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Author: Niranjan N. Chiplunkar
Publisher: Springer
ISBN: 9789811535161
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.
Author: Niranjan N. Chiplunkar
Publisher: Springer
ISBN: 9789811535161
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.
Author: R. Geetha
Publisher: Springer Nature
ISBN: 3031730658
Category :
Languages : en
Pages : 343
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Book Description
Author: Anand Deshpande
Publisher: Packt Publishing Ltd
ISBN: 1788476018
Category : Computers
Languages : en
Pages : 371
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Book Description
Build next-generation Artificial Intelligence systems with Java Key Features 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 Book Description 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. 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 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.
Author: Pushparaj Shetty D.
Publisher: Springer Nature
ISBN: 9811633428
Category : Computers
Languages : en
Pages : 454
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Book Description
This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering.
Author: Byeong Ho Kang
Publisher: Springer
ISBN: 3319501275
Category : Computers
Languages : en
Pages : 731
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Book Description
This book constitutes the refereed proceedings of the 29th Australasian Joint Conference on Artificial Intelligence, AI 2016, held in Hobart, TAS, Australia, in December 2016. The 40 full papers and 18 short papers presented together with 8 invited short papers were carefully reviewed and selected from 121 submissions. The papers are organized in topical sections on agents and multiagent systems; AI applications and innovations; big data; constraint satisfaction, search and optimisation; knowledge representation and reasoning; machine learning and data mining; social intelligence; and text mining and NLP. The proceedings also contains 2 contributions of the AI 2016 doctoral consortium and 6 contributions of the SMA 2016.
Author: Abdulrahman Yarali
Publisher: John Wiley & Sons
ISBN: 1119685184
Category : Computers
Languages : en
Pages : 356
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Book Description
INTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, education, healthcare, logistics, entertainment, security, and manufacturing. Readers will also get access to: A thorough introduction to technology adoption and emerging trends in technology, including business trends and disruptive new applications Comprehensive explorations of telecommunications transformation and intelligent connectivity, including learning algorithms, machine learning, and deep learning Practical discussions of the Internet of Things, including its potential for disruption and future trends for technological development In-depth examinations of 5G wireless technology, including discussions of the first five generations of wireless tech Ideal for telecom and information technology managers, directors, and engineers, Intelligent Connectivity: AI, IoT, and 5G is also an indispensable resource for senior undergraduate and graduate students in telecom and computer science programs.
Author: R. Geetha
Publisher: Springer Nature
ISBN: 3031730682
Category :
Languages : en
Pages : 342
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Book Description
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Robbie Allen
Publisher: Addison-Wesley Professional
ISBN: 9780135226209
Category : Computers
Languages : en
Pages : 99998
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Book Description
Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage. But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects. Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully. You'll learn: What machine learning is, how it compares to "big data" and "artificial intelligence," and why it's suddenly so important What machine learning can do for you: solutions for computer vision, natural language processing, prediction, and more How to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new products Separating hype from reality: identifying pitfalls, limitations, and misconceptions upfront Knowing enough about the technology to work effectively with your technical team Getting the data right: sourcing, collection, governance, security, and culture Solving harder problems: exploring deep learning and other advanced techniques Understanding today's machine learning software and hardware ecosystem Evaluating potential projects, and addressing workforce concerns Staffing your project, acquiring the right tools, and building a workable project plan Interpreting results -- and building an organization that can increasingly learn from data Using machine learning responsibly and ethically Preparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications. For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way.
Author: Maria Virvou
Publisher: Springer
ISBN: 3030137430
Category : Technology & Engineering
Languages : en
Pages : 230
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Book Description
This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
