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Amirhossein Entezari's Projects

foodclub icon foodclub

Back-end project for a restaurant application with Django

ir-block-sorted-based-ndexing icon ir-block-sorted-based-ndexing

An efficient implementation of the Blocked Sort-Based Indexing (BSBI) algorithm for building inverted indexes in large-scale information retrieval systems.

ir-boolean-search icon ir-boolean-search

Information Retrieval Project: This repository contains a suite of Python tools and a Jupyter notebook for simple data processing, inverted indexing, and boolean querying, complemented by detailed experimental analyses.

ir-document-ranking icon ir-document-ranking

An information retrieval system for document ranking. Implementation and evaluation of Okapi BM25, Cosine Similarity, and Language Models for document ranking and retrieval. Includes precision-recall evaluation metrics and a detailed project report.

ir-wildcards-and-spell-correction icon ir-wildcards-and-spell-correction

A project for advanced Information Retrieval (IR) course for processing, indexing, and querying text data including wildcards and spell correction. This project includes data preprocessing tools, efficient indexing mechanisms, and robust query handling to retrieve relevant information. Explore Jupyter notebooks for experiments and detailed report.

persian-literature-classifier icon persian-literature-classifier

Persian Author Classification: A machine learning project leveraging natural language processing techniques to classify Persian texts by author. The repository contains Jupyter notebooks for both model training and data scraping, a detailed project report, and supplementary source code.

polyp-segmenation-unet icon polyp-segmenation-unet

Polyp segmentation using U-Net in PyTorch. This project utilizes the Kvasir-SEG dataset to train and evaluate a deep learning model for accurate segmentation of polyps in endoscopic images.

text-classification-enhancements icon text-classification-enhancements

Enhancing Text Classification in Information Retrieval: Evaluating the effectiveness of Naive Bayes classifiers with various word embeddings (Word2Vec, GloVe, FastText) for natural language processing tasks. This project explores performance differences and offers insights into embedding impacts on text classification.

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