Machine Learning For Intelligent Decision Science
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Machine Learning For Intelligent Decision Science
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Author : Jitendra Kumar Rout
language : en
Publisher: Springer Nature
Release Date : 2020-04-02
Machine Learning For Intelligent Decision Science written by Jitendra Kumar Rout and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Technology & Engineering categories.
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
Progress In Intelligent Decision Science
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Author : Tofigh Allahviranloo
language : en
Publisher: Springer Nature
Release Date : 2021-01-29
Progress In Intelligent Decision Science written by Tofigh Allahviranloo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-29 with Technology & Engineering categories.
This book contains the topics of artificial intelligence and deep learning that do have much application in real-life problems. The concept of uncertainty has long been used in applied science, especially decision making and a logical decision must be made in the field of uncertainty or in the real-life environment that is formed and combined with vague concepts and data. The chapters of this book are connected to the new concepts and aspects of decision making with uncertainty. Besides, other chapters are involved with the concept of data mining and decision making under uncertain computations.
Intelligent Techniques For Data Science
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Author : Rajendra Akerkar
language : en
Publisher: Springer
Release Date : 2016-10-11
Intelligent Techniques For Data Science written by Rajendra Akerkar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-11 with Computers categories.
This textbook provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. These embrace the family of neural networks, fuzzy systems and evolutionary computing in addition to other fields within machine learning, and will help in identifying, visualizing, classifying and analyzing data to support business decisions./p> The authors, discuss advantages and drawbacks of different approaches, and present a sound foundation for the reader to design and implement data analytic solutions for real‐world applications in an intelligent manner. Intelligent Techniques for Data Science also provides real-world cases of extracting value from data in various domains such as retail, health, aviation, telecommunication and tourism.
Deep Learning Applications And Intelligent Decision Making In Engineering
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Author : Karthikrajan Senthilnathan
language : en
Publisher:
Release Date : 2020
Deep Learning Applications And Intelligent Decision Making In Engineering written by Karthikrajan Senthilnathan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computational intelligence categories.
"This book explores the application of deep learning in building a smart world, ranging from smart cities, smart agriculture to smart homes"--
Advances In Decision Science And Management
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Author : Taosheng Wang
language : en
Publisher: Springer Nature
Release Date : 2021-07-26
Advances In Decision Science And Management written by Taosheng Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-26 with Technology & Engineering categories.
This book discusses an emerging area in computer science, IT, and management, i.e., decision sciences and management. It includes studies that employ various computing techniques like machine learning to generate insights from huge amounts of available data; and which explore decision making for cross-platforms that contain heterogeneous data associated with complex assets; leadership; and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the Third International Conference on Decision Science and Management 2021 (ICDSM 2021), held at Hang Seng University of Hong Kong in China.
Applied Intelligent Decision Making In Machine Learning
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Author : Himansu Das
language : en
Publisher: CRC Press
Release Date : 2020-11-18
Applied Intelligent Decision Making In Machine Learning written by Himansu Das and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-18 with Computers categories.
The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.
Machine Learning Algorithms For Problem Solving In Computational Applications Intelligent Techniques
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Author : Kulkarni, Siddhivinayak
language : en
Publisher: IGI Global
Release Date : 2012-06-30
Machine Learning Algorithms For Problem Solving In Computational Applications Intelligent Techniques written by Kulkarni, Siddhivinayak and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-30 with Computers categories.
Machine learning is an emerging area of computer science that deals with the design and development of new algorithms based on various types of data. Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques addresses the complex realm of machine learning and its applications for solving various real-world problems in a variety of disciplines, such as manufacturing, business, information retrieval, and security. This premier reference source is essential for professors, researchers, and students in artificial intelligence as well as computer science and engineering.
Intelligent Decision Support Systems
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Author : Smaranda Belciug
language : en
Publisher:
Release Date : 2020
Intelligent Decision Support Systems written by Smaranda Belciug and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with POLITICAL SCIENCE categories.
The goal of this book is to provide, in a friendly and refreshing manner, both theoretical concepts and practical techniques for the important and exciting field of Artificial Intelligence that can be directly applied to real-world healthcare problems. Healthcare - the final frontier. Lately, it seems like Pandora opened the box and evil was released into the world. Fortunately, there was one thing left in the box: hope. In recent decades, hope has been increasingly represented by Intelligent Decision Support Systems. Their continuing mission: to explore strange new diseases, to seek out new treatments and drugs, and to intelligently manage healthcare resources and patients. Hence, this book is designed for all those who wish to learn how to explore, analyze and find new solutions for the most challenging domain of all time: healthcare. .
Artificial Intelligence Machine Learning And Data Science Technologies
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Author : Neeraj Mohan
language : en
Publisher: CRC Press
Release Date : 2021-10-11
Artificial Intelligence Machine Learning And Data Science Technologies written by Neeraj Mohan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-11 with Computers categories.
This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science.
Guide To Intelligent Data Science
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Author : Michael R. Berthold
language : en
Publisher: Springer
Release Date : 2020-08-15
Guide To Intelligent Data Science written by Michael R. Berthold and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-15 with Computers categories.
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.