Quoting my advisor Prof Jerry Zeyu Gao , this field explores on detecting the graffiti crimes accross the city of San Jose. In this we aim to build a model to detect graffiti on walls. Using machine learning to learn patterns about graffiti from images, will automate the process of detecting the location of the graffiti and the model which will be able to estimate the area of the grafitti. The model will be evaluated and the parameters will be tested for further tweaking. Detection solutions consists of four steps, Image Acquisition, Image Processing,Graffiti Detection and tracking and Drone Control for Graffiti Tracking.
A/B testing Tool compares two versions of a webpage or an app against each other to determine which performs better. A/B testing is the great method for determining the best online marketing strategies for your business. A/B split testing is a method of statistical hypothesis testing which contains two variants and determines which variant performs better.I have worked on the Significance Analysis in the context of Conversion Rates method which determines which variant is better. Even so, I aim to build a even more effective solution using Significance Analysis in the context of analysing p-value.
The fast growth of Cloud enabled mobile sensor applications have massively replaced the legacy IT Cloud implementations, generating a revenue of billions of US Dollars. Thus mobile sensors have been used extensively in all walks of our lives including areas like agriculture, transportation etc. The concept of mobile sensor cloud through a basic architecture is considered and along with the various applications of mobile sensors in healthcare sector, environmental monitoring, transportation of smart cities. Our objective is to explore the current trend involved in Cloud enabled mobile sensor services.