As a data scientist, I like to maintain ties to the research community while working in the data science field. This gives me a firm grip & perspective of the technologies I use in my consulting work: It enables me to apply AI & machine learning and contribute to its intellectual discourse.

Research Projects

Disaster-Location-Mapping-And-Warning-System

The objective of this project is to build a location tagged early warning system, hosted as a web-application, for disasters by analysing the posts on social media sites like Twitter. For this purpose, the system continuously ingests data from social media sites like Twitter, processes it (i.e., using machine learning classification techniques), classifies it into one of the several categories (like damages, need food and resources, people stuck, casualties etc). We also use the geo tagged information from the tweets to identify the latitudes and longitudes on world map in real time. The volume of tweets originating from a particular region are analysed and if the number exceeds certain threshold then we predict a disaster, followed by notifying the relief teams and government about the location.

Disaster prediction system using animal behavior -A Machine Learning predictive model

The purpose of this research is to design a system which can predict disaster/natural calamity or any environmental threat by observing the behavior of animals. The system will study the various animal behavior both psychological and physiological factors and the immediate causes/effects in environment. By feeding the machine learning models with various test and training data, the system will be able to predict the outcome of the disaster. The system keeps monitoring various places and the animal behavior in the region. When the behavior of an animal/group of animals differ the normal behavior test data and it reaches a warning threshold, then the system warns the authorities of a possible natural calamity that is expected to happen in the region.

Digital agriculture: Making the most of machine learning in Agriculture.

The ability of agricultural equipment to think, predict and advise farmers via a variety of artificial intelligence (AI) applications presents Africa with the potential to achieve food security. In this research, I explore various machine learning applications that can help farmers to increase yield, meet the ever-growing demand for agricultural products, and save the environment.