Glaucoma prediction using Deep neural networks

Successfully developed Convolutional Neural Network (CNN) model that achieved an accuracy rate of 93%. This model has the capability to predict both the presence of Glaucoma and its corresponding stage. The effectiveness of this research was showcased through a presentation at the esteemed ICACECS 2020 conference. The implementation of this advanced CNN model was accomplished using Python and cutting-edge Deep Learning techniques, further underscoring its significance in the field of medical image analysis.

 Tech stack:: Python, Supervised ML

Image character recognition

Image character recognition is used for extracting the characters from any kind of image. It can be used for searching/copying/editing in handwritten documents. 

Tech stack: Java, nosql database, character recognition API

Take-ATrip

Developed a Responsive website to book hotels, and travel packages. This is hosted on heroku.

Tech stack:: MEAN stack, Heroic