During the last decades, the advent of Artificial Intelligence (AI) has been taking place in several technical and scientific areas. Despite its success, AI applications to solve real-life problems in pavement enginee...
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ISBN:
(纸本)9783319653402;9783319653396
During the last decades, the advent of Artificial Intelligence (AI) has been taking place in several technical and scientific areas. Despite its success, AI applications to solve real-life problems in pavement engineering are far from reaching its potential. In this paper, a Python machine learning library, scikit-learn, is used to predict asphalt pavement friction. Using data from the Long-Term Pavement Performance (LTPP) database, 113 different sections of asphalt concrete pavement, spread all over the United States, were selected. Two machine learning models were built from these data to predict friction, one based on linear regression and the other on regularized regression with lasso. Both models showed to be feasible and perform similarly. According to the results, initial friction plays an essential role in the way friction evolves over time. The results of this study also showed that scikit-learn can be a versatile tool to solve pavement engineering problems. By applying machine learning methods to predict asphalt pavements friction, this paper emphasizes how theory and practice can be effectively coupled to solve real-life problems in contemporary transportation.
learn advanced techniques to improve the performance and quality of your predictive modelsKey Features• Use ensemble methods to improve the performance of predictive analytics models• Implement feature selection, dime...
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ISBN:
(数字)9781789612240
ISBN:
(纸本)9781789617740
learn advanced techniques to improve the performance and quality of your predictive models
Key Features
• Use ensemble methods to improve the performance of predictive analytics models
• Implement feature selection, dimensionality reduction, and cross-validation techniques
• Develop neural network models and master the basics of deep learning
Book Description
Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.
This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.
By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.
What you will learn
• Use ensemble algorithms to obtain accurate predictions
• Apply dimensionality reduction techniques to combine features and build better models
• Choose the optimal hyperparameters using cross-validation
• Implement different techniques to solve current challenges in the predictive analytics domain
• Understand various elements of deep neural network (DNN) models
• Implement neural networks to solve both classification and regression problems
Who this book is for
Mastering Predictive Analytics with scikit-learn and TensorFlow is for dat
With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new levelKey Features• Explore scikit-l...
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ISBN:
(数字)9781789801767
ISBN:
(纸本)9781789803556
With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level
Key Features
• Explore scikit-learn uniform API and its application into any type of model
• Understand the difference between supervised and unsupervised models
• learn the usage of machine learning through real-world examples
Book Description
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.
The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.
By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.
What you will learn
• Understand the importance of data representation
• Gain insights into the differences between supervised and unsupervised models
• Explore data using the Matplotlib library
• Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN
• Measure model performance through different metrics
• Implement a confusion matrix using scikit-learn
• Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM
• Perform error analysis to improve the performance of the model
• learn to build a comprehensive machine learning program
Who this book is for
Ma
The majority of real-world applications of machine learning employ supervised learning. With an input variable (x) and an outcome variable (y), supervised learning allows one to apply an algorithm to train the mapping...
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With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high-performance batteries like lithium-ion (Li-ion) batteries have soared. Li-ion battery systems require the use of a batte...
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With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high-performance batteries like lithium-ion (Li-ion) batteries have soared. Li-ion battery systems require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li-ion battery cells. A prismatic Li-ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models-linear regression, k-nearest neighbors, random forest, and decision tree-were developed using the scikit-learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R-2 metric. The decision tree-based model, with an R-2 score of 0.99, was determined to be the best model in this case study.
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