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Predictive Analytics for the Modern Enterprise: A Practitioner's Guide To Designing And Implementing Solutions
by Nooruddin Abbas AliThe surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your businessUnderstand mathematical tools commonly used in predictive analyticsLearn the development frameworks used in predictive analytics applicationsAppreciate the role of predictive analytics in the machine learning processExamine industry implementations of predictive analyticsBuild, train, and retrain predictive models using Python and TensorFlow
Predictive Analytics im Controlling: Eine Untersuchung zur Anwendung in der Unternehmenspraxis (BestMasters)
by Zita KlingerDas vorliegende Buch zeigt, inwieweit Predictive Analytics (PA) im Controlling zur Forecast-Erstellung genutzt wird. Außerdem wird eine Handlungsempfehlung für die Implementierung eines PA Forecasts abgeleitet. Die Ergebnisse einer empirischen Untersuchung deuten darauf hin, dass PA bisher bei eher wenigen Unternehmen eingesetzt wird. Als Gründe hierfür werden insbesondere ein hoher Aufwand für den Aufbau von Know-how in Bezug auf PA und für die Implementierung in Bezug auf Zeit und Kosten von den Unternehmen angegeben. Die Ergebnisse zeigen, dass bei der Einführung eines PA Forecasts ein individuelles Vorgehen je Unternehmen erforderlich ist. Es können zwar die erforderlichen Prozessschritte und Best-Practice als Handlungsempfehlung definiert werden, allerdings ist bspw. die Auswahl der Position, für die der PA Forecast erstellt werden soll, von verschiedenen Faktoren abhängig, die von Unternehmen zu Unternehmen individuell geprüft werden müssen. Insgesamt hat sich gezeigt, dass durch den Einsatz von PA der Vorhersageprozess beschleunigt sowie die Vorhersagegenauigkeit erhöht werden kann. Die dadurch generierten Wettbewerbsvorteile für das jeweilige Unternehmen überwiegen in der Regel den erforderlichen Aufwand.
Predictive Analytics in Cloud, Fog, and Edge Computing: Perspectives and Practices of Blockchain, IoT, and 5G
by Bharadwaj Veeravalli Prasan Kumar Sahoo Hiren Kumar Thakkar Chinmaya Kumar DehuryThis book covers the relationship of recent technologies (such as Blockchain, IoT, and 5G) with the cloud computing as well as fog computing, and mobile edge computing. The relationship will not be limited to only architecture proposal, trends, and technical advancements. However, the book also explores the possibility of predictive analytics in cloud computing with respect to Blockchain, IoT, and 5G. The recent advancements in the internet-supported distributed computing i.e. cloud computing, has made it possible to process the bulk amount of data in a parallel and distributed. This has made it a lucrative technology to process the data generated from technologies such as Blockchain, IoT, and 5G. However, there are several issues a Cloud Service Provider (CSP) encounters, such as Blockchain security in cloud, IoT elasticity and scalability management in cloud, Service Level Agreement (SLA) compliances for 5G, Resource management, Load balancing, and Fault-tolerance. This edited book will discuss the aforementioned issues in connection with Blockchain, IoT, and 5G.Moreover, the book discusses how the cloud computing is not sufficient and one needs to use fog computing, and edge computing to efficiently process the data generated from IoT, and 5G. Moreover, the book shows how smart city, smart healthcare system, and smart communities are few of the most relevant IoT applications where fog computing plays a significant role. The book discusses the limitation of fog computing and the need for the edge computing to further reduce the network latency to process streaming data from IoT devices.The book also explores power of predictive analytics of Blockchain, IoT, and 5G data in cloud computing with its sister technologies. Since, the amount of resources increases day-by day, artificial intelligence (AI) tools are becoming more popular due to their capability which can be used in solving wide variety of issues, such as minimize the energy consumption of physical servers, optimize the service cost, improve the quality of experience, increase the service availability, efficiently handle the huge data flow, manages the large number of IoT devices, etc.
Predictive Analytics in Human Resource Management: A Hands-on Approach
by Shivinder Nijjer Sahil RajThis volume is a step-by-step guide to implementing predictive data analytics in human resource management (HRM). It demonstrates how to apply and predict various HR outcomes which have an organisational impact, to aid in strategising and better decision-making. The book: Presents key concepts and expands on the need and role of HR analytics in business management. Utilises popular analytical tools like artificial neural networks (ANNs) and K-nearest neighbour (KNN) to provide practical demonstrations through R scripts for predicting turnover and applicant screening. Discusses real-world corporate examples and employee data collected first-hand by the authors. Includes individual chapter exercises and case studies for students and teachers. Comprehensive and accessible, this guide will be useful for students, teachers, and researchers of data analytics, Big Data, human resource management, statistics, and economics. It will also be of interest to readers interested in learning more about statistics or programming.
