Application of deep learning approach for COVID-19 cases forecasting / Meriem Atig

Public ISBD Titre : Application of deep learning approach for COVID-19 cases forecasting Type de document : texte manuscrit Auteurs : Meriem Atig, Auteur ; Mustapha Bouakkaz, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2022 Importance : 51 p. Format : 30 cm. Accompagnement : 1 disque optique numérique Note générale : Option : Information and decision system (Systèmes d'information et de décision) Langues : Anglais Mots-clés : Covid 19  Prediction  Deep Learning  Neural Network (NN)  Recurrent Neural Network (RNN)  Long - Short Term Memory ( LSTM )  Machine Learning  Artificial Intelligence Résumé : Abstract COVID - 19 , also Brown as the coronavirus , has paralyzed the whole world , infected more than 520 million people worldwide , and caused the death of more than 6 million people . Over the past few decades , the field of artificial intelligence and deep learning has shown tremendous development and multiple uses in many fields such as security , self - driving cars , robotics , and especially healthcare ... The enormous impact of this pandemic has prompted us to try to find and explore ways to use this technology to help limit its spread We proposed a method using long - short term memory ( LSTM ) a type of recurrent neural network ( RNN ) to predict the number of cases in the near future by using the information we have from previous cases since the beginning of the epidemic . note de thèses : Mémoire de master en informatique Application of deep learning approach for COVID-19 cases forecasting [texte manuscrit] / Meriem Atig, Auteur ; Mustapha Bouakkaz, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2022 . - 51 p. ; 30 cm. + 1 disque optique numérique. Option : Information and decision system (Systèmes d'information et de décision) Langues : Anglais Mots-clés : Covid 19  Prediction  Deep Learning  Neural Network (NN)  Recurrent Neural Network (RNN)  Long - Short Term Memory ( LSTM )  Machine Learning  Artificial Intelligence Résumé : Abstract COVID - 19 , also Brown as the coronavirus , has paralyzed the whole world , infected more than 520 million people worldwide , and caused the death of more than 6 million people . Over the past few decades , the field of artificial intelligence and deep learning has shown tremendous development and multiple uses in many fields such as security , self - driving cars , robotics , and especially healthcare ... The enormous impact of this pandemic has prompted us to try to find and explore ways to use this technology to help limit its spread We proposed a method using long - short term memory ( LSTM ) a type of recurrent neural network ( RNN ) to predict the number of cases in the near future by using the information we have from previous cases since the beginning of the epidemic . note de thèses : Mémoire de master en informatique

Réservation

Réserver ce document

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
MF 02-46	MF 02-46	Thése	BIBLIOTHEQUE DE FACULTE DES SCIENCES	théses (sci)	Disponible

---

page 1/1