasound ,aneurysm surgery ,temporary artery occlusion ,clipping time ,artificial neural network ,digital imaging ,monocytes ,promonocytes and monoblasts ,chronic myelomonocytic leukemia (CMML) and acute myeloid leukemia (AML) for acute monoblastic leukemia and acute monocytic leukemia ,concordance between hematopathologists ,mechanical ventilation ,respiratory failure ,ADHD ,social media ,Twitter ,pharmacotherapy ,stimulants ,alpha-2-adrenergic agonists ,non-stimulants ,trust ,passive adherence ,human factors - Abstract
Summary: This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all.
44. AI AND THE SINGULARITY. A FALLACY OR A GREAT OPPORTUNITY?
- Author
-
Logan, Robert K., Braga, Adriana, and Logan, Robert K.
- Subjects
Information technology industries ,technological Singularity ,intelligence ,emotion ,artificial general intelligence ,artificial intelligence ,computer ,logic ,figure/ground ,computers ,consciousness ,singularity ,self ,futures ,technological singularity ,philosophy ,cosmic evolution ,anthropology ,technical singularity ,non-axiomatic reasoning system ,metasystem transitions ,patterns ,patterning ,cognition ,set theory ,language ,information ,abductive reasoning ,futurism and futurology ,hard science fiction ,models of consciousness ,intelligent machines ,machine replication ,machine evolution and optimization ,Turing test ,embodiment ,competition ,cooperation ,self-organization ,robots ,heterogeneity ,team sports ,artificial intelligence (AI) ,automated journalism ,robo-journalism ,writing algorithms ,future of news ,media ecology ,autogenous intelligence ,bootstrap fallacy ,recursive self-improvement ,self-modifying software ,superintelligence ,skepticism ,cyborg ,evolution ,love ,misinformation ,Technological Singularity ,Accelerated Change ,Artificial (General) Intelligence ,apophenia ,pareidolia ,complexity ,research focused social network ,networked minds ,complexity break ,complexity fallacy ,philosophy of information ,machine learning ,information quality ,information friction ,Artificial Intelligence (AI) ,Artificial General Intelligence (AGI) ,Artificial Social Intelligence (ASI) ,social sciences ,embodied cognition ,value alignment ,experience ,phenomenal consciousness ,access consciousness ,percept ,concept ,deep neural networks ,meaning ,understanding ,Singularity ,intuition ,wisdom - Abstract
Summary: "AI and the Technological Singularity: A Fallacy or a Great Opportunity" is a collection of essays that addresses the question of whether the technological singularity-the notion that AI-based computers can program the next generation of AI-based computers until a singularity is achieved, where an AI-based computer can exceed human intelligence-is a fallacy or a great opportunity. The group of scholars that address this question have a variety of positions on the singularity, ranging from advocates to skeptics. No conclusion can be reached, as the development of artificial intelligence is still in its infancy, and there is much wishful thinking and imagination in this issue rather than trustworthy data. The reader will find a cogent summary of the issues faced by researchers who are working to develop the field of artificial intelligence and, in particular, artificial general intelligence. The only conclusion that can be reached is that there exists a variety of well-argued positions as to where AI research is headed.
45. Machine Learning for Cyber Physical Systems. Selected papers from the International Conference ML4CPS 2020.
- Author
-
Beyerer, Jürgen, Maier, Alexander, Niggemann, Oliver, and Beyerer, Jürgen
- Subjects
Electrical engineering ,Communications engineering / telecommunications ,Computer networking & communications ,Cyber-physical systems, IoT ,Communications Engineering, Networks ,Computer Systems Organization and Communication Networks ,Cyber-Physical Systems ,Computer Engineering and Networks ,Machine Learning ,Artificial Intelligence ,Cognitive Robotics ,Internet of Things ,Computational intelligence ,Computer-based algorithms ,Smart grid ,Open Access ,Industry 4.0 ,Cybernetics & systems theory - Abstract
Summary: This open access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS - Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
