Artificial Intelligence in Data Mining Book

Artificial Intelligence in Data Mining


  • Author : D. Binu
  • Publisher : Academic Press
  • Release Date : 2021-02-17
  • Genre: Science
  • Pages : 270
  • ISBN 10 : 9780128206164
  • Total Read : 60
  • File Size : 20,9 Mb

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Artificial Intelligence in Data Mining Summary:

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area. Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense

Artificial Intelligence and Data Mining Approaches in Security Frameworks Book

Artificial Intelligence and Data Mining Approaches in Security Frameworks


  • Author : Neeraj Bhargava
  • Publisher : John Wiley & Sons
  • Release Date : 2021-08-24
  • Genre: Technology & Engineering
  • Pages : 322
  • ISBN 10 : 9781119760405
  • Total Read : 67
  • File Size : 6,5 Mb

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Artificial Intelligence and Data Mining Approaches in Security Frameworks Summary:

ARTIFICIAL INTELLIGENCE AND DATA MINING IN SECURITY FRAMEWORKS Written and edited by a team of experts in the field, this outstanding new volume offers solutions to the problems of security, outlining the concepts behind allowing computers to learn from experience and understand the world in terms of a hierarchy of concepts, with each concept defined through its relation to simpler concepts. Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to artificial intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalized security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and data mining and several other computing technologies to deploy such a system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new v

Machine Learning and Data Mining Book
Score: 3
From 1 Ratings

Machine Learning and Data Mining


  • Author : Igor Kononenko
  • Publisher : Horwood Publishing
  • Release Date : 2007-04-30
  • Genre: Computers
  • Pages : 484
  • ISBN 10 : 1904275214
  • Total Read : 78
  • File Size : 17,5 Mb

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Machine Learning and Data Mining Summary:

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

Artificial Intelligence and Data Mining in Healthcare Book

Artificial Intelligence and Data Mining in Healthcare


  • Author : Malek Masmoudi
  • Publisher : Springer Nature
  • Release Date : 2021
  • Genre: Artificial intelligence
  • Pages : 195
  • ISBN 10 : 9783030452407
  • Total Read : 60
  • File Size : 19,8 Mb

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Artificial Intelligence and Data Mining in Healthcare Summary:

This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

Machine Learning and Data Mining in Aerospace Technology Book

Machine Learning and Data Mining in Aerospace Technology


  • Author : Aboul Ella Hassanien
  • Publisher : Springer
  • Release Date : 2019-07-02
  • Genre: Technology & Engineering
  • Pages : 232
  • ISBN 10 : 9783030202125
  • Total Read : 87
  • File Size : 9,8 Mb

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Machine Learning and Data Mining in Aerospace Technology Summary:

This book explores the main concepts, algorithms, and techniques of Machine Learning and data mining for aerospace technology. Satellites are the ‘eagle eyes’ that allow us to view massive areas of the Earth simultaneously, and can gather more data, more quickly, than tools on the ground. Consequently, the development of intelligent health monitoring systems for artificial satellites – which can determine satellites’ current status and predict their failure based on telemetry data – is one of the most important current issues in aerospace engineering. This book is divided into three parts, the first of which discusses central problems in the health monitoring of artificial satellites, including tensor-based anomaly detection for satellite telemetry data and machine learning in satellite monitoring, as well as the design, implementation, and validation of satellite simulators. The second part addresses telemetry data analytics and mining problems, while the last part focuses on security issues in telemetry data.

Artificial Intelligence and Data Mining for Mergers and Acquisitions Book

Artificial Intelligence and Data Mining for Mergers and Acquisitions


  • Author : Debasis Chanda
  • Publisher : CRC Press
  • Release Date : 2021-03-18
  • Genre: Business & Economics
  • Pages : 263
  • ISBN 10 : 9780429755408
  • Total Read : 72
  • File Size : 9,5 Mb

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Artificial Intelligence and Data Mining for Mergers and Acquisitions Summary:

The goal of this book is to present a modeling framework for the Virtual Organization that is focused on process composition. This framework uses Predicate Calculus Knowledge Bases. Petri Net-based modeling is also discussed. In this context, a Data Mining model is proposed, using a fuzzy mathematical approach, aiming to discover knowledge. A Knowledge-Based framework has been proposed in order to present an all-inclusive knowledge store for static and dynamic properties. Toward this direction, a Knowledge Base is created, and inferences are arrived at. This book features an advisory tool for Mergers and Acquisitions of Organizations using the Fuzzy Data Mining Framework and highlights the novelty of a Knowledge-Based Service-Oriented Architecture approach and development of an Enterprise Architectural model using AI that serves a wide audience. Students of Strategic Management in business schools and postgraduate programs in technology institutes seeking application areas of AI and Data Mining, as well as business/technology professionals in organizations aiming to create value through Mergers and Acquisitions and elsewhere, will benefit from the reading of this book.

Introduction to Algorithms for Data Mining and Machine Learning Book

Introduction to Algorithms for Data Mining and Machine Learning


  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release Date : 2019-06-17
  • Genre: Mathematics
  • Pages : 188
  • ISBN 10 : 9780128172179
  • Total Read : 74
  • File Size : 16,6 Mb

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Introduction to Algorithms for Data Mining and Machine Learning Summary:

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Encyclopedia of Machine Learning Book

Encyclopedia of Machine Learning


  • Author : Claude Sammut
  • Publisher : Springer Science & Business Media
  • Release Date : 2011-03-28
  • Genre: Computers
  • Pages : 1061
  • ISBN 10 : 9780387307688
  • Total Read : 68
  • File Size : 17,6 Mb

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Encyclopedia of Machine Learning Summary:

This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.