Language Translator Using Python

Last Updated on May 3, 2021

About

This project is developed with the help of googletrans library for using google translator to translate language and using Tkinter for GUI.

This project translates one language into the other language having a feautre of Auto detect input language.

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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.

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Daily Planner

Last Updated on May 3, 2021

About

DailyPlanner

Project Title:Daily planner Software Used: Android Studio

Daily planner is java based android applicationThis is daily routine application which will helps us to schedule our daily routine and maintain daily checklist. In this to-do list app, we can update our daily routine as well as weekly tasks. We can also delete and add the task from morning to night and set reminder, we can also see the previous task performed in previous months, weeks and days. We get reminders for a particular task through notification. This is how, we can schedule our tasks as per our timing and will help us to remind and complete every task in an easy and efficient way.Features:

1.Creating Tasks: This feature helps you to create a task. Also, when you create a task there are less chances of forgetting it. This will give you clear idea of how many tasks you have to do.Moreover, daily planners have been a staple for both office and home. By providing sections for every time of the day, it helps you organize everything you need to do in your life, from meetings to important appointments and from spending time with kids to entertainment activities; it assists you with all these things. Daily planners are one of the best methods to address your time management. Planners have daily, weekly and monthly overviews permitting you to pen down all your important tasks and events on your schedule.

2.Setting reminder: This is used to schedule your meetings. And also gives reminder of pending task. Furthermore, these sections are normally large enough for you to write about your commitments, appointments, meetings or anything that you want to accomplish on a specified date.

3.View: Also, it allows you toorganize certain eventsat any day, time or hour, no matter if it is morning or evening. Allowing you to have a track of all events and records, you can manage your time accordingly. If you click on date then it will shows you all the task of that day.

4.Update and Delete:In this app you can update the task by clicking on the task which is displayed on the dashboard. If you don’t want a task that you’ve added before then you can simply delete that task.

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Air Quality Analysis And Prediction Of Italian City

Last Updated on May 3, 2021

About

Problem statement

  • Predict
  • The value of CO in mg/m^3 reference value with respect to the available data. Please assume if you need, but do specify the same.
  • Forecast
  • The value pf CO in mg/m^3 for the next 3 3 weeks on hourly averaged concentration

Data Set Information

located on the field in a significantly polluted area, at road level,within an Italian city. Data were recorded from March 2004 to February 2005 (one year)representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses. Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration

Data collection

0 Date (DD/MM/YYYY)

1 Time (HH.MM.SS)

2 True hourly averaged concentration CO in mg/m^3 (reference analyzer)

3 PT08.S1 (tin oxide) hourly averaged sensor response (nominally CO targeted)

4 True hourly averaged overall Non Metanic HydroCarbons concentration in microg/m^3 (reference analyzer)

5 True hourly averaged Benzene concentration in microg/m^3 (reference analyzer)

6 PT08.S2 (titania) hourly averaged sensor response (nominally NMHC targeted)

7 True hourly averaged NOx concentration in ppb (reference analyzer)

8 PT08.S3 (tungsten oxide) hourly averaged sensor response (nominally NOx targeted)

9 True hourly averaged NO2 concentration in microg/m^3 (reference analyzer)

10 PT08.S4 (tungsten oxide) hourly averaged sensor response (nominally NO2 targeted)

11 PT08.S5 (indium oxide) hourly averaged sensor response (nominally O3 targeted)

12 Temperature in °C

13 Relative Humidity (%)

14 AH Absolute Humidity.

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My Rewards - Alexa Skill

Last Updated on May 3, 2021

About

An Alexa skill to reward kids for good behavior.

Inspiration

After building http://eFamilyBoard.com I decided to purchase an Alexa Show (2nd gen) for comparison. eFamilyBoard has a few nicer features but over all the Alexa is much more powerful and scalable. One heavily used feature was the sticker board on eFamilyBoard that didn't have a comparable skill on Alexa. As a result, I decided to build My Rewards skill to replace it.

What it does

It allows families and teachers to reward kids for good behavior. The user ultimately decides what to do with the rewards. Personally, our kids earn $5 after they've earned 10 total rewards, then they start over. The user can add recipients and give multiple rewards at a time. For example, "Alexa give John 5 stickers" or "Alexa take away 2 stickers from John". And if you don't know what reward type of reward to give or take away you can always simply say "rewards" in the place of the type of reward (e.g. football, sticker, heart, unicorn, truck, cookie, doughnut, etc).

How I built it

I built it with the ASK CLI and Visual Studio Code. I started with the sample hello world app and refactored it to utilize typescript, express, and ngrok to run locally. I also used mocha with chai for unit tests that run and must pass before I can deploy to AWS.

Challenges I ran into

I learned to get stated by taking a course on Udemy but they didn't use typescript and deployed to AWS for every change. That would take FOREVER to debug and build efficiently. I setup a simple express app and use ngrok to route calls to my local machine. This allows me to talk to my Alexa and debug by stepping through the code in VS Code.

Accomplishments that I'm proud of

Project setup, local debugging with typescript, and tests with 90%+ code coverage. Not only does it work for voice but it also supports display templates to show the user what rewards each participant has earned. I was going to add ISP down the road but decided to do it from the start and it ended up being easier than expected. For being my first Alexa app I think the app works extremely well and my kids started to utilize it with no learning curve.

What I learned

Being my first Alexa app I learned a TON. From how to simply use Alexa (still learning tricks) to how to interact with voice commands. I've also never used DynamoDB but the Alexa SDK made that super easy.

What's next for My Rewards

Add support for more languages. My family has been using it for development but exited to see what others think of it.

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Automated Plant Watering System (Iot ,Html)

Last Updated on May 3, 2021

About

The automated plant watering system is a project under the domain                                                          ‘Internet of Things’ that can detect the water requirements of plants using a soil moisture detection sensor and can automatically turn on and off the supply of water accordingly.

The user has the convenience of setting up the system to either automated or manual mode and can check the status of soil and the details of the last time the plant has been watered.

This is accomplished by creating a webserver that contains certain buttons which provides the choice of the mode of supply and provides access to every other detail.

Tools:

1. Raspberry Pi

2. Soil Moisture Sensor

3. Water Pump

4. Relay Module 5V

Languages:

1. Python for Raspberry Pi (flask and psutil libraries)

2. HTML for application interface

 Design and algorithms used:

The code for the project was written in the python language. Various libraries were installed to serve different purposes such as interaction with RPi GPIO, connecting the program to the web server etc. For maintaining the control on entire working, a web page is created and all the actions are controlled using the web interaction page using HTML

Working:

This is the project designed for the Agricultural purpose. In general a person has to monitor the water content in fields and switch on/off the water pump regularly, Using this project the soil moisture sensor itself senses the water content and automatically switches the motor ON if water content is low as per the conditions mentioned. All this procedure is controlled by Raspberry Pi as per the Python Code. Other than this, we have coded such that the last watered time, date also get displayed on the desktop.

    The code contained 4 sections, one of which includes the html code for a web page and the others for running, automated mode and interaction with the web page

My role in the project:

I worked mostly on the coding part in the project, especially the code for establishing the connection between the components using Python 


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