Valentine Fontama

Evaluating convective heat transfer coefficients using neural networks

Liquid crystal thermography combined with transient conduction analysis is often used to deduce local values of convective heat transfer coefficients. Neural networks based on the backpropagation algorithm have been successfully applied to predict heat transfer coefficients from a given set of experimentally obtained conditions. Performance characteristics studied on numerous network configurations relevant to this application indicate that a 3-6-3-1 arrangement yields the least errors with convergence improving directly with both the global learning rates and those of individual layers.

Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain

Abstract Biological monitoring of river water quality in the United Kingdom and several other European and Commonwealth countries is based on the Biological Monitoring Working Party (BMWP) system. Central to the present day application of this system is the prediction of “unpolluted” average score per taxon (ASPT) and number of families present (NFAM). The paper outlines the need for such predictions and proceeds to develop predictors of ASPT and NFAM using neural networks. The basic principles of neural networks are outlined and a brief introduction to their structure and function is given via a typical example. Important preliminary considerations are fully discussed, such as model selection, training and testing procedures and the selection of relevant input variables. The results of impact analyses, designed to optimise the structures of the networks, are reported and discussed. In-depth analyses of the performance of the networks on independent test data and also relative to the industrys current model, RIVPACS III, are presented. The results of investigations into bias and error in the predicted values of ASPT and NFAM are discussed and related to some possible inadequacies in the database. It is concluded that: predictions of ASPT are significantly more reliable than those of NFAM; the neural networks performed marginally better than RIVPACS III; ASPT and NFAM can be predicted directly, without reference to site type or biological community, from a few key environmental variables; and there is scope for improved predictions if additional relevant environmental data are collected.

The role of neural networks in fluid mechanics and heat transfer

The recent applications of Artificial Neural Networks (ANNs) to fluid mechanics and heat transfer are presented. ANNs have proved beneficial by their capability in modelling complex nonlinear problems as well as providing a fast, automatic method in some applications. In heat transfer the backpropagation model has been predominant and it has also been widely used in fluid mechanics. However, flow visualization has witnessed a substantial application of unsupervised learning algorithms. Finally, a novel technique that uses two ART2 (Adaptive Resonance Theory) networks to determine fluid flow velocities has been developed by the authors resulting in accuracies of up to 96.4%

Predictive Analytics with Microsoft Azure Machine Learning

Predictive 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. Whats New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration a new feature announced February 2015Data preparation and feature selection Data visualization with Power BIRecommendation engines Selling your models on Azure Marketplace What youll 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 BIA 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.

Using ART2 networks to deduce flow velocities

A novel algorithm for obtaining flow velocity vectors using ART2 networks (based on adaptive resonance theory) is presented. The method involves tracking the movement of groups of seeding particles in a fluid space through the analysis of two successive images. Simulated flows, created artificially by shifting the particles through known distances or rotating through known angles, were used to establish the accuracy of the technique in predicting displacements. Accuracies were quantified by comparison with known displacements and were found to improve with increasing displacement, angle of rotation and size of the sampling window. In addition, the technique has been extended to derive qualitative and quantitative information for a practical case of natural convective flow.

Introduction to Statistical and Machine Learning Algorithms

This chapter will serve as a reference for some of the most commonly used algorithms in Microsoft Azure Machine Learning. We will provide a brief introduction to algorithms such as linear and logistic regression, k-means for clustering, decision trees, decision forests (random forests, boosted decision trees, and Gemini), neural networks, support vector machines, and Bayes point machines.

Building Customer Propensity Models

This chapter will provide a practical guide for building machine learning models. It focuses on buyer propensity models, showing how to apply the data science process to this business problem. Through a step-by-step guide, this chapter will explain how to apply key concepts and leverage the capabilities of Microsoft Azure Machine Learning for propensity modeling.

Integration with R

This chapter will introduce R and show how it is integrated with Microsoft Azure Machine Learning. Through simple examples, you will learn how to write and run your own R code when working with Azure Machine Learning. You will also learn the R packages supported by Azure Machine Learning, and how you can use them in the Azure Machine Learning Studio (ML Studio).

Integration with Python

This chapter shows you how to use Python in Azure Machine Learning (Azure ML). Using simple examples, you will learn how to integrate Python as part of an Azure ML experiment. This enables you to tap into the powerful capabilities offered by various Python libraries, such as NumPy, SciPy, pandas, scikit-learn, and many more, directly in an Azure ML experiment.

Building Predictive Maintenance Models

The leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost yet increase margin and customer satisfaction. Though traditional techniques such as statistics and manufacturing have helped, the industry is still plagued by serious quality issues and the high cost of business disruption when components fail. Advances in machine learning offer a unique opportunity to improve customer satisfaction and reduce service downtime. This chapter shows how to build models for predictive maintenance using Microsoft Azure Machine Learning. Through examples we will demonstrate how you can use Microsoft Azure Machine Learning to build, validate, and deploy a predictive model for predictive maintenance.