2015cs111_yasas-yasas-ranawaka

Yasas Ranawaka

I am a computer science undergraduate student who loves programming and problem-solving, like to learn emerging and new technologies.


Email: ranawaka.y@gmail.com 
Phone: 0710112570
Course: CS

Interests:Image Processing, Machine Learning, Operation System, Parallel Computing

Technical Skills: Laravel, CodeIgniter , MySQL, MongoDB, Angular, Nodejs, Git, Ubuntu

Project Experience

Vehicle Management and Tracking System – Internship Project
Domain – Vehicle Management Web Application and Driver’s Mobile Application
Technologies – Laravel, PHP, JavaScript, Ajax, Bootstrap, MySQL, Google APIs, Android Studio
SmarTID – Smart distribution system for a Telecommunication agency – 2nd Year Group Project
Domain – Web Application for office staff use and Selling Mobile Application for Field Sales Executives
Technologies – PHP, JavaScript, Ajax, Bootstrap, MySQL, Android Studio
Lab Reservation System – Web Development Individual Project
Domain – Lab reservation web application
Technologies – Angular, Nodejs, Bootstrap, MongoDB
Automated Car Park System
Domain – Standalone Application for car park management
Technologies – C# , MySQL

Work Experience

• Software Engineering Intern – Mobile Learning Group of UCSC
August 2018 – January 2018 (6 months)

Achievements | Awards

• Received University of Colombo Colors for Badminton in year 2016
• University of Colombo Badminton Team Member – 1st Runner-up SLUG 2016

Final Year Project

Future Traffic! A Prediction Model with Multidimensional Data

• Predicting the traffic is a difficult process since it depends on many types of socio-contextual data. Some of such data are not the form that can be used to forecast the traffic. Such data should be transformed due to a form that could be integrated into a single model for forecasting future traffic. This study is to generate a traffic prediction model in order to forecast the future traffic situation and this will be experimented only on Sri Lankan context. The project goal refers to use deep learning Neural Network methods to solve this traffic forecasting problem and also to get better performances.