Contact Management System

Last Updated on May 3, 2021

About

A Mini Project in Python Contact Management System is a fully functional GUI application with amazing widgets. It is similar to the contact manager in cell phones. In this project, you can add, view, edit, search and delete contacts. All added and edited records are saved in a database(sqlite3) and this project also uses multithreading.

You can list contacts by name, phone no., nickname. Sqlite3 database has been used to record all data.


 The key features of contact management system are listed below:

  • Add new contacts: with information such as name, phone number, nickname.
  • List all contacts: lists all the contacts stored in database with their respective contact details
  • Search contacts: based on name and phone number
  • Edit contacts: edit information given while adding the contacts – name, phone number, nickname
  • Delete contacts: deletes contacts from database


More Details: Contact management system
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Image Classification Using Machine Learning

Last Updated on May 3, 2021

About

This is a prototype that shows the given specific image will belong to which category. Here any images can be taken to classify the difference. The main theme is to predict that the given image will belong to which category we had considered.

In this prototype I downloaded images of three different dog breeds named Doberman, golden retriever and shihtzu. The first step is to preprocess data which basically means converting the images into an numpy array and this process named as flattening the image. This numpy array should be the input of the image.


After preprocessing the data, the next step is to check the best suitable parameters for the machine learning algorithm. After getting the parameters, I passed them into the machine learning algorithm as arguments and fit the model. From Sklearn import classification report, accuracy score, confusion matrix which helps us to get brief understanding about our model. The model can be loaded into file using pickle library.


Now the last step is to predict the output. For this I took a input field which takes a URL as an input. The URL should be the image of the dog for which the output is predicted. In the same way we have to flatten the image into a numpy array and predict output for that. The output will show the predicted output that is which breed that the dog belongs to and the image we are checking the output for.


The main theme of this project is to train the computer to show the difference between different classes considered.

More Details: Image Classification using Machine learning

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Hacktube

Last Updated on May 3, 2021

About

A Chrome extension that fights online harassment by filtering out comments with strong language.

Inspiration

YouTube is a place for millions of people to share their voices and engage with their communities. Unfortunately, the YouTube comments section is notorious for enabling anonymous users to post hateful and derogatory messages with the click of a button. These messages are purely meant to cause anger and depression without ever providing any constructive criticism. For YouTubers, this means seeing the degrading and mentally-harmful comments on their content, and for the YouTube community, this means reading negative and offensive comments on their favorite videos. As young adults who consume this online content, we feel as though it is necessary to have a tool that combats these comments to make YouTube a safer place.

What it does

HackTube automatically analyzes every YouTube video you watch, targeting comments which are degrading and offensive. It is constantly checking the page for hateful comments, so if the user loads more comments, the extension will pick those up. It then blocks comments which it deems damaging to the user, listing the total number of blocked comments at the top of the page. This process is all based on user preference, since the user chooses which types of comments (sexist, racist, homophobic, etc) they do not want to see. It is important to note that the user can disable the effects of the extension at any time. HackTube is not meant to censor constructive criticism; rather, it combats comments which are purely malicious in intent.

How we built it

HackTube uses JavaScript to parse through every YouTube comment almost instantly, comparing its content to large arrays that we made which are full of words that are commonly used in hate speech. We chose our lists of words carefully to ensure that the extension would focus on injurious comments rather than helpful criticism. We used standard HTML and CSS to style the popup for the extension and the format of the censored comments.

Challenges we ran into

We are trying to use cookies to create settings for the user which would be remembered even after the user closes the browser. That way anyone who uses HackTube will be able to choose exactly which types of comments they don't want to see and then have those preferences remembered by the extension. Unfortunately, Chrome blocks the use of cookies unless you use a special API, and we didn't have enough time to complete our implementation of that API at this hackathon.

Accomplishments that we're proud of

We are proud of making a functional product that can not only fight online harassment and cyberbullying but also appeal to a wide variety of people.

What we learned

We learned how to dynamically alter the source code of a webpage through a Chrome extension. We also learned just how many YouTube comments are full of hate and malicious intent.

What's next for HackTube

Right now, for demo purposes, HackTube merely changes the hateful comments into a red warning statement. In the future, HackTube will have an option to fully take out the malicious comment, so users’ YouTube comments feed will be free of any trace of hateful comments. Users won’t have to worry about how many comments were flagged and what they contained. Additionally, we will have a way for users to input their own words that offend them and take the comments that contain those words out of the section.

More Details: HackTube

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Cluster Ai

Last Updated on May 3, 2021

About

Explore a galaxy of research papers in 3D space using a state-of-the-art machine learning model.