46. Numerical and Evolutionary Optimization 2021.
- Author
-
Quiroz, Marcela, de la Fraga, Luis Gerardo, Lara, Adriana, Trujillo, Leonardo, Schütze, Oliver, and Quiroz, Marcela
- Subjects
Information technology industries ,Computer science ,distributor's pallet loading problem ,heuristics ,bin packing ,real-life instances ,emergency medical services ,emergency medicine ,decision-support system ,pre-hospital emergency care ,ambulance response time ,machine learning ,geo-indistinguishability ,differential privacy ,privacy-preserving machine learning ,input perturbation ,estimation of distribution algorithm ,Mallows model ,moth-flame algorithm ,job shop scheduling problem ,quay crane scheduling problem ,first-passage time ,Markov chain ,queueing theory ,simulation ,OR in health services ,KPI ,wind energy ,wind turbine blades ,erosion ,modal analysis ,aerodynamic analysis ,AutoML ,feature selection ,fault severity assessment ,gearboxes ,XGBoost classifiers ,autism ,attention ,ASD ,learning activities ,EEG ,BCI ,features ,artificial intelligence ,Grouping Genetic Algorithm ,variable decomposition ,Large-Scale Constrained Optimization ,DVT ,early diagnosis ,machine-learning ,smart system ,embedded system ,edge computing ,edge device ,OpenFOAM ,CFD ,ANFIS ,ANFIS (GA) ,ANFIS (PSO) ,ANFIS (FFA) ,nonlinear programming ,largest small polygons (LSP) ,{LSP(n)} model-class ,optimal area sequence {A(n)} ,revised LSP model ,mathematica model development environment ,IPOPT solver engine ,numerical optimization results and regression model for estimating {A(n)} ,n/a - Abstract
Summary: This reprint was established after the 9th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
47. Artificial Intelligence and Ambient Intelligence.
- Author
-
Gams, Matjaz, Gjoreski, Martin, and Gams, Matjaz
- Subjects
Information technology industries ,robotic hand ,control ,perception ,tactile sensing ,mechatronics ,grasping ,manipulation ,PUT-Hand ,underactuated ,multi-modal fusion ,machine learning ,robotics ,perception for grasping ,effective computing ,emotion system ,emotional machine ,agent ,human-machine interface ,Wi-Fi ,CSI ,crowd counting ,Doppler spectrum ,information society ,electronics ,artificial intelligence ,ambient intelligence ,one-dimensional depth sensor ,biometrics ,identification ,affective computing ,cognitive load ,psychophysiology ,supervised learning ,n/a - Abstract
Summary: This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on "Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules", presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots' ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science.
48. Industry 4.0 for SMEs - Smart Manufacturing and Logistics for SMEs.
- Author
-
Rauch, Erwin, Woschank, Manuel, and Rauch, Erwin
- Subjects
History of engineering & technology ,latent semantic analysis ,virtual quality management ,concept investigation ,concept disambiguation ,knowledge discovery ,sustainable methodologies ,small and medium sized enterprises ,material handling systems ,simulation ,ARENA®, time study ,overall equipment effectiveness ,manufacturing performance ,Industry 4.0 ,manufacturing sustainability ,manufacturing process model ,business process management ,hierarchical clustering ,similarity ,BPMN ,human factors ,cyber-physical systems ,cyber-physical production systems ,anthropocentric design ,Operator 4.0 ,human-machine interaction ,energy efficient operation ,manufacturing system ,stochastic event ,digital twin ,Max-plus Algebra ,MATLAB-Simulink ,advanced manufacturing ,industry 4.0 ,SME ,technology adoption model ,assembly supply chain ,sustainability ,complexity indicators ,testing criteria ,SMEs ,e-business modelling ,LSP Lifecycle Model ,Quality Function Deployment ,Best-Worst Method ,Internet of Things ,India ,awareness ,small and medium-sized enterprises ,assessment model ,collaborative robotics ,physical ergonomics ,human-robot collaboration ,human-centered design ,assembly ,small and medium sized enterprise ,positive complexity ,negative complexity ,infeasible configurations ,product platform ,customer's perception ,assessment ,field study ,smart manufacturing ,cloud platform ,artificial intelligence ,machine learning ,deep learning ,smart logistics ,logistics 4.0 ,smart technologies ,sustainable agriculture ,plant factory - Abstract
Summary: In recent years, the industrial environment has been changing radically due to the introduction of concepts and technologies based on the fourth industrial revolution, also known as Industry 4.0. After the introduction of Industry 4.0 in large enterprises, SMEs have moved into the focus, as they are the backbone of many economies. Small organizations are increasingly proactive in improving their operational processes, which is a good starting point for introducing the new concepts of Industry 4.0. The readiness of SME-adapted Industry 4.0 concepts and the organizational capability of SMEs to meet this challenge exist only in some areas. This reveals the need for further research and action plans for preparing SMEs in a technical and organizational direction. Therefore, special research and investigations are needed for the implementation of Industry 4.0 technologies and concepts in SMEs. SMEs will only achieve Industry 4.0 by following SME-customized implementation strategies and approaches and realizing SME-adapted concepts and technological solutions. Thus, this Special Issue represents a collection of theoretical models as well as practical case studies related to the introduction of Industry 4.0 concepts in small- and medium-sized enterprises.
