Project: ‘Cloud Based TASKI Cleaning Machine’
Technologies: Thingworx and Raspberry Pi
It is a prize winning IoT application which aims at modernization of existing TASKI floor cleaning machines. It migrates data from certain components of the TASKI machine to a cloud platform which analyses, processes the data and provides a dashboard to view parameters of the machine. Features are GPS tracking, Dashboard for Analysis, Battery Management, Theft alerts and other real time alerts (SMS/ e-mail) on over-speeding, overheating and low charging of battery, theft alerts, etc. It is an end-to-end IOT solution made in Thingworx platform and a Raspberry Pi device which reads sensor data and transfers it to the platform.
Project: ‘Connected Car-Internet of Things’
Technologies: Thingworx and Raspberry Pi
It is an IoT application created which aims at modernization of existing carss. It migrates data from certain components of a car to a cloud platform which analyses, processes the data and provides a dashboard to view parameters of the car. Features are GPS tracking, Journey Analysis, Diagnostic Services, Predictive Maintenance, Driver Monitoring, Navigation, Fuel Management, Theft alerts and other real time alerts (SMS/ e-mail) on over-speeding, overheating of engine, theft alerts, etc. It is an end-to-end IOT solution made in Thingworx platform and a Raspberry Pi device which reads sensor data and transfers it to the platform.
Project: ‘Gesture Recognition for Mood Detection’
Technologies: Java, MATLAB and MYSQL
It is an award winning BE project in the Image Processing domain for an application which aims to assist a psychologist/ doctor to diagnose a patient facing depression. It has a simple interface to detect an individual’s facial expression to trace the possibility of any mood disorder in the individual. It uses a dataset of images to classify facial expressions. The project can be utilized in hospitals, companies, schools, colleges, etc. for the motive of tracking an individual’s mental health or performance in the presence of a mood disorder and hence, help in diagnosis.
Project: ‘Cricket Fantasy League Game’
Technologies: Java and MySQL
It is a multi-player game for registered users who participate in an auction for buying a squad of players. They select 11 players for every upcoming cricket match of the tournament (Ex. World Cup, IPL tournament). Users get points based on the performance of the players they own in the match. The points are summed up for the whole tournament to decide the winner.
Project: 'Recyclone Application for Recycling Company'
Technologies: Java and MySQL
It is a grade XII project for a company which acts as an intermediate for Recycling of products. It is a project used for processing customer and client relationships through buying and selling of recyclable materials.
Project: 'Chargeback Calculation in Stock Market'
Technologies: Java, HTML, JavaScript, JSP, Servlets and Oracle
It is a web application used for determining portfolio transaction costs driven by client cash flows, and to charge those costs back to the appropriate clients. Chargeback is calculated by the Bank Representative at the time of buying or selling a stock for a client or for a fund irrespective of profit or loss.
Project: 'Doodle - Appointment and Meeting Scheduler web application'
Technologies: PHP, MySQL, CSS and HTML
Developed a web application that schedules appointments using a polling system to decide the final meeting time. It automatically selects the best time to meet based on the votes in the poll. It is a simple and intuitive web application that helps decide the meeting time by coordinating efficiently between people.
Project: 'Grocery Sales Forecasting - Supermarket Company'
Technologies: Python and Scikit Learn
Developed an application to forecast product sales using ensemble of machine learning algorithms like, Artificial Neural Networks, Deep Learning, Gradient Boosting, Random Forest, AdaBoost for over 200,000 different products. Predicted unit sales for a dataset of over 5 years with an average error of 0.30 on the mean squared error function.