Pinned Repositories
20210569-machine-learning-cw
Machine learning coursework second year-second semester
BuyAnthying.com_E-commerce_Website
This is a website created using HTML, CSS and JavaScript Languages.
cricket-project
CutiCare
A cross-platform skin disease detection mobile application built with Flutter and Firebase that uses deep learning to identify up to five common skin diseases. DermNet dataset was employed with a transfer learning approach using the ResNet-50 model.
DL-CW
Edge-AI-Project
Our project aims to enhance traditional door lock systems by introducing a Facial Recognition Door Lock System with Intruder Detection, providing improved security and user-friendly access control.
Virus_Model
A virus model developed using Netlogo to demonstrate how a virus is spread based on the population, infectiousness percentage of the virus, etc..
OOD_GroupFuelSystem_Final
This is the Group OOD assignment submission. Members: Anuttara Rajasinghe - 20210216, Lakshani Nissanka - 20210570, Kavindya Koralegei - 20210575, Suvini Viduneth - 20210569
DSGP_Group20
DL-CW
This is a Group Coursework for the module Deep Learning. Using the Yelp Review Dataset, our Group is attempting a Sentiment Analysis using a Supervised and an Unsupervised Deep Learning Models.
suviniV's Repositories
suviniV/Edge-AI-Project
Our project aims to enhance traditional door lock systems by introducing a Facial Recognition Door Lock System with Intruder Detection, providing improved security and user-friendly access control.
suviniV/DL-CW
suviniV/20210569-machine-learning-cw
Machine learning coursework second year-second semester
suviniV/Virus_Model
A virus model developed using Netlogo to demonstrate how a virus is spread based on the population, infectiousness percentage of the virus, etc..
suviniV/BuyAnthying.com_E-commerce_Website
This is a website created using HTML, CSS and JavaScript Languages.
suviniV/cricket-project
suviniV/CutiCare
A cross-platform skin disease detection mobile application built with Flutter and Firebase that uses deep learning to identify up to five common skin diseases. DermNet dataset was employed with a transfer learning approach using the ResNet-50 model.