49. Systems Engineering: Availability and Reliability.
- Author
-
Antosz, Katarzyna, Machado, Jose, Mazurkiewicz, Dariusz, Antonelli, Dario, Soares, Filomena, and Antosz, Katarzyna
- Subjects
Technology: general issues ,History of engineering & technology ,Quality check ,bike frame ,mathematical model ,graphical user interface ,risk management ,safety assurance ,medical parallel robot ,robotic assisted cancer treatment ,risk performance reasoning ,hidden Markov model ,Handy bauxite carrier ,process safety ,performance evaluation ,safety ,coupling correlation ,risk assessment ,multi-dimensional theory ,precision steel tape ,tape transportation ,roller-tape interactions ,roller-tape contact pair ,reliability ,truck unloading system ,petroleum equipment ,preventive maintenance ,cork-rubber composites ,compression ,apparent compression modulus ,Young's modulus ,bonded condition ,importance measure ,cost ,inventory systems ,air compression system ,nitrogen generation system ,utility module ,availability ,sensitivity analysis ,predictive maintenance ,Industry 4.0 ,Internet of Things ,artificial intelligence ,machine learning ,maintenance ,predictive scheduling ,flow shop ,job shop ,ant colony optimisation ,on-line monitoring ,collaborative robots ,human robot collaboration ,time between failure (TBF) ,common beta hypothesis (CBH) test ,meta-analysis ,level of heterogeneity ,mean time between failure (MTBF) ,text mining ,network-based distributed manufacturing systems ,moth flame optimization algorithm ,support vector machines ,Naive Bayes ,random forest ,decision trees ,supplier classification ,machining centre ,DSM ,Copula function ,fault propagation intensity ,fault propagation behaviour ,lubricity ,gear oil ,wear ,operational reliability ,n/a - Abstract
Summary: Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and the complexity of engineering systems, research and development in this area needs to focus on new solutions in the integration of intelligent machines or systems, with an emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated to that of the ICIE'2020 conference. This conference and journal's Special Issue is to present current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to reliability and risk assessment, innovations in maintenance strategies, production process scheduling, management and maintenance or systems analysis, simulation, design and modelling.
50. Industrial Applications: New Solutions for the New Era.
- Author
-
de Sales Guerra Tsuzuki, Marcos, Pessoa, Marcosiris Amorim de Oliveira, Acácio de Andrade, Alexandre, and de Sales Guerra Tsuzuki, Marcos
- Subjects
Technology: general issues ,History of engineering & technology ,induction machines ,electrical machines ,vector control ,SVPWM modulation ,frequency inverter ,artificial intelligence ,photovoltaics ,fault detection ,machine learning ,operation and maintenance ,renewable energy ,water-in-crude oil emulsion ,water content ,ultrasound ,propagation velocity ,exoskeletons ,test bench ,industry ,benchmarking ,microgrid model-based systems engineering ,service systems ,goal-oriented requirements engineering ,safety instrumented system ,ventricular assist device Bayesian network ,Petri net ,control strategy ,UAV ,fuzzy ,PID controller ,ROS ,Industry 4.0 ,database ,data models ,big data and analytics ,asset administration shell ,MLOps ,digital twin ,IoT ,prediction ,coordinate metrology ,optical scanning ,noise reduction ,digital manufacturing ,integrated inspection system ,data analytics ,uncertainty ,convolutional neural networks ,warehouse management ,image classification ,ensemble learning ,synthetic data ,depth image ,electrical maintenance ,COVID-19 ,thermography ,fever ,computer vision ,intelligent systems - Abstract
Summary: This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section.
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