Many measurements have been taken to tackle the COVID-19 pandemic. Among which, wearing a face mask is one way to prevent spreading the virus. This work aims to detect if a person is wearing a mask or not. With this objective, a machine learning model is developed, which leverages transfer learning to detect mask. For collecting human faces with the mask, a subset (1000 images) of the MAFA dataset is used.

Online text and video tutorials are among the most popular and heavily used resources for learning feature-rich software applications (e.g., Photoshop, Weka, AutoCAD, Fusion 360). However, when searching, users can find it difficult to assess whether a tutorial is designed for their level of expertise. TutVis stands for tutorial visualization, which is a Photoshop tutorial browsing system that provides auto-generated information to assist users in the tutorial searching and selection. The provided information is as follows: difficulty level (advanced/beginner), topics covered, length, text complexity, command usage ratio.

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.

The objective of this project is to predict if a customer will get a loan given applicant income, loan amount, loan amount term, credit history, education status, self-employment status, property area, etc. A model is trained using the training data on previous customers’ loan approval history. A web service is created, which runs the trained model in the background. The service presents an interface through which any user can request to get an automated decision/prediction (i.

Heart disease or Cardiovascular disease is one of the biggest causes of mortality (i.e., causing 1 out of 4 deaths in the US) among the population of the world. Therefore, prediction of Cardiovascular disease is considered one of the important subjects in clinical data analysis. However, several contributory risk factors such as diabetes, high blood pressure, high cholesterol, abnormal pulse rate, etc. lead to cardiac arrest. So, the purpose of this work is to predict if any patient has the chance of having heart disease or not.

The objective of this project is to develop a system for physically impaired people who do not have a pair of working hands. The system provides different functionalities to the users: navigate computers using head movements, eye blink for clicking, different head gestures to read documents, and watch multimedia. The system is developed using C#. The head movement is tracked using EmguCV (OpenCV wrapper) and some heuristic calculations. Blink detection is performed using haarcascade of eyes.

A pdf reader that allows a blind person to read documents. The concept is to use the haptic touch on the screen and speak aloud the word on tap. This system provides some smart features e.g., dynamic scrolling, line checking and vibration notification for the line break. The system is built on the Android platform. The smart feature - dynamic scrolling leverages the phone gyroscope reading and can detect tilt. Based on the tilt angle it triggers the scrolling.

eExpense is a smart system that keeps track of everyday expenses. Some of the key functionalities of this app are automatically detecting expenses from text messages, automatically taking input from the receipts, creating a balance sheet, showing history in the calendar view. The system is built on the Android platform. The input from receipts is taken using Tesseract OCR (Optical Character Recognition) engine. Publication Source: Check the publication

A system to replace the traditional mouse and keyboard that only uses hand gestures to control computers. This system focuses on navigating computers, reading documents and controlling multimedia like videos, pictures. This multimodal system uses a leap motion device and a webcam to capture hand movements and different gestures. Real-time tracking of hand movement. (haarcascade, Viola-Jones) Implements the Kalman filter for smoothing. Captures hand gesture with leap motion device.

A security system for computers which runs as the background process and tracks keyboard or mouse inputs. Any unauthorized input triggers this system and sends a notification to the user’s phone through text messages (GSM). Users can use a mobile app (named Pointer Security) to see the live webcam feed, screen streaming and check who is using the computer and can send commands remotely through the app to either shut down or lock the computer.