Predictive Analytics of Psychological Disorders in Healthcare: Data Analytics on Psychological Disorders (Lecture Notes on Data Engineering and Communications Technologies #128)
by Mamta Mittal Lalit Mohan GoyalThis book discusses an interdisciplinary field which combines two major domains: healthcare and data analytics. It presents research studies by experts helping to fight discontent, distress, anxiety and unrealized potential by using mathematical models, machine learning, artificial intelligence, etc. and take preventive measures beforehand. Psychological disorders and biological abnormalities are significantly related with the applications of cognitive illnesses which has increased significantly in contemporary years and needs rapid investigation. The research content of this book is helpful for psychological undergraduates, health workers and their trainees, therapists, medical psychologists, and nurses.
Predictive Analytics und Data Mining: Eine Einführung mit R
by Marlis von der HudeDieses Buch bietet einen leicht verständlichen Einstieg in die Thematik des Data Minings und der Prädiktiven Analyseverfahren. Als Methodensammlung gedacht, bietet es zu jedem Verfahren zunächst eine kurze Darstellung der Theorie und erklärt die zum Verständnis notwendigen Formeln. Es folgt jeweils eine Illustration der Verfahren mit Hilfe von Beispielen, die mit dem Programmpaket R erarbeitet werden. Zum Abschluss wird eine einfache Möglichkeit präsentiert, mit der die Performancewerte verschiedener Verfahren mit statistischen Mitteln verglichen werden können. Zum Einsatz kommen hierbei geeignete Grafiken und Konfidenzintervalle.Das Buch verzichtet nicht auf Theorie, es präsentiert jedoch so wenig Theorie wie möglich, aber so viel wie nötig und ist somit optimal für Studium und Selbststudium geeignet.
Predictive Analytics with KNIME: Analytics for Citizen Data Scientists
by Frank AcitoThis book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool. The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.
Predictive Analytics with Microsoft Azure Machine Learning
by Roger Barga Valentine Fontama Wee Hyong TokPredictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What's New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration - a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace What you'll learn A structured introduction to Data Science and its best practices An introduction to the new Microsoft Azure Machine Learning service, explaining how to effectively build and deploy predictive models Practical skills such as how to solve typical predictive analytics problems like propensity modeling, churn analysis, product recommendation, and visualization with Power BI A practical way to sell your own predictive models on the Azure Marketplace Who this book is for Data Scientists, Business Analysts, BI Professionals and Developers who are interested in expanding their repertoire of skill applied to machine learning and predictive analytics, as well as anyone interested in an in-depth explanation of the Microsoft Azure Machine Learning service through practical tasks and concrete applications. The reader is assumed to have basic knowledge of statistics and data analysis, but not deep experience in data science or data mining. Advanced programming skills are not required, although some experience with R programming would prove very useful. Table of Contents Part 1: Introducing Data Science and Microsoft Azure Machine Learning 1. Introduction to Data Science 2. Introducing Microsoft Azure Machine Learning 3. Data Preparation 4. Integration with R 5. Integration with Python Part 2: Statistical and Machine Learning Algorithms 6. Introduction to Statistical and Machine Learning Algorithms Part 3: Practical applications 7. Building Customer Propensity Models 8. Visualizing Your Models with Power BI 9. Building Churn Models 10. Customer Segmentation Models 11. Building Predictive Maintenance Models 12. Recommendation Systems 13. Consuming and Publishing Models on Azure Marketplace 14. Cortana Analytics
Predictive Analytics with SAS and R: Core Concepts, Tools, and Implementation
by Pallavi Vijay Chavan Ramchandra S MangrulkarGain practical knowledge of application implementation using various programming approaches in predictive analytics. This book serves as a comprehensive guide for both beginners and professionals in the field of predictive analytics, offering core principles and practical insights without requiring an extensive mathematics or statistics background. The book starts with an introduction to analytics in decision making, protective analytics basics, and implementation in various industries. The book then takes you through types of regression, and simple linear regression in detail, followed by a demonstration of R Studio and SAS. Multiple Linear Regression is discussed next along with MLR model diagnostics. The book covers Multivariate Analysis and teaches you how to work with Principal Components Analysis, Factor Analysis, and much more. You also learn Time series Analysis with an understanding of Autoregressive Moving Average (ARMA) Models. After reading the book, you will be able to put predictive analytics principles into practice. What You Will Learn Understand modeling, estimating, and evaluating models for forecasting Implement Partial F-Test and Variable Selection Method Demonstrate each analysis model in R Studio and SAS Understand SLR and MLR Analysis models Who This Book Is For Students and professionals in the field of data analysis and intelligence applications
Predictive Analytics with TensorFlow
by Rezaul KarimAccomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow. About This Book A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book Understand deep learning and predictive analytics along with its challenges and best practices Who This Book Is For This book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need! What You Will Learn Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling Develop predictive models using classification, regression, and clustering algorithms Develop predictive models for NLP Learn how to use reinforcement learning for predictive analytics Factorization Machines for advanced recommendation systems Get a hands-on understanding of deep learning architectures for advanced predictive analytics Learn how to use deep Neural Networks for predictive analytics See how to use recurrent Neural Networks for predictive analytics Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysis In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence. This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling. The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system. The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis. Style and approach TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.
