Titlebook: Statistical Machine Learning for Engineering with Applications; Jürgen Franke,Anita Sch?bel Textbook 2024 The Editor(s) (if applicable) an

啜泣

Optimal Experimental Design Supported by Machine Learning Regression Models,the machine learning approach by applying the algorithm to several models from chemical process engineering. For small-scale models the method obtains similar results, and for large-scale models the method drastically outperforms classical OED algorithms.

right-atrium

水汽

0930-0325 scientists to understand the potential and pitfalls of mach.This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods.

陶醉

Segmentation and Aggregation in Text Classification,tation setup. As a result, the classification algorithm gives a probability for each segmentation and every class. To classify the whole document, an aggregation method is needed. We investigate different approaches of aggregating and apply those methods to text documents for public administration.

orthopedist

Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization,for document digitization that combines various state-of-the-art machine learning techniques to not only give reliable results but enable user interference and traceability for nonexperts. We evaluate our solution with the help of legal authorities as well as investigators and apply it to data from real criminal investigations.

Condescending

Machine Learning Methods for Prediction of Breakthrough Curves in Reactive Porous Media,pproximation tensor method, are well suited for predicting breakthrough curves (Fokina et al., On the performance of machine learning methods for breakthrough curve prediction (2022)). In this paper, we demonstrate their performance in the case of pore-scale reactive flow in catalytic filters.

fringe

Hardware-Aware Neural Architecture Search, search automatizes the search for optimal topologies. We will outline the implementation of an evolutionary search algorithm for neural networks, which can take several criteria for the search into consideration. As a final step, hardware awareness is introduced for an exemplary hardware architecture.

ANIM

Data Analytics, Artificial Intelligence, and Machine Learning in Mobility and Vehicle Engineering,r applications, especially in the field of vehicle engineering and mobility. This article gives an overview of possible application and throws some spotlights on specifically chosen examples and case studies.

exclamation

Textbook 2024arning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice.

FLINT

An Introduction of Statistical Learning for Engineers,rithms. In the following, we present a leisurely introduction to this topic, as it is of particular interest for applications in engineering research and practice..Statistics is one of the roots of modern machine learning, in particular the discipline of nonparametric statistics, which more than 40