Inspiration

Search engines like Google Scholar make it easy to find research papers on a specific topic. However, it can be hard to branch out from a general position to find topics for your research that need to be specified. Wouldn’t it be great to have a tool that not only recommends you research papers, but does it in a way that makes it easy to explore other related topics and solutions to your topic?

What it does

Users will input either a text query or research paper into Cluster AI. Cluster AI uses BERT (Bidirectional Encoder Representations from Transformers), a Natural Language Processing model, in order to connect users to similar papers. Cluster AI uses the CORE Research API to fetch research articles that may be relevant, then visualizes the similarity of these papers in a 3d space. Each node represents a research paper, and the distances between the nodes show the similarity between those papers. Using this, users can visualize clusters of research papers with close connections in order to quickly find resources that pertain to their topic.

Test Cluster AI here

Note: Running on CPU based server, deploying your own Django server using instructions in the Source Code is highly recommended. Demo may have delays depending on the query and number of users at any given point. 10-100 papers, but up to 20 papers requested in the query will be optimal.

Check out the Source Code!

How we built it

We used a multitude of technologies, languages, and frameworks in order to build ClusterAI.

  1. BERT (Bidirectional Encoder Representations from Transformers) and MDS (Multidimensional Scaling) with PyTorch for the Machine Learning
  2. Python and Django for the backend
  3. Javascript for the graph visualizations (ThreeJS/WebGL)
  4. Bootstrap/HTML/CSS/Javascript for the frontend

Challenges we ran into

The CORE Research API did not always provide all the necessary information that was requested. It sometimes returned papers not in English or without abstracts. We were able to solve this problem by validating the results ourselves. Getting the HTML/CSS to do exactly what we wanted gave us trouble.

Accomplishments that we're proud of

We worked with a state-of-the-art natural language processing model which successfully condensed each paper into a 3D point.

The visualization of the graph turned out great and let us see the results of the machine learning techniques we used and the similarities between large amounts of research papers.

What we learned

We learned more about HTML, CSS, JavaScript, since the frontend required new techniques and knowledge to accomplish what we wanted. We learned more about the BERT model and dimensionality reduction. The semantic analysis of each paper’s abstract the BERT model provided served as the basis for condensing each paper into 3D points.

What's next for Cluster AI

We can add filtering to the nodes so that only nodes of a given specification are shown. We can expand Cluster AI to visualize other corpora of text, such as books, movie scripts, or news articles. Some papers are in different languages; we would like to use an API to convert the different languages into a person’s native language, so anyone will be able to read the papers.

More Details: Cluster AI

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Image Processing

Last Updated on May 3, 2021

About

What is image processing ?

The aim of pre-processing is an improvement of the image data that suppresses unwilling distortions or enhances some image features important for further processing, although geometric transformations of images (e.g. rotation, scaling, translation) are classified among pre-processing methods here since similar.

Preprocessing refers to all the transformations on the raw data before it is fed to the machine learning or deep learning algorithm. For instance, training a convolutional neural network on raw images will probably lead to bad classification performances.


convolutional neural network

convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data. ... A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.


CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.


Problem description:  Case study , We have a dataset has 3 subfolders inside it are single prediction contains only 2 images to test the model and prediction so that we know our CNN model is working , test set with 2000 images (1000 of dogs and 1000 of cats) where we will evaluate our model , training set contains 8000 images 4000 of cats and 4000 of dogs as we are going to train our CNN model on these images of dogs and cats . so basically our CNN model is going to predict whether the image given is of a a cat or a dog. By generating random number on google then choosing the image .  Eg: cat


Prediction for CAT

PREDICTION FOR DOG

More Details: IMAGE PROCESSING

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E-Commerce

Last Updated on May 3, 2021

About

- Implement E-Commerce Web App which had started from 13 November to 12 December 2020.

- In this Web App user can able to purchase the various products which is available in Database and virtually placing the orders.

- Applied Python , DJANGO , Bootstrap and JavaScript ,especially focus on backend to explore the skill and knowledge of backend.

- This Web App consist of proper Database functionality which help to implement different function and operations.

- User can able to ask any query regarding products and processes , also there is special search functionality in which user can able to filter their required products by simply search on there.

- There are pop-down Cart which shows the product available in the Cart which is select by the user with two buttons in the bottom, one is Checkout and another is Clear Cart.

- On clicking the Checkout button it render the user to the place order page in which user should give all their details by filling the blanks input and finally place the Order.

- All the orders detail of user, orders and query will be stored in the Databases with their Username and Date.

- On clicking the Clear Cart button , it clear all the product which is select by the user for purchase.

More Details: E-Commerce

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