License Number Extraction - YOLO v4, Vision API (GCP)

The objective is to extract the registration/license plate number from different vehicle images. The task is divided into two parts. First, detect the possible ROI(region of interest) - in this case, it is the license plate of vehicles. Second, extract the letters and numbers from the detected region. To separate ROI from the images, object detection model - YOLO v4 is used. YOLO v4 (You only look once) is a family of one-stage object detectors that are fast (i.

April 5, 2017

PhenomDetect: Detection of Air Hazards in the U. S.- SVM, Random Forest, Gradient Boost, XGBoost, KNN, LSTM, GRU, Tableau

This project is a team effort of Team ‘Vesper’ participated in the NASA International Space Apps Challenge'20. Our team took on the challenge of the automatic air hazard detection because we were inspired by the idea of building a tool that could potentially save many lives just by automatically analyzing data from a variety of sources and putting this analysis into the hands of key decision-makers, as well as the general public.

April 5, 2017

Credit Card Fraud Detection - Autoencoder, KNN, SVM, MLP

The purpose of this project is to leverage machine learning and find out fraudulent transactions of the credit cards. The idea is to prevent fraudulent activity only by analyzing credit card transaction data. The transaction data that has been used is highly imbalanced, having only 0.2% fraud cases. The overall challenge is to make a supervised model that can detect fraud transactions from normal transactions. The data can be found in the following link: