Projects

Last Updated on May 3, 2021

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Project 1: Title- Shared memory parallelism of LSSVM

Description: Least Squares Support Vector Machines(LS-SVM) have been widely applied for classification and regression with comparable performance to SVMs. The LS-SVM model lacks sparsity and is unable to handle large scale data due to computational and memory constraints. We propose to implement a method that overcomes the problem of memory constraints and high computational costs resulting in sparse reduction to LSSVM models. The approximation of the model (Nystrom approximation with a set of prototype vectors) allow to scale the models to large scale datasets.


Project 2: Title- Covid 19 Fake news detection

Description: This task focuses on the detection of COVID19-related fake news in English. The sources of data are various social-media platforms such as Twitter, Facebook, Instagram, etc. Given a social media post, the objective of the shared task is to classify it into either fake or real news

Project 3: Title: Housing Price Competiton

Description: To predict the housing price for a given set of dataset




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Enterprise Ai

Last Updated on May 3, 2021

About

Enterprise AI is about enhancing the customer satisfaction index and to ensure customer stickiness to your organization. By infusing emerging technologies like artificial intelligence to engage and retain the set of customers. Using AI algorithm, we should address the use case. The business operation

processes like determining the customer sentiments from various different media like - Social media, Audio Calls, Video Calls, Images, Emails & Chats, interact with customers to provide quick and effortless

solutions, analyze and learn from buying behaviour to generate next best offer, ascertain customer retention and ensure lesser churn, derive AI-based Customer Segmentation, manage customer

touchpoints, evaluate customer feedback and engage with the customers. We provide a membership card to all the customers who purchase stocks in the store. By scanning the QR code the customer can fill the

feedback. Through the user can easily complete the feedback (Bad, Good, Very good) after purchasing. We are providing three categories (Bronze, Gold and Platinum) for our customers to categorize their

purchasing list to calculate the purchasing efficiency based on their quality ,they purchase. The customer who gives feedback as very good, they come under platinum category, best offers are provided to

them (free purchase for Rs.1000). Notifications will be sent to customers through the messages about the new products available along with its price. Best offers are also provided on festival occasions. We classify the feedback using classification algorithms like random forest to get the positive and negative feedbacks.

Negative feedback will be collected and rectified soon. Through this approach, the shopkeeper is able to get clear feedback about his shop easily.



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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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Smart Health Monitoring App

Last Updated on May 3, 2021

About

The proposed solution will be an online mobile based application. This app will contain information regarding pre and post maternal session. The app will help a pregnant lady to know about pregnancy milestone and when to worry and when to not. According to this app, user needs to register by entering name, age, mobile number and preferred language. The app will be user friendly making it multi-lingual and audio-video guide to help people who have impaired hearing or sight keeping in mind women who reside in rural areas and one deprived of primary education. The app will encompass two sections pre-natal and post- natal.

           In case of emergency i.e. when the water breaks (indication) there will be a provision to send emergency message (notification) that will be sent to FCM (Firebase Cloud Messaging), it then at first tries to access the GPS settings in cell, in case the GPS isn’t on, Geolocation API will be used. Using Wi-Fi nodes that mobile device can detect, Internet, Google’s datasets, nearby towers, a precise location is generated and sent via Geocoding to FCM, that in turn generates push notifications, and the tokens will be sent to registered user’s, hospitals, nearby doctors, etc. and necessary actions will be implemented, so that timely            help will be provided

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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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Hacksat

Last Updated on May 3, 2021

About

Imagine a satellite which enables anyone to avoid thinking in data transfer, energy and all of those nuisances.

What it does

HackSat consists of a prototype for a CubeSat blueprint which will allow anyone who wants to do any experimentation up in outer space to avoid worrying about how to send data or how to provide energy and start thinking about which data will be sent and when they will sent it.

It is also worth noticing that everything will be released under an Open Source License

How we built it

We designed the basic structure, based on the CubeSat specs provided by California Polytechnic State University and used by NASA to send low cost satellites.

We printed the structure by means of a couple 3d printers.

We handcrafted all electronics by using a combination of 3 Arduinos, which required us to search for low consuming components, in order to maximize the battery power, we also work on minimize the energy consumption for the whole satellite.

We opted to use recycled components, like solar panels, cables, battery, converter...

We worked a lot on the data transfer part, so it allows the Sat to be sleeping by the most part, on an effort to increase even more the battery life.

And almost 24hours of nonstop work and a lot of enthusiasm!!

Challenges we ran into

We find mostly challenging the electronics, because our main objective was to get the optimal energy out of our battery and avoid draining it too fast.

Another point worth mentioning was the data transfer between the experiment section and the Sat section, because we wanted to isolate each part as much as possible from the other, so the experiment just need to tell the Sat to send the data and nothing more.

Accomplishments that we are proud of

We are very proud to have accomplished the objective of making a viable prototype, even though we have faced some issues during these days, nonetheless we managed to overcome all of those issues and as a consequence we have grown wiser and our vision has become wider.

What we learned

During the development for HackSat, we have learned a lot about radio transmission, a huge lot about serial port and how to communicate data between 3 different micros, using 2 different protocols.

What's next for HackSat

The first improvement that should be made is fix some issues we encountered with the measures of our designs, which have required some on site profiling.

Another obvious improvement is update the case so it is made of aluminium instead of plastic, which is the first blocking issue at the moment for HackSat to be launched.

Finally, we would change the hardware so it has more dedicated hardware which most likely will allow us to optimise even the battery consumption and global lifespan for the Sat.

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