Shahad Altamimi

Arabic Named Entity Recognition Using AraBERT

This project implements an Arabic Named Entity Recognition (NER) system using AraBERT.
It focuses on detecting and classifying entities in Arabic text across multiple categories.
The work included dataset preparation and preprocessing, training the AraBERT model with fine-tuning, evaluating model performance using classification metrics, and experimenting with different optimization strategies to improve accuracy.
The project demonstrates proficiency in handling Arabic NLP tasks and applying transformer-based models effectively.

AI-Powered Recipe Generator

This project implements an AI-powered recipe generator that creates meal recipes based on user-provided criteria such as ingredients or preferences.
The system uses a trained language model to produce coherent and relevant recipe outputs given user input.
The solution also includes a calorie estimation component to provide nutritional insights for the generated recipes.
The project demonstrates practical skills in Natural Language Generation and building end-to-end AI applications.

Multi-Style Anime Image Transformation using GAN and Diffusion Models

This project implements a multi-style image transformation system that converts input images into various artistic and anime-inspired styles using GANs and diffusion-based models.

The system combines traditional Generative Adversarial Networks (GANs) with Stable Diffusion techniques to achieve high-quality style transfer results.

The project involved image preprocessing, model integration, style conditioning, and output quality optimization.

It demonstrates practical experience in Computer Vision, generative modeling, and diffusion-based image synthesis workflows.