Machine Learning for Intelligent Decision Science Book

Machine Learning for Intelligent Decision Science


  • Author : Jitendra Kumar Rout
  • Publisher : Springer Nature
  • Release Date : 2020-04-02
  • Genre: Technology & Engineering
  • Pages : 209
  • ISBN 10 : 9789811536892

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Machine Learning for Intelligent Decision Science Excerpt :

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.

Artificial Intelligence  Machine Learning  and Data Science Technologies Book

Artificial Intelligence Machine Learning and Data Science Technologies


  • Author : Neeraj Mohan
  • Publisher : CRC Press
  • Release Date : 2021-10-12
  • Genre: Technology & Engineering
  • Pages : 310
  • ISBN 10 : 9781000460544

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Artificial Intelligence Machine Learning and Data Science Technologies Excerpt :

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.

Progress in Intelligent Decision Science Book

Progress in Intelligent Decision Science


  • Author : Tofigh Allahviranloo
  • Publisher : Springer Nature
  • Release Date : 2021-01-29
  • Genre: Technology & Engineering
  • Pages : 989
  • ISBN 10 : 9783030665012

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Progress in Intelligent Decision Science Excerpt :

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.

Applied Intelligent Decision Making in Machine Learning Book

Applied Intelligent Decision Making in Machine Learning


  • Author : Himansu Das
  • Publisher : CRC Press
  • Release Date : 2020-11-18
  • Genre: Computers
  • Pages : 252
  • ISBN 10 : 9781000208542

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Applied Intelligent Decision Making in Machine Learning Excerpt :

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.

Deep Learning Applications and Intelligent Decision Making in Engineering Book

Deep Learning Applications and Intelligent Decision Making in Engineering


  • Author : Senthilnathan, Karthikrajan
  • Publisher : IGI Global
  • Release Date : 2020-10-23
  • Genre: Technology & Engineering
  • Pages : 332
  • ISBN 10 : 9781799821106

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Deep Learning Applications and Intelligent Decision Making in Engineering Excerpt :

Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.

Intelligent Healthcare Book

Intelligent Healthcare


  • Author : Surbhi Bhatia
  • Publisher : Springer Nature
  • Release Date : 2021-07-02
  • Genre: Technology & Engineering
  • Pages : 323
  • ISBN 10 : 9783030670511

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Intelligent Healthcare Excerpt :

This book fosters a scientific debate for sophisticated approaches and cognitive technologies (such as deep learning, machine learning and advanced analytics) for enhanced healthcare services in light of the tremendous scope in the future of intelligent systems for healthcare. The authors discuss the proliferation of huge data sources (e.g. genomes, electronic health records (EHRs), mobile diagnostics, and wearable devices) and breakthroughs in artificial intelligence applications, which have unlocked the doors for diagnosing and treating multitudes of rare diseases. The contributors show how the widespread adoption of intelligent health based systems could help overcome challenges, such as shortages of staff and supplies, accessibility barriers, lack of awareness on certain health issues, identification of patient needs, and early detection and diagnosis of illnesses. This book is a small yet significant step towards exploring recent advances, disseminating state-of-the-art techniques and deploying novel technologies in intelligent healthcare services and applications. Describes the advances of computing methodologies for life and medical science data; Presents applications of artificial intelligence in healthcare along with case studies and datasets; Provides an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

Advances in Decision Science and Management Book
Score: 5
From 1 Ratings

Advances in Decision Science and Management


  • Author : Taosheng Wang
  • Publisher : Springer Nature
  • Release Date : 2021-07-26
  • Genre: Technology & Engineering
  • Pages : 707
  • ISBN 10 : 9789811625022

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Advances in Decision Science and Management Excerpt :

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.

Intelligent Decision making Support Systems Book

Intelligent Decision making Support Systems


  • Author : Jatinder N.D. Gupta
  • Publisher : Springer Science & Business Media
  • Release Date : 2007-03-30
  • Genre: Technology & Engineering
  • Pages : 503
  • ISBN 10 : 9781846282317

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Intelligent Decision making Support Systems Excerpt :

This book will be bought by researchers and graduates students in Artificial Intelligence and management as well as practising managers and consultants interested in the application of IT and information systems in real business environment.

