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Model and Data Engineering

This book constitutes the refereed proceedings of the 6th International Conference on Model and Data Engineering, MEDI 2016, held;

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Model and Data Engineering

This book constitutes the refereed proceedings of the 5th International Conference on Model and Data Engineering, MEDI 2015, held in Rhodes;

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Model and Data Engineering

This book constitutes the refereed proceedings of the 9th International Conference on Model and Data Engineering, MEDI 2019, held;

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Nonlinear Regression Modeling for Engineering Applications

improvement. Alternately, when the model fits the data, our understanding is sufficient and confidently functional for engineering applications. This;

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Model and Data Engineering

This book constitutes the refereed proceedings of the 10th International Conference on Model and Data Engineering, MEDI 2021, held;

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Applied Probabilistic Calculus for Financial Engineering

illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and;

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New Trends in Model and Data Engineering

Modeling, Verification and Testing of Dependable Critical systems, DETECT 2019, Workshop on Data Science for Social Good in Africa, DSSGA 2019, and;

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Modern Engineering Statistics

This concise introduction to engineering statistics emphasizes data analysis and model building, not mathematical statistics. Intended for;

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Neural Networks for Applied Sciences and Engineering

forecasting Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction;

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Computer-Aided Highway Engineering

road infrastructures. It discusses Digital Terrain Model (DTM) using satellite data including highway geometric, pavement and tunnel design;

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Handbook of Mixture Analysis

on mixture models and their applications in scientific domains such as data mining, machine learning, physics, mechanical engineering, signal;

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Test Data Engineering

This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the;

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Data Assimilation

Data assimilation is considered a key component of numerical ocean model development and new data acquisition strategies. The basic concept;

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Deep Learning and Missing Data in Engineering Systems

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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Handbook of Neuroimaging Data Analysis

of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science;

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Web Information Systems Engineering - WISE 2017

data model, language processing and web protocols, web-based applications, data storage and generator, security and privacy, sentiment analysis;

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Web Information Systems Engineering - WISE 2017

data model, language processing and web protocols, web-based applications, data storage and generator, security and privacy, sentiment analysis;

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Handbook of Neuroimaging Data Analysis

of novel statistical approaches to model brain data. Designed for researchers in statistics, biostatistics, computer science, cognitive science;

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Engineering Of Mind

hierarchy of massive parallel computational modules and data structures interconnected by information pathways. Using this as the basic model on;

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Data Modeling Made Simple with Embarcadero ER/Studio Data Architect

Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. This;

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Generative and Transformational Techniques in Software Engineering III

transformation of programs, data, models, metamodels, documentation, and entire software systems. The topics covered include software reverse and re;

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A Framework for Model-Driven Scientific Workflow Engineering

Scientific workflows are one important means in the context of data-intensive science for reliable and efficient scientific data processing;

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Model Management and Analytics for Large Scale Systems

approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data;

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SDL 2017: Model-Driven Engineering for Future Internet

engineering of database queries; network service design and regression testing; and modeling for Internet of Things (IoT) data processing.;

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Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science

machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess;

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Introductory Time Series with R

series model and the R function used to fit the model to data. Finally, the model is used to analyse observed data taken from a practical;

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Data Model Patterns: A Metadata Map

enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that;

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