Predictive Analytics with TensorFlow: Implement deep learning principles to predict valuable insights using TensorFlow
by Md. Rezaul KarimAccomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow. About This Book • A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts • Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book • Understand deep learning and predictive analytics along with its challenges and best practices Who This Book Is For This book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need! What You Will Learn • Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling • Develop predictive models using classification, regression, and clustering algorithms • Develop predictive models for NLP • Learn how to use reinforcement learning for predictive analytics • Factorization Machines for advanced recommendation systems • Get a hands-on understanding of deep learning architectures for advanced predictive analytics • Learn how to use deep Neural Networks for predictive analytics • See how to use recurrent Neural Networks for predictive analytics • Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysis In Detail Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence. This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling. The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system. The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis. Style and approach TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.
Predictive Analytics, Data Mining and Big Data
by Steven FinlayThis in-depth guide provides managers with a solid understanding of data and data trends, the opportunities that it can offer to businesses, and the dangers of these technologies. Written in an accessible style, Steven Finlay provides a contextual roadmap for developing solutions that deliver benefits to organizations.
Predictive Analytics: Modeling and Optimization (Advanced Research in Reliability and System Assurance Engineering)
by Edited by Vijay Kumar and Mangey RamPredictive analytics refers to making predictions about the future based on different parameters which are historical data, machine learning, and artificial intelligence. This book provides the most recent advances in the field along with case studies and real-world examples. It discusses predictive modeling and analytics in reliability engineering and introduces current achievements and applications of artificial intelligence, data mining, and other techniques in supply chain management. It covers applications to reliability engineering practice, presents numerous examples to illustrate the theoretical results, and considers and analyses case studies and real-word examples. The book is written for researchers and practitioners in the field of system reliability, quality, supply chain management, and logistics management. Students taking courses in these areas will also find this book of interest.
Predictive Analytics: Parametric Models for Regression and Classification Using R (Wiley Series in Probability and Statistics)
by Ajit C. TamhaneProvides a foundation in classical parametric methods of regression and classification essential for pursuing advanced topics in predictive analytics and statistical learning This book covers a broad range of topics in parametric regression and classification including multiple regression, logistic regression (binary and multinomial), discriminant analysis, Bayesian classification, generalized linear models and Cox regression for survival data. The book also gives brief introductions to some modern computer-intensive methods such as classification and regression trees (CART), neural networks and support vector machines. The book is organized so that it can be used by both advanced undergraduate or masters students with applied interests and by doctoral students who also want to learn the underlying theory. This is done by devoting the main body of the text of each chapter with basic statistical methodology illustrated by real data examples. Derivations, proofs and extensions are relegated to the Technical Notes section of each chapter, Exercises are also divided into theoretical and applied. Answers to selected exercises are provided. A solution manual is available to instructors who adopt the text. Data sets of moderate to large sizes are used in examples and exercises. They come from a variety of disciplines including business (finance, marketing and sales), economics, education, engineering and sciences (biological, health, physical and social). All data sets are available at the book’s web site. Open source software R is used for all data analyses. R codes and outputs are provided for most examples. R codes are also available at the book’s web site. Predictive Analytics: Parametric Models for Regression and Classification Using R is ideal for a one-semester upper-level undergraduate and/or beginning level graduate course in regression for students in business, economics, finance, marketing, engineering, and computer science. It is also an excellent resource for practitioners in these fields.
