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Artificial Intelligence in Data Mining

Artificial Intelligence in Data Mining
  • Author : D. Binu
  • Publsiher : Academic Press
  • Release : 17 February 2021
  • ISBN : 0128206160
  • Pages : 270 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKArtificial 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 in Data Mining

Artificial Intelligence in Data Mining
  • Author : D. Binu,B.R. Rajakumar
  • Publisher : Academic Press
  • Release : 17 February 2021
GET THIS BOOKArtificial Intelligence in Data Mining

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

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Artificial Intelligence in Data Mining

Artificial Intelligence in Data Mining
  • Author : D. Binu,B.R. Rajakumar
  • Publisher : Academic Press
  • Release : 15 May 2021
GET THIS BOOKArtificial Intelligence in Data Mining

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

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Machine Learning and Data Mining

Machine Learning and Data Mining
  • Author : Igor Kononenko,Matjaz Kukar
  • Publisher : Horwood Publishing
  • Release : 14 May 2007
GET THIS BOOKMachine Learning and Data Mining

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

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Computational Intelligence in Data Mining

Computational Intelligence in Data Mining
  • Author : Giacomo Della Riccia,Rudolf Kruse,Hans-J. Lenz
  • Publisher : Springer
  • Release : 04 May 2014
GET THIS BOOKComputational Intelligence in Data Mining

The book aims to merge Computational Intelligence with Data Mining, which are both hot topics of current research and industrial development, Computational Intelligence, incorporates techniques like data fusion, uncertain reasoning, heuristic search, learning, and soft computing. Data Mining focuses on unscrambling unknown patterns or structures in very large data sets. Under the headline "Discovering Structures in Large Databases” the book starts with a unified view on ‘Data Mining and Statistics – A System Point of View’. Two special techniques follow: ‘Subgroup

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Machine Learning and Data Mining in Aerospace Technology

Machine Learning and Data Mining in Aerospace Technology
  • Author : Aboul Ella Hassanien,Ashraf Darwish,Hesham El-Askary
  • Publisher : Springer
  • Release : 02 July 2019
GET THIS BOOKMachine Learning and Data Mining in Aerospace Technology

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

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Artificial Intelligence and Data Mining Approaches in Security Frameworks

Artificial Intelligence and Data Mining Approaches in Security Frameworks
  • Author : Neeraj Bhargava,Ritu Bhargava,Pramod Singh Rathore,Rashmi Agrawal
  • Publisher : John Wiley & Sons
  • Release : 24 August 2021
GET THIS BOOKArtificial Intelligence and Data Mining Approaches in Security Frameworks

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

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Data Mining and Machine Learning in Cybersecurity

Data Mining and Machine Learning in Cybersecurity
  • Author : Sumeet Dua,Xian Du
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKData Mining and Machine Learning in Cybersecurity

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible

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Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer Science & Business Media
  • Release : 12 August 2011
GET THIS BOOKMachine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.

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Privacy and Security Issues in Data Mining and Machine Learning

Privacy and Security Issues in Data Mining and Machine Learning
  • Author : Christos Dimitrakakis,Aris Gkoulalas-Divanis,Aikaterini Mitrokotsa,Vassilios S. Verykios,Yücel Saygin
  • Publisher : Springer Science & Business Media
  • Release : 17 March 2011
GET THIS BOOKPrivacy and Security Issues in Data Mining and Machine Learning

This book constitutes the refereed proceedings of the International ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning, PSDML 2010, held in Barcelona, Spain, in September 2010. The 11 revised full papers presented were carefully reviewed and selected from 21 submissions. The papers range from data privacy to security applications, focusing on detecting malicious behavior in computer systems.

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Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning
  • Author : Xin-She Yang
  • Publisher : Academic Press
  • Release : 17 June 2019
GET THIS BOOKIntroduction to Algorithms for Data Mining and Machine Learning

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

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Statistics, Data Mining, and Machine Learning in Astronomy

Statistics, Data Mining, and Machine Learning in Astronomy
  • Author : Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray
  • Publisher : Princeton University Press
  • Release : 12 January 2014
GET THIS BOOKStatistics, Data Mining, and Machine Learning in Astronomy

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as

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Machine Learning and Data Mining

Machine Learning and Data Mining
  • Author : Igor Kononenko,Matjaz Kukar
  • Publisher : Elsevier
  • Release : 30 April 2007
GET THIS BOOKMachine Learning and Data Mining

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). 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. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in

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Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release : 29 March 2012
GET THIS BOOKAdvances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

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Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner,Atsushi Imiya
  • Publisher : Springer
  • Release : 25 August 2005
GET THIS BOOKMachine Learning and Data Mining in Pattern Recognition

We met again in front of the statue of Gottfried Wilhelm von Leibniz in the city of Leipzig. Leibniz, a famous son of Leipzig, planned automatic logical inference using symbolic computation, aimed to collate all human knowledge. Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition

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