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Dynamic prediction and multi-objective optimization on driving position of tunnel boring machine (TBM): an automated deep learning approach
This paper proposes an automated deep learning (AutoDL) framework for dynamic prediction and multi-objective optimization (MOO) on the driving...
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Towards big industrial data mining through explainable automated machine learning
Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they...
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AutoTiM - An Open-Source Service for Automated Provisioning and Operation of Time Series Based Machine Learning Models
The ubiquitous availability of heterogeneous sensor data created by Internet-of-Things (IoT) technologies and Industry 4.0 trends drastically...
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A Robust Automated Machine Learning System with Pseudoinverse Learning
Develo** a robust deep neural network (DNN) for a specific task is not only time-consuming but also requires lots of experienced human experts. In...
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A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities
In this paper, we explore and compare three recently proposed Automated Machine Learning (AutoML) tools (AutoGluon, H...
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Forecasting closures on shellfish farms using machine learning
Biotoxins and harmful algal blooms (HABs) are damaging to aquaculture operations. Occurrences lead to disrupted operations, fish kills, and...
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Political Optimizer-Based Automated Machine Learning for Skin Lesion Data
Today in the age of information revolution, everything is being automated. Machine learning is needed for every industry to boost growth in business,...
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AutoClues: Exploring Clustering Pipelines via AutoML and Diversification
AutoML has witnessed effective applications in the field of supervised learning – mainly in classification tasks – where the goal is to find the best...
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Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance
Despite the broad range of Machine Learning (ML) algorithms, there are no clear guidelines on how to identify the optimal algorithm and corresponding...
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Genetic Algorithms for AutoML in Process Predictive Monitoring
In recent years, AutoML has emerged as a promising technique for reducing computational and time cost by automating the development of machine...
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AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals
Part defects in additive manufacturing are more frequent compared to machining or molding. Failures can go unnoticed for hours, wasting resources and...
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Investigation of Random Laser in the Machine Learning Approach
Machine learning and deep learning are computational tools that fall within the domain of artificial intelligence. In recent years, numerous research...
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STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex...
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An AutoML Based Algorithm for Performance Prediction in HPC Systems
Neural networks are extensively utilized for building performance prediction models for high-performance computing systems. It is challenging to...
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Diagnostics of Oil Well Pum** Equipment by Using Machine Learning
AbstractIf speaking of timely detection of deviations in operation of pum** equipment, there is a problem of the current coverage of the oil well...
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Deep Heterogeneous AutoML Trend Prediction Model for Algorithmic Trading in the USD/COP Colombian FX Market Through Limit Order Book (LOB)
This study presents a novel and competitive approach for algorithmic trading in the Colombian US dollar inter-bank market (SET-FX). At the core of...
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TPOT-NN: augmenting tree-based automated machine learning with neural network estimators
Automated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding...
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Building a Model of Wind Turbine Power Using AutoML Methods
Wind power is one of the prominent alternative sources of energy. But due to its unstable nature it is crucial to be able to predict amount of energy...
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Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic...