Predictive Control for Spacecraft Rendezvous (SpringerBriefs in Applied Sciences and Technology)
by Afonso Botelho Baltazar Parreira Paulo N. Rosa João Miranda LemosThis brief addresses the design of model predictive control algorithms for performing space rendezvous manoeuvres. It consolidates developments within guidance and control algorithms, with the aim of improving the efficiency, safety, and autonomy of these manoeuvres. The brief presents several applications of model predictive control to rendezvous manoeuvres, including Ankersen zero-order-hold particular solution1, which provides a realistic thrust profile. It offers new approaches for rendezvous manoeuvres in elliptical orbits, formulating obstacle avoidance constraints, passive safety constraints, and robustness techniques. It also compares finite-horizon and variable-horizon formulations for model predictive control in the context of performance and computational complexity. Predictive Control for Spacecraft Rendezvous is accessible to academics and students new to the topics of orbital rendezvous and model predictive control, but also presents compelling subject matter for researchers and professionals in the aerospace industry.
Predictive Control: for Linear and Hybrid Systems
by Francesco Borrelli Alberto Bemporad Manfred MorariModel Predictive Control (MPC), the dominant advanced control approach in industry over the past twenty-five years, is presented comprehensively in this unique book. With a simple, unified approach, and with attention to real-time implementation, it covers predictive control theory including the stability, feasibility, and robustness of MPC controllers. The theory of explicit MPC, where the nonlinear optimal feedback controller can be calculated efficiently, is presented in the context of linear systems with linear constraints, switched linear systems, and, more generally, linear hybrid systems. Drawing upon years of practical experience and using numerous examples and illustrative applications, the authors discuss the techniques required to design predictive control laws, including algorithms for polyhedral manipulations, mathematical and multiparametric programming and how to validate the theoretical properties and to implement predictive control policies. The most important algorithms feature in an accompanying free online MATLAB toolbox, which allows easy access to sample solutions. Predictive Control for Linear and Hybrid Systems is an ideal reference for graduate, postgraduate and advanced control practitioners interested in theory and/or implementation aspects of predictive control.
Predictive Data Mining Models
by David L. Olson Desheng WuThis book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access. "
Predictive Data Mining Models (Computational Risk Management)
by David L. Olson Desheng WuThis book provides an overview of predictive methods demonstrated by open source software modeling with Rattle (R’) and WEKA. Knowledge management involves application of human knowledge (epistemology) with the technological advances of our current society (computer systems) and big data, both in terms of collecting data and in analyzing it. We see three types of analytic tools. Descriptive analytics focus on reports of what has happened. Predictive analytics extend statistical and/or artificial intelligence to provide forecasting capability. It also includes classification modeling. Prescriptive analytics applies quantitative models to optimize systems, or at least to identify improved systems. Data mining includes descriptive and predictive modeling. Operations research includes all three. This book focuses on prescriptive analytics.The book seeks to provide simple explanations and demonstration of some descriptive tools. This second edition provides more examples of big data impact, updates the content on visualization, clarifies some points, and expands coverage of association rules and cluster analysis. Chapter 1 gives an overview in the context of knowledge management. Chapter 2 discusses some basic data types. Chapter 3 covers fundamentals time series modeling tools, and Chapter 4 provides demonstration of multiple regression modeling. Chapter 5 demonstrates regression tree modeling. Chapter 6 presents autoregressive/integrated/moving average models, as well as GARCH models. Chapter 7 covers the set of data mining tools used in classification, to include special variants support vector machines, random forests, and boosting. Models are demonstrated using business related data. The style of the book is intended to be descriptive, seeking to explain how methods work, with some citations, but without deep scholarly reference. The data sets and software are all selected for widespread availability and access by any reader with computer links.
Predictive Data Modelling for Biomedical Data and Imaging (River Publishers Series in Biotechnology and Medical Research)
by Poonam Tanwar K. Kalaiselvi Haider Raza Seema Rawat Tapas KumarIn this book, we embark on a journey into the realm of predictive data modeling for biomedical data and imaging in healthcare. It explores the potential of predictive analytics in the field of medical science through utilizing various tools and techniques to unravel insights and enhance patient care. This volume creates a medium for an interchange of knowledge from expertise and concerns in the field of predictive data modeling. In detail, the research work on this will include the effective use of predictive data modeling algorithms to run image analysis tasks for understanding.Predictive Data Modelling for Biomedical Data and Imaging is divided into three sections, namely Section I – Beginning of Predictive Data Modeling for Biomedical Data and Imaging/Healthcare, Section II – Data Design and Analysis for Biomedical Data and Imaging/Healthcare, and Section III – Case Studies of Predictive Analytics for Biomedical Data and Imaging/Healthcare. We hope this book will inspire further research and innovation in the field of predictive data modeling for biomedical data and imaging in healthcare. By exploring diverse case studies and methodologies, this book contributes to the advancement of healthcare practices, ultimately improving patient outcomes and well-being.
