Email: supraja.baskaran@mail.concordia.ca
Languages known:
CGPA - NIL
CGPA - 8.08
Percentage : 96.25%
CGPA - 10
Industry verticals: Underwriting Insurance Framework, Marketing, Government Platform, Customer Service.Worked in: Case Management, Responsive UI Design, Data Model Design, Reports, Accessibility, Mashup Integration, Requirement Analysis and Quality Assurance in Agile Methodology.
Industry Verticals: HRD Framework. Worked in: Automatic reply to the incoming emails using an Email Listener and creation of cases, Case Management.
An automated Knowledge representation and Answer Sheets Evaluator is developed in an Indian Scenario for the students. The basic idea behind the project is to reduce the human errors while evaluating and make the overall process efficient. It uses python and Django framework along with Android Studio which is the end user platform for uploading the answer sheets. These are converted into digital text using OCR (Object Character Recognition) taking help from Tesseract and tokenized with NLTK. The tokenized answer key is compared against the tokenized answer scripts. Marks will be awarded accordingly.
This automated online movie ticket booking application is developed using Pega 8.2. The application provides a platform for the movie theatres as well as the end customers. The theatre administrators provide their movie running schedule times along with the food items available during the corresponding show time. Customers are able to view the shows for specified date and make their booking. Case Management, Data Management, Reporting, and REST integration simulations were used.
A Web and Android integrated Application developed with the motive to make it a common platform for people to donate and receive wasted food, used books, clothes and Blood. The targeted people are the NGOs, Private Social working teams, etc. The application is built as a prototype keeping blood and food.
This is an embedded system using Universal Learning Kit(ULK) developed with C programming. The 16*2 LCD shows the parties in the election. The voter needs to touch the party they wish to vote and the votes are calculated at the backend after every interaction and the winner is decided.
This is a speech emotion recognition model. The main idea behind this work is to recognize emotion from speech. It uses librosa and sklearn libraries developed using python.
This is a Keystroke logger model to capture the keys struct on a keyboard developed using python. This is also implemented for virtual keyboards.