Intelligent Decision Support Systems Book

Intelligent Decision Support Systems


  • Author : Miquel Sànchez-Marrè
  • Publisher : Springer Nature
  • Release Date : 2022-03-28
  • Genre: Technology & Engineering
  • Pages : 806
  • ISBN 10 : 9783030877903

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Intelligent Decision Support Systems Excerpt :

This book presents the potential use and implementation of intelligent techniques in decision making processes involved in organizations and companies. It provides a thorough analysis of decisions, reviewing the classical decision theory, and describing usual methods for modeling the decision process. It describes the chronological evolution of Decision Support Systems (DSS) from early Management Information Systems until the appearance of Intelligent Decision Support Systems (IDSS). It explains the most commonly used intelligent techniques, both data-driven and model-driven, and illustrates the use of knowledge models in Decision Support through case studies. The author pays special attention to the whole Data Science process, which provides intelligent data-driven models in IDSS. The book describes main uncertainty models used in Artificial Intelligence to model inexactness; covers recommender systems; and reviews available development tools for inducing data-driven models, for using model-driven methods and for aiding the development of Intelligent Decision Support Systems

Intelligent Decision Technologies 2019 Book

Intelligent Decision Technologies 2019


  • Author : Ireneusz Czarnowski
  • Publisher : Springer
  • Release Date : 2019-07-16
  • Genre: Technology & Engineering
  • Pages : 354
  • ISBN 10 : 9789811383113

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Intelligent Decision Technologies 2019 Excerpt :

The book presents a collection of peer-reviewed articles from the 11th KES International Conference on Intelligent Decision Technologies (KES-IDT-19), held Malta on 17–19 June 2019. The conference provided opportunities for the presentation of new research results and discussion about them. It was also an opportunity to generation of new ideas in the field of intelligent decision making. The range of topics explored is wide, and covers methods of classification, prediction, data analysis, decision support, modelling and many more in such areas as finance, cybersecurity, economy, health, management and transportation. The topics cover also problems of data science, signal processing and knowledge engineering.

Intelligent Decision Technologies Book

Intelligent Decision Technologies


  • Author : Ireneusz Czarnowski
  • Publisher : Springer Nature
  • Release Date : 2021-07-07
  • Genre: Technology & Engineering
  • Pages : 671
  • ISBN 10 : 9789811627651

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Intelligent Decision Technologies Excerpt :

This book contains selected papers from the KES-IDT-2021 conference, being held as a virtual conference in June 14–16, 2021. The KES-IDT is an interdisciplinary conference with opportunities for the presentation of new research results and discussion about them under the common title "Intelligent Decision Technologies". The conference has been creating for years a platform for knowledge transfer and the generation of new ideas in the field of intelligent decision making. The range of topics discussed during the conference covered methods of classification, prediction, data analysis, big data, decision support, knowledge engineering, modeling, social networks and many more in areas such as finance, economy, management and transportation. The discussed topics covered also decision making for problems regarding the electric vehicle industry. The book contains also several sections devoted to specific topics, such as Advances in intelligent data processing and its applications Multi-criteria decision analysis methods Knowledge engineering in large-scale systems High-dimensional data analysis Spatial data analysis and sparse estimation Innovative technologies and applications in computer intelligence Intelligent diagnosis and monitoring of systems Decision making theory for economics.

Machine Learning for Decision Sciences with Case Studies in Python Book

Machine Learning for Decision Sciences with Case Studies in Python


  • Author : S. Sumathi
  • Publisher : CRC Press
  • Release Date : 2022-07-06
  • Genre: Mathematics
  • Pages : 476
  • ISBN 10 : 9781000590937

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Machine Learning for Decision Sciences with Case Studies in Python Excerpt :

This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.

Intelligent Decision Technology Support in Practice Book

Intelligent Decision Technology Support in Practice


  • Author : Jeffrey W. Tweedale
  • Publisher : Springer
  • Release Date : 2015-08-22
  • Genre: Technology & Engineering
  • Pages : 261
  • ISBN 10 : 9783319212098

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Intelligent Decision Technology Support in Practice Excerpt :

This book contains a collection of innovative chapters emanating from topics raised during the 5th KES International Conference on Intelligent Decision Technologies (IDT), held during 2013 at Sesimbra, Portugal. The authors were invited to expand their original papers into a plethora of innovative chapters espousing IDT methodologies and applications. This book documents leading-edge contributions, representing advances in Knowledge-Based and Intelligent Information and Engineering System. It acknowledges that researchers recognize that society is familiar with modern Advanced Information Processing and increasingly expect richer IDT systems. Each chapter concentrates on the theory, design, development, implementation, testing or evaluation of IDT techniques or applications. Anyone that wants to work with IDT or simply process knowledge should consider reading one or more chapters and focus on their technique of choice. Most readers will benefit from reading additional chapters to access alternative technique that often represent alternative approaches. This book is suitable for anyone interested in or already working with IDT or Intelligent Decision Support Systems. It is also suitable for students and researchers seeking to learn more about modern Artificial Intelligence and Computational Intelligence techniques that support decision-making in modern computer systems.

Data Science Book

Data Science


  • Author : Vijay Kotu
  • Publisher : Morgan Kaufmann
  • Release Date : 2018-11-27
  • Genre: Computers
  • Pages : 568
  • ISBN 10 : 9780128147627

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Data Science Excerpt :

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner