Results 111 to 120 of about 6,098 (213)

Net-Net AutoML for CoCoMac Brain Network

, 2018
Data set for AutoML study of CoCoMac Brain network ...
Cristian Robert Munteanu (560454), Humbert Gonzalez-Diaz (5192699)   +1 more
core   +1 more source

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

Applied Sciences
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention.
Dagoberto Castellanos-Nieves, Luis García-Forte   +1 more
doaj   +1 more source

AutoIntent: AutoML for Text Classification

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
EMNLP 2025 System ...
Ilya Alekseev, Roman Solomatin, Darina Rustamova, Denis Kuznetsov   +3 more
openaire   +3 more sources

Learning meta-features for AutoML.

, 2022
This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper ...
Rakotoarison, Herilalaina, Milijaona, Louisot, Rasoanaivo, Andry, Sebag, Michèle, Schoenauer, Marc   +4 more
openaire   +2 more sources

Genetic Programming Based Automated Machine Learning in Classifying ESG Performances

IEEE Access
AutoML offers significant benefits in solving real-life problems because it accelerates the development of machine learning models. In contexts involving real scenarios like analyzing companies’ environmental, social and governance (ESG), where ...
Abdullah Sani Abd Rahman, Suraya Masrom, Rahayu Abdul Rahman, Roslina Ibrahim, Abdul Rehman Gilal   +4 more
doaj   +1 more source

Sammanflätande av AutoML och Data Science-metod

, 2021
The advent of automated machine learning (AutoML) tools promises to democratize machine learning to non-experts in the field. These tools certainly effect the process of applying machine learning for non-experts, but it is not clear how data scientists ...
Aldor, Edvard, Helle, Daniel
core   +1 more source

Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy

Ophthalmology Science
Purpose: Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes.
Arina Nisanova, BA, Arefeh Yavary, MSc, Jordan Deaner, MD, Ferhina S. Ali, MD, MPH, Priyanka Gogte, MD, Richard Kaplan, MD, Kevin C. Chen, MD, Eric Nudleman, MD, PhD, Dilraj Grewal, MD, Meenakashi Gupta, MD, Jeremy Wolfe, MD, Michael Klufas, MD, Glenn Yiu, MD, PhD, Iman Soltani, PhD, Parisa Emami-Naeini, MD, MPH   +14 more
doaj   +1 more source

Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes

Signals
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social ...
Matteo Bodini
doaj   +1 more source

No-Code and Low-Code Artificial Intelligence in Healthcare Imaging: A Scoping Review

International Dental Journal
Introduction and aims: The aim of this review was to evaluate applications, methodological characteristics, limitations and future directions of no-code, low-code and automated machine learning (LCNC/AutoML) approaches in healthcare imaging.
Manal Hamdan, Zaid Badr, Sergio E. Uribe, Marcel Reymus, Elsbeth Kalenderian, Falk Schwendicke   +5 more
doaj   +1 more source

Problem-oriented AutoML in Clustering

CoRR
The Problem-oriented AutoML in Clustering (PoAC) framework introduces a novel, flexible approach to automating clustering tasks by addressing the shortcomings of traditional AutoML solutions. Conventional methods often rely on predefined internal Clustering Validity Indexes (CVIs) and static meta-features, limiting their adaptability and effectiveness ...
Matheus Camilo da Silva, Gabriel Marques Tavares, Eric Medvet, Sylvio Barbon Junior   +3 more
openaire   +2 more sources