Artificial Intelligence and Data Mining for Mergers and Acquisitions

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  • Author : Debasis Chanda
  • Publisher : CRC Press
  • Pages : 188 pages
  • ISBN : 0429755414
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Artificial Intelligence and Data Mining for Mergers and Acquisitions

Download or Read online Artificial Intelligence and Data Mining for Mergers and Acquisitions full in PDF, ePub and kindle. this book written by Debasis Chanda and published by CRC Press which was released on 18 March 2021 with total page 188 pages. We cannot guarantee that Artificial Intelligence and Data Mining for Mergers and Acquisitions 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. 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.

Artificial Intelligence and Data Mining for Mergers and Acquisitions

Artificial Intelligence and Data Mining for Mergers and Acquisitions
  • Author : Debasis Chanda
  • Publisher : CRC Press
  • Release : 18 March 2021
GET THIS BOOK Artificial Intelligence and Data Mining for Mergers and Acquisitions

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

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
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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

Machine Learning and Data Mining for Computer Security

Machine Learning and Data Mining for Computer Security
  • Author : Marcus A. Maloof
  • Publisher : Springer Science & Business Media
  • Release : 28 February 2006
GET THIS BOOK Machine Learning and Data Mining for Computer Security

"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security. The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of

Data Mining

Data Mining
  • Author : Ian H. Witten,Eibe Frank,Mark A. Hall,Christopher J. Pal
  • Publisher : Morgan Kaufmann
  • Release : 01 October 2016
GET THIS BOOK Data Mining

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates

Data Mining Practical Machine Learning Tools and Techniques

Data Mining  Practical Machine Learning Tools and Techniques
  • Author : Ian H. Witten,Eibe Frank,Mark A. Hall
  • Publisher : Elsevier
  • Release : 03 February 2011
GET THIS BOOK Data Mining Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect

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 BOOK Artificial 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

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 BOOK Advances 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, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book’s introductory part provides context to issues in the astronomical sciences that are also

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
  • Author : Petra Perner
  • Publisher : Springer
  • Release : 27 June 2016
GET THIS BOOK Machine Learning and Data Mining in Pattern Recognition

This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.

Mathematical Analysis for Machine Learning and Data Mining

Mathematical Analysis for Machine Learning and Data Mining
  • Author : Simovici Dan A
  • Publisher : World Scientific
  • Release : 21 May 2018
GET THIS BOOK Mathematical Analysis for Machine Learning and Data Mining

This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit

Textbook of Machine Learning and Data Mining

Textbook of Machine Learning and Data Mining
  • Author : Hiroshi Mamitsuka
  • Publisher : Unknown
  • Release : 12 September 2018
GET THIS BOOK Textbook of Machine Learning and Data Mining

Data-driven approaches, particularly machine learning and data mining, are the main driving force of the current artificial intelligence technology. This book covers a wide variety of methods in machine learning and data mining, dividing them from a viewpoint of data types, which begin with rather simple vectors and end by graphs and also combination of different data types. This book describes standard techniques of machine learning and data mining for each data type, especially focusing on the relevance and difference

Machine Learning for Data Mining

Machine Learning for Data Mining
  • Author : Jesus Salcedo
  • Publisher : Packt Publishing Ltd
  • Release : 30 April 2019
GET THIS BOOK Machine Learning for Data Mining

Get efficient in performing data mining and machine learning using IBM SPSS Modeler Key Features Learn how to apply machine learning techniques in the field of data science Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results A step-by-step approach to improving model development and performance Book Description Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with

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