Artificial Intelligence and Data Mining in Healthcare

Produk Detail:
  • Author : Malek Masmoudi
  • Publisher : Springer Nature
  • Pages : 195 pages
  • ISBN : 3030452409
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Artificial Intelligence and Data Mining in Healthcare

Download or Read online Artificial Intelligence and Data Mining in Healthcare full in PDF, ePub and kindle. this book written by Malek Masmoudi and published by Springer Nature which was released on 22 January 2022 with total page 195 pages. We cannot guarantee that Artificial Intelligence and Data Mining in Healthcare book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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.

Artificial Intelligence and Data Mining in Healthcare

Artificial Intelligence and Data Mining in Healthcare
  • Author : Malek Masmoudi,Bassem Jarboui,Patrick Siarry
  • Publisher : Springer Nature
  • Release : 22 January 2022
GET THIS BOOK Artificial Intelligence and Data Mining in Healthcare

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.

Artificial Intelligence in Data Mining

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

Machine Learning and Data Mining

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

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 BOOK Machine 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.

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 : 11 August 2021
GET THIS BOOK Artificial 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

Data Science

Data Science
  • Author : Richard Hurley
  • Publisher : Unknown
  • Release : 02 November 2019
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If you want to learn about data science and big data, then keep reading... Two manuscripts in one book: Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining This book will discuss everything that you need

Predictive Analytics

Predictive Analytics
  • Author : Dursun Delen
  • Publisher : FT Press Analytics
  • Release : 30 October 2020
GET THIS BOOK Predictive Analytics

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web

Data Mining Know It All

Data Mining  Know It All
  • Author : Soumen Chakrabarti,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten
  • Publisher : Morgan Kaufmann
  • Release : 31 October 2008
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This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer
  • Release : 08 July 2018
GET THIS BOOK Machine Learning and Data Mining in Pattern Recognition

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video

Data Mining for Business Applications

Data Mining for Business Applications
  • Author : Carlos A. Mota Soares,Rayid Ghani
  • Publisher : IOS Press
  • Release : 01 January 2010
GET THIS BOOK Data Mining for Business Applications

Data mining is already incorporated into the business processes in sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.

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 : 16 July 2001
GET THIS BOOK Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition, MLDM 2001, held in Leipzig, Germany in July 2001. The 26 revised full papers presented together with two invited papers were carefully reviewed and selected for inclusion in the proceedings. The papers are organized in topical sections on case-based reasoning and associative memory; rule induction and grammars; clustering and conceptual clustering; data mining on signals, images, and spatio-temporal data; nonlinear function learning and

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer
  • Release : 04 July 2017
GET THIS BOOK Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.

Big Data Data Mining and Machine Learning

Big Data  Data Mining  and Machine Learning
  • Author : Jared Dean
  • Publisher : John Wiley & Sons
  • Release : 27 May 2014
GET THIS BOOK Big Data Data Mining and Machine Learning

With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of

Data Preparation for Data Mining

Data Preparation for Data Mining
  • Author : Dorian Pyle
  • Publisher : Morgan Kaufmann
  • Release : 05 April 1999
GET THIS BOOK Data Preparation for Data Mining

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing. Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle