University Sétif 1 FERHAT ABBAS Faculty of Sciences
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Titre : Deep learning for the analysis of emotions in the Algerian dialect Type de document : texte imprimé Auteurs : Merouani ,Ahmed Abdenour, Auteur ; Mediani,Chahrazed, Directeur de thèse Editeur : Setif:UFA Année de publication : 2021 Importance : 1 vol (66 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine learning
Deep learningIndex. décimale : 004 - Informatique Résumé :
Machines and algorithms can now discern a variety of human emotions thanks
to technological advancements. Emotion identification has a significant social
impact and is increasingly in demand in a number of industries, from
retail to healthcare. Aside from the important role of emotion recognition
in healthcare, which aids in the diagnosis of mental illnesses by identifying a
pattern in emotional kinds, adv .
The goal of this research was to create a deep learning model that could
recognize emotions from the text we exchange on a regular basis. Nowadays,
personalisation is required in everything we encounter on a daily basis.Côte titre : MAI/0484 En ligne : https://drive.google.com/file/d/1iMcfS7T7QXepC-KqZ-plIda35KnRBLvH/view?usp=shari [...] Format de la ressource électronique : Deep learning for the analysis of emotions in the Algerian dialect [texte imprimé] / Merouani ,Ahmed Abdenour, Auteur ; Mediani,Chahrazed, Directeur de thèse . - [S.l.] : Setif:UFA, 2021 . - 1 vol (66 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Machine learning
Deep learningIndex. décimale : 004 - Informatique Résumé :
Machines and algorithms can now discern a variety of human emotions thanks
to technological advancements. Emotion identification has a significant social
impact and is increasingly in demand in a number of industries, from
retail to healthcare. Aside from the important role of emotion recognition
in healthcare, which aids in the diagnosis of mental illnesses by identifying a
pattern in emotional kinds, adv .
The goal of this research was to create a deep learning model that could
recognize emotions from the text we exchange on a regular basis. Nowadays,
personalisation is required in everything we encounter on a daily basis.Côte titre : MAI/0484 En ligne : https://drive.google.com/file/d/1iMcfS7T7QXepC-KqZ-plIda35KnRBLvH/view?usp=shari [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0484 MAI/0484 Mémoire Bibliothéque des sciences Anglais 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
Disponible