University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Titre : A Deep Learning Model For Collaborative Filtering Type de document : texte imprimé Auteurs : Ferkous, Mohamed Laid, Auteur ; Ahlem Drif, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (83 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Recommender systems
Mutual Influence
Graph Attention NetworkIndex. décimale : 004 - Informatique Résumé :
Recommender systems have become an integral part of e-commerce sites and
other platforms such as social networking, movie/-music rendering websites. Network representation
learning have been used as a successful approaches to build efficient recommender
systems. However, learning the mutual influence generated by the contributions of each
node in the network is a challenging issue. In fact, the mutual influence carries collaborative
signals on user decisions helping to account for complex user-item interactions. For this
purpose, in this master thesis, we propose Multual Intercation Graph Attention Network
‘MIGAN” which is a new algorithm based on a the self-supervised representation learning
on large scale bipartite graph (BGNN). Experiments on real-world datasets demonstrate
that the proposed Graph learning method can achieve more accurate predictions and higher
recommendation efficiency.Côte titre : MAI/0522 En ligne : https://drive.google.com/file/d/1mGYDn5WsQu4tz8wXwWqFf3RXOFw_iyJV/view?usp=shari [...] Format de la ressource électronique : A Deep Learning Model For Collaborative Filtering [texte imprimé] / Ferkous, Mohamed Laid, Auteur ; Ahlem Drif, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (83 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Recommender systems
Mutual Influence
Graph Attention NetworkIndex. décimale : 004 - Informatique Résumé :
Recommender systems have become an integral part of e-commerce sites and
other platforms such as social networking, movie/-music rendering websites. Network representation
learning have been used as a successful approaches to build efficient recommender
systems. However, learning the mutual influence generated by the contributions of each
node in the network is a challenging issue. In fact, the mutual influence carries collaborative
signals on user decisions helping to account for complex user-item interactions. For this
purpose, in this master thesis, we propose Multual Intercation Graph Attention Network
‘MIGAN” which is a new algorithm based on a the self-supervised representation learning
on large scale bipartite graph (BGNN). Experiments on real-world datasets demonstrate
that the proposed Graph learning method can achieve more accurate predictions and higher
recommendation efficiency.Côte titre : MAI/0522 En ligne : https://drive.google.com/file/d/1mGYDn5WsQu4tz8wXwWqFf3RXOFw_iyJV/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0522 MAI/0522 Mémoire Bibliothéque des sciences Anglais Disponible
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Titre : Deep Learning model for diagnosis of Diabetic Retinopathy Type de document : texte imprimé Auteurs : Bellal, Haroune, Auteur ; Mediani,Chahrazed, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (44 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Côte titre : MAI/0531 En ligne : https://drive.google.com/file/d/1kd1ou4QKRTnlx3y6GGoT3kFVfluoekXU/view?usp=shari [...] Format de la ressource électronique : Deep Learning model for diagnosis of Diabetic Retinopathy [texte imprimé] / Bellal, Haroune, Auteur ; Mediani,Chahrazed, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (44 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Côte titre : MAI/0531 En ligne : https://drive.google.com/file/d/1kd1ou4QKRTnlx3y6GGoT3kFVfluoekXU/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0531 MAI/0531 Mémoire Bibliothéque des sciences Français Disponible
DisponibleA deep learning model for fakenewes detection / Belhakimi,Mohamed Amine
Titre : A deep learning model for fakenewes detection Type de document : texte imprimé Auteurs : Belhakimi,Mohamed Amine, Auteur ; Drif,Ahlem, Directeur de thèse Editeur : Setif:UFA Année de publication : 2019 Importance : 1 vol (59 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0282 A deep learning model for fakenewes detection [texte imprimé] / Belhakimi,Mohamed Amine, Auteur ; Drif,Ahlem, Directeur de thèse . - [S.l.] : Setif:UFA, 2019 . - 1 vol (59 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Informatique Index. décimale : 004 Informatique Côte titre : MAI/0282 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0282 MAI/0282 Mémoire Bibliothéque des sciences Français Disponible
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Titre : A Deep Learning Model For A Hybrid Recommender System Of Online Resources Type de document : texte imprimé Auteurs : Sebihi,Abdelkader, Auteur ; Mediani,Chahrazed, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (70 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine learning
Deep learning
Recommender systemsIndex. décimale : 004 - Informatique Résumé :
Recommender system is a tool for interacting with large scale and complex
information space. They provide personalized views and prioritized items
likely to be of interest to the user. The field was named in 1995 and has been
greatly developed in the various problems solved, the technology used and
its practical application. Recommender systems research has incorporated
a wide variety of artificial intelligence techniques including machine learning,
data mining, user modeling, case-based reasoning, and constraint satisfaction,
among others. Personalized and prioritized recommendations are
an important part of many on-line e-commerce applications such as Amazon.
