AmeenReda1

Cleaning_Text_Data

This repo aim to clean text data before extract features from it

Computer-Science-Textbooks

Computer science textbooks for computer science students.

consumer_complaints-classification

Face-detection-js-TensorFlow-

in this project I use Tensor Flow library with JavaScript to detect your face and your expression

Fuel-Economy-Datasets-Analysis

IMDB_web_spcraping

This project is just try to scrap top 250 film in IMDB website

memoryGame

spotify_design_vue_js

Text_classification_NADACorpus

A-Hybrid-Arabic-Text-Summarization-Approach-based-on-Transformers

In this paper, we proposed a sequential hybrid model based on a transformer to summarize Arabic articles. We used two approaches of summarization to make our model. The First is the extractive approach which depends on the most important sentences from the articles to be the summary, so we used Deep Learning techniques specifically transformers such as AraBert to make our summary, The second is abstractive, and this approach is similar to human summarization, which means that it can use some words which have the same meaning but different from the original text. We apply this kind of summary using MT5 Arabic pre-trained transformer model. We sequentially applied these two summarization approaches to building our A3SUT hybrid model. The output of the extractive module is fed into the abstractive module. We enhanced the summary’s quality to be closer to the human summary by applying this approach. We tested our model on the ESAC dataset and evaluated the extractive summary using the Rouge score technique; we got a precision of 0.5348 and a recall of 0.5515, and an f1 score of 0.4932 and the evaluation of the abstractive model is evaluated by user satisfaction. We add some features to our summary to make it more understandable by applying the metadata generation task” data about data” and classification. By applying metadata generation, we add facilities to our summary, identification, and summary organization. Metadata provides essential contextual details, as not all summaries are self-describing. Also, classify the original text to determine the summary topic before reading. We acquire 97.5% accuracy by using Support Vector Machine (SVM) and trained it using NADA corpus.

AmeenReda1/Cleaning_Text_Data

This repo aim to clean text data before extract features from it

AmeenReda1/consumer_complaints-classification

AmeenReda1/IMDB_web_spcraping

This project is just try to scrap top 250 film in IMDB website

AmeenReda1/spotify_design_vue_js

AmeenReda1/Text_classification_NADACorpus

AmeenReda1/Abdelrhman2022

AmeenReda1/AmeenReda1

AmeenReda1/e-commerce-nodejs-apis

AmeenReda1/ERD-Project

AmeenReda1/github-profile-readme-generator

🚀 Generate GitHub profile README easily with the latest add-ons like visitors count, GitHub stats, etc using minimal UI.

AmeenReda1/Information-retrieval-using-word-embeddings

AmeenReda1/Library-Managment-System

AmeenReda1/Maha

Maha is a text processing library specially developed to deal with Arabic text.

AmeenReda1/MahmoudHassan77

AmeenReda1/mishkal

Mishkal is an arabic text vocalization software

AmeenReda1/ML-sentiment-analysis

AmeenReda1/NLP

AmeenReda1/portfolio

AmeenReda1/Problem-Solving

AmeenReda1/push-notifications

AmeenReda1/Sartorius---Cell-Instance-Segmentation

# This project is about cell instance classification with Sartorius data

AmeenReda1/Shopping-cart-js-project

AmeenReda1/Spam_notSpam_classification

This project aim to classify spam and not spam email using kaggle dataset

AmeenReda1/starter-express-api

AmeenReda1/Summarization_using_summa_for_textRanking

AmeenReda1/Text_summarization

AmeenReda1/textsummarization

AmeenReda1/The-Android-Market-App

AmeenReda1/translate_german_english_deepLearning

AmeenReda1/type_Speed-js