Predictive Data Security using AI: Insights and Issues of Blockchain, IoT, and DevOps (Studies in Computational Intelligence #1065)
by Robin Singh Bhadoria Hiren Kumar Thakkar Mayank SwarnkarThis contributed volume consists of 11 chapters that specifically cover the security aspects of the latest technologies such as Blockchain, IoT, and DevOps, and how to effectively deal with them using Intelligent techniques. Moreover, machine learning (ML) and deep learning (DL) algorithms are also not secured and often manipulated by attackers for data stealing. This book also discusses the types of attacks and offers novel solutions to counter the attacks on ML and DL algorithms. This book describes the concepts and issues with figures and the supporting arguments with facts and charts. In addition to that, the book provides the comparison of different security solutions in terms of experimental results with tables and charts. Besides, the book also provides the future directions for each chapter and novel alternative approaches, wherever applicable. Often the existing literature provides domain-specific knowledge such as the description of security aspects. However, the readers find it difficult to understand how to tackle the application-specific security issues. This book takes one step forward and offers the security issues, current trends, and technologies supported by alternate solutions. Moreover, the book provides thorough guidance on the applicability of ML and DL algorithms to deal with application-specific security issues followed by novel approaches to counter threats to ML and DL algorithms. The book includes contributions from academicians, researchers, security experts, security architectures, and practitioners and provides an in-depth understanding of the mentioned issues.
Predictive Dialing Fundamentals: An Overview of Predictive Dialing Technologies, Their Applications, and Usage Today
by Aleksander Szlam Ken ThatcherWho should read this book? This is a must read if you're a newcomer to predictive dialers and responsible for analyzing, recommending and deploying inbound and outbound call center solutions; or, if you're in the midst of implementing a predictive dialer,
Predictive Intelligence for Data-Driven Managers: Process Model, Assessment-Tool, IT-Blueprint, Competence Model and Case Studies (Future of Business and Finance)
by Uwe SeebacherThis book describes how companies can easily and pragmatically set up and realize the path to a data-driven enterprise, especially in the marketing practice, without external support and additional investments. Using a predictive intelligence (PI) ecosystem, the book first introduces and explains the most important concepts and terminology. The PI maturity model then describes the phases in which you can build a PI ecosystem in your company. The book also demonstrates a PI self-test which helps managers identify the initial steps. In addition, a blueprint for a PI tech stack is defined for the first time, showing how IT can best support the topic. Finally, the PI competency model summarizes all elements into an action model for the company. The entire book is underpinned with practical examples, and case studies show how predictive intelligence, in the spirit of data-driven management, can be used profitably in the short, medium, and long terms.
Predictive Intelligence in Medicine: 4th International Workshop, PRIME 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (Lecture Notes in Computer Science #12928)
by Ehsan Adeli Islem Rekik Sang Hyun Park Julia SchnabelThis book constitutes the proceedings of the 4th International Workshop on Predictive Intelligence in Medicine, PRIME 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in October 2021.*The 25 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine. *The workshop was held virtually.
Predictive Intelligence in Medicine: 5th International Workshop, PRIME 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings (Lecture Notes in Computer Science #13564)
by Ehsan Adeli Islem Rekik Sang Hyun Park Celia CintasThis book constitutes the proceedings of the 5th International Workshop on Predictive Intelligence in Medicine, PRIME 2022, held in conjunction with MICCAI 2022 as a hybrid event in Singapore, in September 2022.The 19 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
Predictive Intelligence in Medicine: 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings (Lecture Notes in Computer Science #14277)
by Ehsan Adeli Islem Rekik Sang Hyun Park Ghada Zamzmi Celia CintasThis volume LNCS 14277 constitutes the refereed proceedings of the 6th International Workshop, PRIME 2023, Held in Conjunction with MICCAI 2023, in October 2023, held in Vancouver, BC, Canada. The 24 full papers presented were carefully reviewed and selected from 27 submissions. This workshop intersects ideas from both machine learning and mathematical/statistical/physical modeling research directions in the hope to provide a deeper understanding of the foundations of predictive intelligence developed for medicine, as well as to where we currently stand and what we aspire to achieve through this field.