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  • Created 9 months ago
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1

realtime_violencedetection

Python
3
star
2

SkimLit

In this project, we're going to be putting what we've learned into practice. More specificially, we're going to be replicating the deep learning model behind the 2017 paper [*PubMed 200k RCT: a Dataset for Sequenctial Sentence Classification in Medical Abstracts*](https://arxiv.org/abs/1710.06071). When it was released, the paper presented a new dataset called PubMed 200k RCT which consists of ~200,000 labelled Randomized Controlled Trial (RCT) abstracts. The goal of the dataset was to explore the ability for NLP models to classify sentences which appear in sequential order. In other words, given the abstract of a RCT, what role does each sentence serve in the abstract? ![Skimlit example inputs and outputs](https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/images/09-skimlit-overview-input-and-output.png) *Example inputs ([harder to read abstract from PubMed](https://pubmed.ncbi.nlm.nih.gov/28942748/)) and outputs ([easier to read abstract](https://pubmed.ncbi.nlm.nih.gov/32537182/)) of the model we're going to build. The model will take an abstract wall of text and predict the section label each sentence should have.* ### Model Input For example, can we train an NLP model which takes the following input (note: the following sample has had all numerical symbols replaced with "@"): > To investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( OA ). A total of @ patients with primary knee OA were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks. Outcome measures included pain reduction and improvement in function scores and systemic inflammation markers. Pain was assessed using the visual analog pain scale ( @-@ mm ). Secondary outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index scores , patient global assessment ( PGA ) of the severity of knee OA , and @-min walk distance ( @MWD )., Serum levels of interleukin @ ( IL-@ ) , IL-@ , tumor necrosis factor ( TNF ) - , and high-sensitivity C-reactive protein ( hsCRP ) were measured. There was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , PGA , and @MWD at @ weeks. The mean difference between treatment arms ( @ % CI ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively. Further , there was a clinically relevant reduction in the serum levels of IL-@ , IL-@ , TNF - , and hsCRP at @ weeks in the intervention group when compared to the placebo group. These differences remained significant at @ weeks. The Outcome Measures in Rheumatology Clinical Trials-Osteoarthritis Research Society International responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ). Low-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee OA ( ClinicalTrials.gov identifier NCT@ ). ### Model output And returns the following output: ``` ['###24293578\n', 'OBJECTIVE\tTo investigate the efficacy of @ weeks of daily low-dose oral prednisolone in improving pain , mobility , and systemic low-grade inflammation in the short term and whether the effect would be sustained at @ weeks in older adults with moderate to severe knee osteoarthritis ( OA ) .\n', 'METHODS\tA total of @ patients with primary knee OA were randomized @:@ ; @ received @ mg/day of prednisolone and @ received placebo for @ weeks .\n', 'METHODS\tOutcome measures included pain reduction and improvement in function scores and systemic inflammation markers .\n', 'METHODS\tPain was assessed using the visual analog pain scale ( @-@ mm ) .\n', 'METHODS\tSecondary outcome measures included the Western Ontario and McMaster Universities Osteoarthritis Index scores , patient global assessment ( PGA ) of the severity of knee OA , and @-min walk distance ( @MWD ) .\n', 'METHODS\tSerum levels of interleukin @ ( IL-@ ) , IL-@ , tumor necrosis factor ( TNF ) - , and high-sensitivity C-reactive protein ( hsCRP ) were measured .\n', 'RESULTS\tThere was a clinically relevant reduction in the intervention group compared to the placebo group for knee pain , physical function , PGA , and @MWD at @ weeks .\n', 'RESULTS\tThe mean difference between treatment arms ( @ % CI ) was @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; @ ( @-@ @ ) , p < @ ; and @ ( @-@ @ ) , p < @ , respectively .\n', 'RESULTS\tFurther , there was a clinically relevant reduction in the serum levels of IL-@ , IL-@ , TNF - , and hsCRP at @ weeks in the intervention group when compared to the placebo group .\n', 'RESULTS\tThese differences remained significant at @ weeks .\n', 'RESULTS\tThe Outcome Measures in Rheumatology Clinical Trials-Osteoarthritis Research Society International responder rate was @ % in the intervention group and @ % in the placebo group ( p < @ ) .\n', 'CONCLUSIONS\tLow-dose oral prednisolone had both a short-term and a longer sustained effect resulting in less knee pain , better physical function , and attenuation of systemic inflammation in older patients with knee OA ( ClinicalTrials.gov identifier NCT@ ) .\n', '\n'] ```
Jupyter Notebook
3
star
3

