Predictive Modeling And Risk Assessment
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Predictive Modeling And Risk Assessment
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Author : Rui Costa
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-12-02
Predictive Modeling And Risk Assessment written by Rui Costa and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-12-02 with Technology & Engineering categories.
The single most important task of food scientists and the food industry as a whole is to ensure the safety of foods supplied to consumers. Recent trends in global food production, distribution and preparation call for increased emphasis on hygienic practices at all levels and for increased research in food safety in order to ensure a safer global food supply. The ISEKI-Food book series is a collection of books where various aspects of food safety and environmental issues are introduced and reviewed by scientists specializing in the field. In all of the books a special emp- sis was placed on including case studies applicable to each specific topic. The books are intended for graduate students and senior level undergraduate students as well as professionals and researchers interested in food safety and environmental issues applicable to food safety. The idea and planning of the books originates from two working groups in the European thematic network “ISEKI-Food” an acronym for “Integrating Safety and Environmental Knowledge In to Food Studies”. Participants in the ISEKI-Food network come from 29 countries in Europe and most of the institutes and univer- ties involved with Food Science education at the university level are represented. Some international companies and non teaching institutions have also participated in the program. The ISEKI-Food network is coordinated by Professor Cristina Silva at The Catholic University of Portugal, College of Biotechnology (Escola) in Porto. The program has a web site at: http://www. esb. ucp. pt/iseki/.
Healthcare Risk Adjustment And Predictive Modeling
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Author : Ian G. Duncan
language : en
Publisher: ACTEX Publications
Release Date : 2011
Healthcare Risk Adjustment And Predictive Modeling written by Ian G. Duncan and has been published by ACTEX Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Business & Economics categories.
This text is listed on the Course of Reading for SOA Fellowship study in the Group & Health specialty track. Healthcare Risk Adjustment and Predictive Modeling provides a comprehensive guide to healthcare actuaries and other professionals interested in healthcare data analytics, risk adjustment and predictive modeling. The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare, disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U.S. For readers who wish to experiment with their own models, the book also provides access to a test dataset.
Emerging Methods In Predictive Analytics Risk Management And Decision Making
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Author : Hsu, William H.
language : en
Publisher: IGI Global
Release Date : 2014-01-31
Emerging Methods In Predictive Analytics Risk Management And Decision Making written by Hsu, William H. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-31 with Business & Economics categories.
Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.
Review Of Predictive Modeling And Uncertainty Analysis For Application In Ecological Risk Assessment
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Author : Canada. Environment Canada
language : en
Publisher:
Release Date : 1997
Review Of Predictive Modeling And Uncertainty Analysis For Application In Ecological Risk Assessment written by Canada. Environment Canada and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Ecological risk assessment categories.
Medical Risk Prediction Models
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Author : Thomas A. Gerds
language : en
Publisher: CRC Press
Release Date : 2021-02-01
Medical Risk Prediction Models written by Thomas A. Gerds and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-01 with Mathematics categories.
Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians, epidemiologists, and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination, calibration, and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service, and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research.
Predictive Models To Risk Analysis With Neural Networks Regression And Decision Trees
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Author :
language : en
Publisher: CESAR PEREZ
Release Date :
Predictive Models To Risk Analysis With Neural Networks Regression And Decision Trees written by and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
The essential aim of this book is to use predictive models to analyze risk. Models of decision trees, regression and neural networks are used to predict various risk categories. This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models and neural network models to predict a continuous target. Successive chapters present examples that clarify the application of the models in the field of risk. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used. The book begins by introducing the basics of creating a project, manipulating data sources, and navigating through different results windows. Data Mining tools are used to build the main risk models: Decision Tree, Neural Network, and Regression.
Probabilistic Predictive Modelling For Complex System Risk Assessments
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Author : Andrey Kostogryzov
language : en
Publisher:
Release Date : 2020
Probabilistic Predictive Modelling For Complex System Risk Assessments written by Andrey Kostogryzov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.
The risks assessment is described by the action of estimating the probability distribution functions of possible successes or failures of a system during a given prediction period. Typical probabilistic predictive models and methods for solving risks prediction problems are described, and their classification is given. Priority development directions for risks prediction in standard system processes and their implementation procedures are proposed. The reported examples demonstrate the effects and interpretation of the predictive results obtained. Notes: 1. System is a combination of interacting elements organized to achieve one or more stated purposes (according to ISO/IEC/IEEE 15288 ,ÄúSystems and software engineering,ÄîSystem life cycle processes,Äù). 2. Risk is defined as the effect of uncertainty on objectives considering consequences. An effect is a deviation from the expected ,Äî positive and/or negative (according to ISO Guide 73).
Risk Assessment And Evaluation Of Predictions
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Author : Mei-Ling Ting Lee
language : en
Publisher: Springer
Release Date : 2013-12-11
Risk Assessment And Evaluation Of Predictions written by Mei-Ling Ting Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Medical categories.
