DSpace Communauté:https://dspace.univ-ouargla.dz/jspui/handle/123456789/2782026-04-12T13:54:59Z2026-04-12T13:54:59ZSVM-based classification of fruit imagesBaya, AbanouAyat errahmane, Bentayebhttps://dspace.univ-ouargla.dz/jspui/handle/123456789/387022025-11-12T15:20:07Z2023-01-01T00:00:00ZTitre: SVM-based classification of fruit images
Auteur(s): Baya, Abanou; Ayat errahmane, Bentayeb2023-01-01T00:00:00ZLogistic Regression-based model for multi-classification of iris flowersHideb, HadjerKhouidat, HouriaHassani, Kholoudhttps://dspace.univ-ouargla.dz/jspui/handle/123456789/387012025-11-12T14:41:43Z2023-01-01T00:00:00ZTitre: Logistic Regression-based model for multi-classification of iris flowers
Auteur(s): Hideb, Hadjer; Khouidat, Houria; Hassani, Kholoud2023-01-01T00:00:00Zالتنبأ بأمراض القلب باستخدام الانحدار اللوجستيبن حبيرش, محمد لمينحموية, عبد الجليلتجيني, عبد الجليلhttps://dspace.univ-ouargla.dz/jspui/handle/123456789/386952025-11-12T10:22:22Z2021-01-01T00:00:00ZTitre: التنبأ بأمراض القلب باستخدام الانحدار اللوجستي
Auteur(s): بن حبيرش, محمد لمين; حموية, عبد الجليل; تجيني, عبد الجليل
Résumé: Heart disease is one of the most common diseases in the world today and the most terrifying
as it causes many deaths. We are now in the data age where huge amounts of data are collected
and stored in the internet from different parts of the world (from companies, social networking
sites, medical clinics, etc...). In this research work, we seek to build a model that predicts heart
disease to improve early general diagnosis around it, that is, using logistic regression as a kind of
classification without the help of a set of ready-made functions for machine learning, and that is
based on a set of data consisting of 319,795 examples that help train the model and compare the
results of Model with sklearn logistic regression algorithm and some other classification
algorithms (SVM(SVC), NN, RF, KNN, DT, NBC). where the accuracy rate in our model was
good (74%), which is very close to the rest of the sklearn classification algorithms, which were
between 70% and 76% accuracy.2021-01-01T00:00:00ZAnalysing Product Reviews Using Artificial Neural Network (ANN)Issam Eddine, KiouerLahouel IbrahimBenzine Mohemmed Aymenhttps://dspace.univ-ouargla.dz/jspui/handle/123456789/386942025-11-12T10:21:41Z2023-01-01T00:00:00ZTitre: Analysing Product Reviews Using Artificial Neural Network (ANN)
Auteur(s): Issam Eddine, Kiouer; Lahouel Ibrahim; Benzine Mohemmed Aymen
Résumé: This study focuses on the development of an AI model to analyze client reviews of a
specific product and determine their sentiment towards the product. The primary objective is
to assess whether clients liked or disliked the product based on their feedback. The study
presents a program utilizing an Artificial Neural Network architecture for opinion analysis; Cette étude se concentre sur le développement d'un modèle d'intelligence artificielle (IA)
pour analyser les avis des clients sur un produit spécifique et déterminer leur sentiment à l'égard
du produit. L'objectif principal est d'évaluer si les clients ont aimé ou n'ont pas aimé le produit
en fonction de leurs commentaires. L'étude présente un programme utilisant une architecture
de réseau neuronal artificiel pour l'analyse des opinions.2023-01-01T00:00:00Z