"You become what you think!"
Academic Background[edit | edit source]
I am a student at University of Western Ontario, currently pursuing my MEng program offered by department of ECE from May 2021. My master's program is focussed on software engineering. I have completed my Bacheolar of Engineering(BE) from Anna University where I did a final year capstone project on disaster detection using Artificial neural network a deep learning technique to predict the disaster in advance by giving room for the goverment to evacuate people from the high risk zone.
Industry Background[edit | edit source]
I have worked in TATA CONSULTANCY SERVICES (TCS) as a Big Data engineer for 2 years. During my time in the industry I have been actively involved in several fascinating projects that deals with big data and data analytics. My core job requirements involve collection of data from various sources using REST API/ file from MFT and process it using spark/ spark structured streaming for streaming data and store it in a data lake. The data from data lake is accessed for data analytics using machine learning or deep learning to provide insights to the buisness/ management team. I have worked in a dedicated distributed cluster in a production environment handling several GB's of live data per minute.
Research Interests[edit | edit source]
- Artificial Intelligence
- Computer vision
- Machine Learning
- Deep Learning
- Big data analytics
Projects[edit | edit source]
Blood Cell type classificaion[edit | edit source]
Diagnosis of blood-based diseases involves the identification and classification of blood samples into its subtypes namely Neutrophil, Monocyte, Lymphocytes, and Eosinophil, etc. These four types of cells are found in various proportions in sick and non-diseased blood. These fundamental statistics are frequently used by doctors to determine the type and severity of this condition. Automatic detection and classification of blood samples into its subtypes reduce the manual load in the testing process of lab technicians. This project acts as a tool that enables the lab technicians to easily detect the blood subtypes and enable the faster detection of blood-based diseases.
Proactive Disaster Detection[edit | edit source]
The main concept of this paper is to predict the natural disasters beforehand. With the help of deep learning one can apply statistical models to historical data to predict the future outcomes. With the help of GIS data of tectonic plates and occurred earthquakes we can train a model to predict the future earthquakes and tsunamis. The proposed system helps to predict disasters well in ahead of time which can give suitable time for evacuation and preparation for the disasters.
Citation:
K. L. Sreeram, V. M. Sundharam, and G. Bharathwaj, "Proactive Disaster Detection", 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-5, DOI: 10.1109/INCET49848.2020.9154174 click here
Publications[edit | edit source]
- K. L. Sreeram, V. M. Sundharam, and G. Bharathwaj, "Proactive Disaster Detection", 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-5, DOI: 10.1109/INCET49848.2020.9154174 click here