Methods of risk analysis and the outcome of particular evaluations and predictions are covered in detail in this proceedings volume, whose contributions are based on invited presentations from Professor Mei-Ling Ting Lee's 2011 symposium on Risk Analysis and the Evaluation of Predictions. This symposium was held at the University of Maryland in October of 2011. Risk analysis is the science of evaluating health, environmental, and engineering risks resulting from past, current, or anticipated, future activities. The use of these evaluations include to provide information for determining regulatory actions to limit risk, present scientific evidence in legal settings, evaluate products and potential liabilities within private organizations, resolve World Trade disputes amongst nations, and educate the public concerning particular risk issues. Risk analysis is an interdisciplinary science that relies on epidemiology and laboratory studies, collection of exposure and other field data, computer modeling, and related social, economic and communication considerations. In addition, social dimensions of risk are addressed by social scientists.
Predictive Modeling Based Approaches For Secure And Energy Efficient
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Author : Jithish J
language : en
Publisher:
Release Date : 2023-05-25
Predictive Modeling Based Approaches For Secure And Energy Efficient written by Jithish J and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-25 with categories.
"Predictive Modeling-Based Approaches for Secure and Energy Efficient Systems" is an insightful and comprehensive book that explores the intersection of predictive modeling, security, and energy efficiency in various systems. Authored by experts in the field, this book serves as a valuable resource for researchers, practitioners, and professionals seeking to leverage predictive modeling techniques to enhance the security and energy efficiency of modern systems. In this book, the authors delve into the challenges and opportunities presented by the ever-increasing demand for secure and energy-efficient systems across domains such as cyber-physical systems, IoT, cloud computing, and wireless networks. They present innovative approaches that utilize predictive modeling and advanced analytics to proactively identify security threats, optimize energy consumption, and improve overall system performance. Key topics covered in this book include: Predictive modeling fundamentals: The authors provide a comprehensive introduction to predictive modeling techniques, including machine learning, statistical analysis, and data mining. Readers gain a solid understanding of model training, feature selection, validation, and evaluation methods as foundational knowledge for secure and energy-efficient systems. Security prediction and threat detection: The book explores how predictive modeling can be used to anticipate and mitigate security threats in various systems. It covers topics such as anomaly detection, intrusion detection, malware analysis, and risk assessment, highlighting how predictive models can identify and respond to emerging security risks. Energy efficiency optimization: The authors examine the role of predictive modeling in optimizing energy consumption and improving energy efficiency in diverse systems. They discuss techniques such as load prediction, power management, resource allocation, and scheduling algorithms, demonstrating how predictive models can enable intelligent decision-making for energy conservation. Data-driven security and privacy: Recognizing the importance of data-driven approaches, the book investigates how predictive modeling can address security and privacy concerns. It explores techniques such as privacy-preserving predictive modeling, secure data sharing, and privacy risk assessment, promoting the development of secure and privacy-aware systems. Real-world applications and case studies: The authors provide practical insights through real-world applications and case studies across various domains. Examples include smart grids, smart homes, intelligent transportation systems, and healthcare, illustrating how predictive modeling-based approaches can enhance security and energy efficiency in specific contexts. Throughout the book, the authors emphasize the integration of predictive modeling techniques with system design, architecture, and optimization. They offer guidance on selecting suitable models, data collection and preprocessing, model deployment, and evaluating the effectiveness of predictive models in practical settings. By embracing the power of predictive modeling, "Predictive Modeling-Based Approaches for Secure and Energy Efficient Systems" equips readers with the knowledge and tools to develop and deploy robust and energy-efficient systems while proactively addressing security threats. This book provides a foundation for researchers and practitioners to advance the state-of-the-art in secure and energy-efficient technologies across a range of applications and domains.
Predictive Modelling In Food
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Author : Antonio Valero Diaz
language : en
Publisher: Cambridge Scholars Publishing
Release Date : 2019-09-13
Predictive Modelling In Food written by Antonio Valero Diaz and has been published by Cambridge Scholars Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-13 with Business & Economics categories.
This volume brings together papers detailing the latest advances in the field of predictive microbiology in foods presented at the 10th International Conference on Predictive Modelling in Food, held in Córdoba, Spain, in 2016. Predictive microbiology is a scientific area providing mathematical models to predict microbial behaviour in the food environment, providing valuable tools for food risk managers, food scientists and the food industry as a whole. The book introduces the reader to the most used and recognized modelling techniques for food, providing a thorough overview of this discipline and establishing the basis for future investigations. It is presented as a compendium of several high-quality research studies developed across the world, representing a unique contribution to the field as it shows recent discoveries and new trends of modelling in food and risk assessment. The most innovative methods, such as the use of genomic information for risk assessment and the application of quantitative risk assessment technology for foodborne pathogenic microorganisms, are also included here.