Decision Intelligence Solutions
Download Decision Intelligence Solutions full books in PDF, epub, and Kindle. Read online free Decision Intelligence Solutions ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Nitasha Hasteer |
| Publisher | : Springer Nature |
| Total Pages | : 388 |
| Release | : 2024-01-15 |
| Genre | : Technology & Engineering |
| ISBN | : 9819959942 |
This book comprises the select peer-reviewed proceedings of the 3rd International Conference on Information Technology (InCITe-2023). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in decision intelligence, deep learning, machine learning, artificial intelligence, data science, and enabling technologies for IoT, blockchain, and other futuristic computational technologies. It covers various topics that span cutting-edge, collaborative technologies and areas of computation. The content would serve as a rich knowledge repository on information & communication technologies, neural networks, fuzzy systems, natural language processing, data mining & warehousing, big data analytics, cloud computing, security, social networks and intelligence, decision-making and modeling, information systems, and IT architectures. This book provides a valuable resource for those in academia and industry.
| Author | : Pamela Baker |
| Publisher | : John Wiley & Sons |
| Total Pages | : 323 |
| Release | : 2022-02-08 |
| Genre | : Computers |
| ISBN | : 1119824842 |
Learn to use, and not be used by, data to make more insightful decisions The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether? Decision Intelligence For Dummies pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible. In this timely book, you’ll learn to: Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company Perfect for business leaders in technology and finance, Decision Intelligence For Dummies is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.
| Author | : Sucar, L. Enrique |
| Publisher | : IGI Global |
| Total Pages | : 444 |
| Release | : 2011-10-31 |
| Genre | : Computers |
| ISBN | : 160960167X |
One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.
| Author | : Stacia Misner |
| Publisher | : Microsoft Press |
| Total Pages | : 196 |
| Release | : 2008-12-10 |
| Genre | : |
| ISBN | : 0735645833 |
“This readable, practical book helps business people quickly understand what business intelligence is, how it works, where it's used, and why and when to use it—all illustrated by real case studies, not just theory.” Nigel Pendse Author of The OLAP Report www.olapreport.com So much information, so little time. All too often, business data is hard to get at and use—thus slowing decision-making to a crawl. This insightful book illustrates how organizations can make better, faster decisions about their customers, partners, and operations by turning mountains of data into valuable business information that’s always at the fingertips of decision makers. You’ll learn what’s involved in using business intelligence to bring together information, people, and technology to create successful business strategies—and how to execute those strategies with confidence. Topics covered include: THE BUSINESS INTELLIGENCE MINDSET: Discover the basics behind business intelligence, such as how it’s defined, why and how to use it in your organization, and what characteristics, components, and general architecture most business intelligence solutions share. THE CASE FOR BUSINESS INTELLIGENCE: Read how world leaders in finance, manufacturing, and retail have successfully implemented business intelligence solutions and see what benefits they have reaped. THE PRACTICE OF BUSINESS INTELLIGENCE: Find out what’s involved in implementing a business intelligence solution in your organization, including how to identify your business intelligence opportunities, what decisions you must make to get a business intelligence project going, and what to do to sustain the momentum so that you can continue to make sense of all the data you gather.
| Author | : Ramesh Sharda |
| Publisher | : |
| Total Pages | : 832 |
| Release | : 2020-03-06 |
| Genre | : Business intelligence |
| ISBN | : 9781292341552 |
For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.
| Author | : Sergii Babichev |
| Publisher | : Springer Nature |
| Total Pages | : 805 |
| Release | : 2021-07-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3030820149 |
This book is devoted to current problems of artificial and computational intelligence including decision-making systems. Collecting, analysis, and processing information are the current directions of modern computer science. Development of new modern information and computer technologies for data analysis and processing in various fields of data mining and machine learning creates the conditions for increasing effectiveness of the information processing by both the decrease of time and the increase of accuracy of the data processing. The book contains of 54 science papers which include the results of research concerning the current directions in the fields of data mining, machine learning, and decision making. The papers are divided in terms of their topic into three sections. The first section "Analysis and Modeling of Complex Systems and Processes" contains of 26 papers, and the second section "Theoretical and Applied Aspects of Decision-Making Systems" contains of 13 papers. There are 15 papers in the third section "Computational Intelligence and Inductive Modeling". The book is focused to scientists and developers in the fields of data mining, machine learning and decision-making systems.
| Author | : Sebastian Thrun |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 346 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461555299 |
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.
| Author | : Vicki L. Sauter |
| Publisher | : John Wiley & Sons |
| Total Pages | : 482 |
| Release | : 2014-08-21 |
| Genre | : Business & Economics |
| ISBN | : 1118627237 |
Praise for the First Edition "This is the most usable decision support systems text. [i]t is far better than any other text in the field" —Computing Reviews Computer-based systems known as decision support systems (DSS) play a vital role in helping professionals across various fields of practice understand what information is needed, when it is needed, and in what form in order to make smart and valuable business decisions. Providing a unique combination of theory, applications, and technology, Decision Support Systems for Business Intelligence, Second Edition supplies readers with the hands-on approach that is needed to understand the implications of theory to DSS design as well as the skills needed to construct a DSS. This new edition reflects numerous advances in the field as well as the latest related technological developments. By addressing all topics on three levels—general theory, implications for DSS design, and code development—the author presents an integrated analysis of what every DSS designer needs to know. This Second Edition features: Expanded coverage of data mining with new examples Newly added discussion of business intelligence and transnational corporations Discussion of the increased capabilities of databases and the significant growth of user interfaces and models Emphasis on analytics to encourage DSS builders to utilize sufficient modeling support in their systems A thoroughly updated section on data warehousing including architecture, data adjustment, and data scrubbing Explanations and implications of DSS differences across cultures and the challenges associated with transnational systems Each chapter discusses various aspects of DSS that exist in real-world applications, and one main example of a DSS to facilitate car purchases is used throughout the entire book. Screenshots from JavaScript® and Adobe® ColdFusion are presented to demonstrate the use of popular software packages that carry out the discussed techniques, and a related Web site houses all of the book's figures along with demo versions of decision support packages, additional examples, and links to developments in the field. Decision Support Systems for Business Intelligence, Second Edition is an excellent book for courses on information systems, decision support systems, and data mining at the advanced undergraduate and graduate levels. It also serves as a practical reference for professionals working in the fields of business, statistics, engineering, and computer technology.
| Author | : Marko Kohtamäki |
| Publisher | : Springer |
| Total Pages | : 233 |
| Release | : 2017-07-05 |
| Genre | : Business & Economics |
| ISBN | : 3319548468 |
This book discusses and conceptualizes practices on real-time strategy, focusing on the interplay between strategy and business intelligence. Combining strategic practices and business intelligence systems, the authors demonstrate how managerial practices can be developed in the age of digitization. Also developing the concept of strategic agility, the book provides perspectives from a range of disciplines including strategic practices and decision making, customer relationship management, human resource management, competitive intelligence, supplier network management and business intelligence systems. Presenting managerial frameworks and guidelines, Real-time Strategy and Business Intelligence explores how to improve utilization of business intelligence systems in real-time decision making. Providing practical and future-oriented insights backed by examples and best practices, the authors present a clearly conceptualized theoretical framework.
| Author | : Adam Bohr |
| Publisher | : Academic Press |
| Total Pages | : 385 |
| Release | : 2020-06-21 |
| Genre | : Computers |
| ISBN | : 0128184396 |
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
