Mohammad Mahdi Abootorabi

Contrastive Language-Speech Pretraining for Multilingual Multimodal Information Retrieval CLASP is a multilingual, multimodal representation model designed for audio-text information retrieval. It uses contrastive learning to bridge the gap between language and speech domains and is trained on a diverse speech-text dataset. The model sets new benchmarks in retrieval metrics across multiple languages. It created the shared embedding space for speech and text.	Health Information Retrieval System An advanced search engine designed for efficient retrieval and organization of health and medical-related content. It uses techniques like Boolean search, TF-IDF, FastText, Transformers, and Elasticsearch models. The system features a user-friendly interface and can be deployed on online servers.	Automated Resume Parser A Python package, designed as an automated resume parser, extracts valuable information from multiple CVs and stores it in a CSV file. Currently supporting Persian CVs, it enhances Applicant Tracking Systems’ review capabilities.
News Classification, Summarization, & QA System This project is a comprehensive system for Persian news analysis, equally focusing on classification, question answering, and summarization. It classifies articles using TF-IDF and transformer models, generates relevant questions and answers, and provides fine-tuned summarization of news content.	Bias detection & De‐biasing in Language Models This project focuses on the detection and mitigation of various biases, such as gender and ethnic biases, in diverse language models, including those for multiple languages and multilingual models. A significant aspect of the project is the development of de-biasing techniques specifically tailored for Persian language models.	Personal Information Hider A Python package designed to enhance privacy by concealing personal information in text files. It uses POS tagging and regex-based solutions to effectively remove identifiable details like names, addresses, URLs, email addresses, and more.
Automated Medical Report Generation for Fundus Fluorescein Angiography Images This project presents a new model for automated medical report generation from FFA images. It uses a CNN for visual feature extraction, Cross-modal Memory for feature alignment, and a reinforcement learning algorithm guided by NLG metrics for report generation.	Automated Image Captioning with Recurrent Neural Networks An RNN-based system that generates relevant captions for images. It utilizes pre-trained ResNet50 for image feature extraction and LSTM networks for text generation.	MemoReminder MemoReminder is a mobile application that allows you to share and relive your best moments. It is available on both iOS and Android platforms.
Implementation of Classical Machine Learning Models This project involves the implementation of three classical machine learning models: Linear Regression, Decision Tree, and SVM. The models are trained and evaluated on various datasets, with preprocessing tasks such as PCA and EDA also performed.	Neural Network Implementation from Scratch This project involves the development of a neural network from scratch using NumPy, applied to the CIFAR-10 dataset for image classification.	Drowsiness Detection Embedded System This project is a real-time drowsiness detection system that uses your webcam to monitor signs of drowsiness and phone usage.
Content Management System This project is a versatile content management system that allows you to create and manage various types of libraries (videos, music, books, pictures) and share them with your friends.	Text Exploration This project involves the crawling, exploration, and analysis of text from two websites, Zoomg and Skysports, using NLP methods to extract meaningful insights.