Sentiment Analysis from Health and Fitness app reviews - BERT

The objective of this project is sentiment analysis (i.e., Positive, Neutral, Negative) from the popular health and fitness app reviews. For this task, app reviews are collected from the google play store. 10 popular health and fitness apps are chosen. All around 12000 most recent reviews are collected. Ratings are considered as the measure/label of positive, negative, and neutral sentiment of the reviews. The collected data are preprocessed and trained using a transformer model.

April 6, 2017

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.