com, Netflix, and Pandora.Côte titre : MAI/0468 En ligne : https://drive.google.com/file/d/1-ZZ1i0PobHJ_2WhxueC8d6w_YP4IxTvk/view?usp=shari [...] Format de la ressource électronique : A Deep Learning Model For A Hybrid Recommender System Of Online Resources [texte imprimé] / Sebihi,Abdelkader, Auteur ; Mediani,Chahrazed, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (70 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine learning
Deep learning
Recommender systemsIndex. décimale : 004 - Informatique Résumé :
Recommender system is a tool for interacting with large scale and complex
information space. They provide personalized views and prioritized items
likely to be of interest to the user. The field was named in 1995 and has been
greatly developed in the various problems solved, the technology used and
its practical application. Recommender systems research has incorporated
a wide variety of artificial intelligence techniques including machine learning,
data mining, user modeling, case-based reasoning, and constraint satisfaction,
among others. Personalized and prioritized recommendations are
an important part of many on-line e-commerce applications such as Amazon.
com, Netflix, and Pandora.Côte titre : MAI/0468 En ligne : https://drive.google.com/file/d/1-ZZ1i0PobHJ_2WhxueC8d6w_YP4IxTvk/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0468 MAI/0468 Mémoire Bibliothéque des sciences Français Disponible
Disponible
Titre : A Deep Learning Model for Predication Type de document : texte imprimé Auteurs : Ammar Assif Menaouel, Auteur ; Seif Eddine Ayad, Auteur ; Daifi,ahlem, Directeur de thèse Année de publication : 2022 Importance : 1 vol (85 f .) Format : 29cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine Learning
Deep LearningIndex. décimale : 004 Informatique Résumé :
Time series forecasting involves developing a predictive model on data where there is an
ordered relationship between observations. In fact, there are many challenges when forecasting
one or more possible future observations because forecasting models add the complexity of order
or temporal dependence between observations. Traditionally, time series forecasting has been
dominated by linear methods like ARIMA because they are well understood and effective on
many problems. However, this linear relationship excludes more complex joint distributions and
many real-world problems have multiple input variables. In this thesis, we focus on developing
a deep learning model as it has already been proved that they are effective on more complex
time series forecasting problems with multiple input variables. Our proposed model is based on
LSTM autoencoder (Long Short Term Memory), which extract complex nonlinear relationships
and perform well for mutivariates inputs. In addition, the ability of the autoencoder to project
the data in latent space help to deal with the limitation of missing data. We conducted our
experiments on a data set of people with type 1 diabetes in order to predict their blood glucose
level for a period of 5 minutes to an hour. We have obtained a good result, which we will work
on improving in the upcoming works, InchaaAllahCôte titre : MAI/0582 En ligne : https://drive.google.com/file/d/1dgX0lB3xwdmu5pvcqrhPGFAWV7YK3fZF/view?usp=share [...] Format de la ressource électronique : A Deep Learning Model for Predication [texte imprimé] / Ammar Assif Menaouel, Auteur ; Seif Eddine Ayad, Auteur ; Daifi,ahlem, Directeur de thèse . - 2022 . - 1 vol (85 f .) ; 29cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine Learning
Deep LearningIndex. décimale : 004 Informatique Résumé :
Time series forecasting involves developing a predictive model on data where there is an
ordered relationship between observations. In fact, there are many challenges when forecasting
one or more possible future observations because forecasting models add the complexity of order
or temporal dependence between observations. Traditionally, time series forecasting has been
dominated by linear methods like ARIMA because they are well understood and effective on
many problems. However, this linear relationship excludes more complex joint distributions and
many real-world problems have multiple input variables. In this thesis, we focus on developing
a deep learning model as it has already been proved that they are effective on more complex
time series forecasting problems with multiple input variables. Our proposed model is based on
LSTM autoencoder (Long Short Term Memory), which extract complex nonlinear relationships
and perform well for mutivariates inputs. In addition, the ability of the autoencoder to project
the data in latent space help to deal with the limitation of missing data. We conducted our
experiments on a data set of people with type 1 diabetes in order to predict their blood glucose
level for a period of 5 minutes to an hour. We have obtained a good result, which we will work
on improving in the upcoming works, InchaaAllahCôte titre : MAI/0582 En ligne : https://drive.google.com/file/d/1dgX0lB3xwdmu5pvcqrhPGFAWV7YK3fZF/view?usp=share [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0582 MAI/0582 Mémoire Bibliothéque des sciences Anglais Disponible
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