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Masoumeh Chapariniya's Projects

basic_vqa icon basic_vqa

Pytorch VQA : Visual Question Answering (https://arxiv.org/pdf/1505.00468.pdf)

bmmal icon bmmal

[ACMMM 2023] BMMAL: Towards Balanced Active Learning for Multimodal Classification

crnn-keras icon crnn-keras

CRNN (CNN+RNN) for OCR using Keras / License Plate Recognition

examples icon examples

Example code and applications for machine learning on Graphcore IPUs

fastformer icon fastformer

A pytorch &keras implementation and demo of Fastformer.

hed-document-detection icon hed-document-detection

A Pytorch Implementation of HED (Holistically-Nested Edge Detection algorithm) for Document Detection

lp-ocr icon lp-ocr

license plate detection and recognition in unconstrained scenarios

lprnet_pytorch icon lprnet_pytorch

Pytorch Implementation For LPRNet, A High Performance And Lightweight License Plate Recognition Framework.

onn icon onn

Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)

opencv-document-scanner icon opencv-document-scanner

An interactive document scanner built in Python using OpenCV featuring automatic corner detection, image sharpening, and color thresholding.

parsivar icon parsivar

A Language Processing Toolkit for Persian

pcos-gene-expression-data-analysis icon pcos-gene-expression-data-analysis

This repository contains all material related to the project done as a part of the course Algorithmic Approaches to Computational Biology (CS6024) in the Fall 2020 semester.

pcosprediction icon pcosprediction

Polycystic Ovary Syndrome (PCOS) is a widespread pathology that affects many aspects of women's health, with long-term consequences beyond the reproductive age. The wide variety of clinical referrals, as well as the lack of internationally accepted diagnostic procedures, have had a significant impact on making it difficult to determine the exact etiology of the disease. The exact histology of PCOS is not yet clear. It is therefore a multifaceted study, which shares genetic and environmental factors. The aim of this project is to analyse simple factors (height, weight, lifestyle changes, etc.) and complex (imbalances of bio hormones and chemicals such as insulin, vitamin D, etc.) factors that contribute to the development of the disease. The data we used for our project was published in Kaggle, written by Prasoon Kottarathil, called Polycystic ovary syndrome (PCOS) in 2020. This database contains records of 543 PCOS patients tested on the basis of 40 parameters. For this, we have used Machine Learning techniques such as Logistic Regression, Decision Trees, SVMs, Random Forests, etc, A detailed analysis of all the items made using graphs and programs and prediction using Machine Learning Models helped us to identify the most important indicators for the same.

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