Výsledky vyhledávání - "Moncef Garouani"

Akademický článek

Autoencoder-kNN meta-model based data characterization approach for an automated selection of AI algorithms

Autor: Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Mohamed Hamlich

Publikováno v: Journal of Big Data, Vol 10, Iss 1, Pp 1-18 (2023)

Abstract The recent evolution of machine learning (ML) algorithms and the high level of expertise required to use them have fuelled the demand for non-experts solutions. The selection of an appropriate algorithm and the configuration of its hyperpara

Externí odkaz: https://doaj.org/article/74763689a38a4a7587adef4a0da8d524

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Akademický článek

Version [2.0] - [AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data]

Autor: Moncef Garouani, Mourad Bouneffa, Adeel Ahmad, Mohamed Hamlich

Publikováno v: SoftwareX, Vol 23, Iss , Pp 101444- (2023)

We report a new release of the self-explainable AutoML software AMLBID. The software package is a meta-learning based AutoML decision support system that assists machine learning(ML) experts and neophytes in building well performing ML pipelines and

Externí odkaz: https://doaj.org/article/ca71151924284f43a3e5c70630202494

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Akademický článek

Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data

Autor: Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Mohamed Hamlich, Gregory Bourguin, Arnaud Lewandowski

Publikováno v: Journal of Big Data, Vol 9, Iss 1, Pp 1-20 (2022)

Abstract Advanced analytics are fundamental to transform large manufacturing data into resourceful knowledge for various purposes. In its very nature, such “industrial big data” can relay its usefulness to reach further utilitarian applications.

Externí odkaz: https://doaj.org/article/baf05cc1c68949049ec48a693eb87a2e

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Akademický článek

AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data

Autor: Moncef Garouani, Adeel Ahmad, Mourad Bouneffa, Mohamed Hamlich

Publikováno v: SoftwareX, Vol 17, Iss , Pp 100919- (2022)

The Machine Learning(ML) based solutions in manufacturing industrial contexts often require skilled resources. More practical non-expert software solutions are then desired to enhance the usability of ML algorithms. The algorithm selection and config

Externí odkaz: https://doaj.org/article/7c85caab88614dc68ce154713e48d07c

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Product defect detection based on convolutional autoencoder and one-class classification

Autor: Meryem Chaabi, Mohamed Hamlich, Moncef Garouani

To meet customer expectations and remain competitive, industrials try constantly to improve their quality control systems. There is hence increasing demand for adopting automatic defect detection solutions. However, the biggest issue in addressing su

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4cc7e44dd3ffd5700854abb140a0b637
https://zenodo.org/record/7455904

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