Characterizing the electric energy curve can improve the energy efficiency of existing
buildings without any structural change and is the basis for controlling and optimizing …

In this study, we present a comprehensive approach for predicting the remaining useful life
(RUL) of aircraft engines, incorporating advanced feature engineering, dimensionality …

Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

In the context of the current COVID-19 pandemic, various sophisticated epidemic and
machine learning models have been used for forecasting. These models, however, rely on …

This article is dedicated to solving the problem of an insufficient degree of automation of
artificial neural network training. Despite the availability of a large number of libraries for …

Electrical biosignals, stemming from potential differences in specific tissues like muscles,
play a pivotal role in medical diagnoses, aiding in identifying ailments from arrhythmias to …

This paper demonstrates the application of a fully automated machine learning (ML) pipeline
on time series data from the domain of production engineering. The workflow aims to …

The utilization of machine learning has become ubiquitous in addressing contemporary
challenges in data science. Moreover, there has been significant interest in democratizing …

Time series forecasting (TSF) is vital in fields like finance, economics, and meteorology. This
thesis extensively probes TSF, contributing to econometrics and deep learning. In this thesis …

In the realm of data science, dealing with real-world datasets often presents a formidable
challenge, primarily due to the sheer volume of features that significantly lack relevance or …