EDA

Data Analysis on various Cryptocurrencies
2
star
4

AiYog

An AI built to make yoga easy and an instructor who keep you in the right path and posture to do yoga.
JavaScript
2
star
5

boston_house_price_prediction

Jupyter Notebook
2
star
6

First-project

Text-based-application
Python
2
star
7

BootCamp_Assignment

CSS
2
star
8

iNueronFSDS2.0-Assessment

This repo contains solutions of the assessment given by the FSDS2.0 Course
Jupyter Notebook
2
star
9

hackathon

HTML
2
star
10

ExpenseApp

Dart
2
star
11

mlproject

Jupyter Notebook
2
star
12

test

2
star
13

Portfolio

JavaScript
2
star
14

flask1

Python
2
star
15

invoiceSharing

2
star
16

autoML-Model

This is a Machine Learning model which can create small size Machine Learning model with also providing data analysis of the Dataset.
Python
2
star
17

ChatApp

Dart
2
star
18

rollDice

A simple flutter application for any board game that can utilize the Dice with 6 phases, this application shows a dice and generates a random face of the dice to play fair n square.
C++
2
star
19

Billboard-Analysis

JavaScript
2
star
20

LLM-s

HTML
2
star
21

OCR_Detection

This project will detect the captcha from an image captcha
Jupyter Notebook
2
star
22

mlflow_project

2
star
23

Eye-Blink-Detection

This is a minor project of Eye Blink detection where we used Python as language and tools like OpenCV, Dlib and some computer vision concepts
Python
2
star
24

V-Library

SCSS
2
star
25

Library-Management

Book Management System
Python
2
star
26

Saurabh7Goku

Config files for my GitHub profile.
2
star
27

flask

Python
2
star
28

ViolenceDetection_InVideo

Jupyter Notebook
2
star
29

Movie-Review-Transparency

Overview If we are planning on going out to see a movie, how well can we trust online reviews and ratings? **Especially** if the same company showing the rating **also** makes money by selling movie tickets. Do they have a bias towards rating movies higher than they should be rated?
Jupyter Notebook
2
star
30

Spell_Check

This project is based on getting a word from user and checking its spelling
Python
2
star
31

BostonHousePrediction

Jupyter Notebook
2
star
32

Bulldozers_future_price_prediction

# 🚜 Predicting the Sale Price of Bulldozers using Machine Learning In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers. Since we're trying to predict a number, this kind of problem is known as a **regression problem**. The data and evaluation metric we'll be using (root mean square log error or RMSLE) is from the [Kaggle Bluebook for Bulldozers competition](https://www.kaggle.com/c/bluebook-for-bulldozers/overview).
Jupyter Notebook
2
star
33

Deep_Learning_Project

Python
2
star
34

mlflow_Dagshub

Python
2
star
35

Advanced-stable-diffusion-model

Python
2
star
36

eCommerceAnalysis

2
star
37

fight_violence_Detection

2
star
38

Sales_data_analysis

2
star
39

Multi_Model-Chat-App

Jupyter Notebook
2
star
40

Nutritionist_AI

PowerShell
2
star
41

SavantGen-AI

An Advanced Artificial Intelligence Build using the Gemini-Pro Generative Model.
Dart
2
star
42

ATS_Resume_Checker

Python
1
star
43

anomalyDetection-Flask_FrameWork

Python
1
star