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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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Involvement of Domain Experts in the AI Training Does not Affect Adherence: An AutoML Study
AutoML is a promising field of Machine Learning (ML) that is supposed to bring the advantages of artificial intelligence to a wide range of...
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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...
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Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data
A correct protocol assignment is critical to high-quality imaging examinations, and its automation can be amenable to natural language processing...
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A Hierarchical Dissimilarity Metric for Automated Machine Learning Pipelines, and Visualizing Search Behaviour
In this study, the challenge of develo** a dissimilarity metric for machine learning pipeline optimization is addressed. Traditional approaches,...
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Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach
Predicting students’ learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this...
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Application of K-Means Clustering Algorithm in Automatic Machine Learning
This article studies the application of k-means clustering algorithm in automatic machine learning. By selecting the Iris dataset from the UCI...
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Identifying major climate extreme indices driver of stream flow discharge variability using machine learning and SHaply Additive Explanation
This study identifies major climate extreme indices as drivers of stream flow discharge variability using machine learning and the SHaply Additive...
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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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Machine Learning-Based Prototype Design for Rainfall Forecasting
Predicting rainfall is one of the difficult and uncertain activities that have a significant influence on human society. Predictions that are correct...
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Graph Neural Networks: AutoML
Graph neural networks (GNNs) are efficient deep learning tools to analyze networked data. Being widely applied in graph analysis tasks, the rapid...
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Efficient Automated Deep Learning for Time Series Forecasting
Recent years have witnessed tremendously improved efficiency of Automated Machine Learning (AutoML), especially Automated Deep Learning (AutoDL)...
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Production Time Prediction for Contract Manufacturing Industries Using Automated Machine Learning
The estimation of production time is an essential part of the manufacturing domain, allowing companies to optimize their production plan and meet the...
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Development of a code-free machine learning model for the classification of cataract surgery phases
This study assessed the performance of automated machine learning (AutoML) in classifying cataract surgery phases from surgical videos. Two...
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Machine Learning Algorithm Recommendation System
There has been a significant increase in interest and growth in industrial machine learning applications recently. As a result, demand for...
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On the Use of AutoML for Combating Alert Fatigue in Security Operations Centers
An overwhelming number of alerts – especially false ones – can desensitize analysts in security operations centers (SOC), possibly resulting in...
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Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data
Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such...
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Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars
Automated Machine Learning (AutoML) has become increasingly popular in recent years due to its ability to reduce the amount of time and expertise...
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ML Pro: digital assistance system for interactive machine learning in production
The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution...
