Academia

Research & Education.

Think Big Scholar at the University of Bristol. MSc Data Science with Distinction. IEEE-published researcher with a granted Machine Learning patent.

1 Publications
1 Patents

Education

Academic foundations

University of Bristol

Master of Science (MSc) · Data Science

2020 – 2021First Class with Distinction

Thesis: MedFetch: Converting Unstructured EMR data to Structured data

Large Scale Data EngineeringVisual AnalyticsText Analytics (NLP)Software Development: Programming and AlgorithmsIntroduction to Artificial Intelligence

SRM Institute of Science and Technology

Bachelor of Technology (BTech) · Electronics and Communication Engineering

2016 – 2020First Class with Distinction

Thesis: Smart Glass: Real-Time Leaf Disease Detection using YOLO Transfer Learning

Digitial Signal ProcessingDigital SystemsCommunication SystemsVery Large Scale Integration (VLSI) DesignLinear Integrated Circuits

Publications

Smart Glass: Real-Time Leaf Disease Detection using YOLO Transfer Learning

Amrith Coumaran, Akhash Subramanian Shunmuam, Kritin Rajaram, Sanoj Senthilvelavan

Chennai2020Conference

Having a keen observation and recognizing patterns in minute things is an arduous task in today's fast-paced world. These patterns might contain information that might be of significance to humans. In order to harness these regularities and to predict the activities in the near future, there exists numerous Artificial Neural Network architectures that have high degrees of accuracy. The drawback? These architectures demand GPUs with high processing capabilities, which in turn increase the size of the overall system. Existing systems capable of processing Machine Learning algorithms are not economical, or are not portable. Combining the merits of mobility, and a state-of-the-art You Only Look Once(YOLOv3) object detection system, we present to you, a Smart Glass, capable of highly accurate binary classification of data in real-time. Training the architecture with agricultural data, this wearable device would be able to identify Healthy and Unhealthy plant leaves in Real-time. Researchers could train the architecture with different datasets to obtain solutions to a wide variety of problems in the Agriculture, Healthcare, Automobile Industry, etc.

DOI

Patents

A system and a device for identifying plant diseases

Filed
IN 564969Filed 2020-09-24India

Co-inventors: Amrith Coumaran, Akhash Subramanian Shunmugam, Kritin Rajaram, Sanoj Senthilvelavan

The patents comprise a smart eyewear and a local host. The smart eyewear comprises an image capturing unit which captures a plurality of images of the surroundings and provides a live feed; a communication unit streams the live feed to the local host. Further, a display unit receives and displays a processed stream received from the local host. The local host comprises a data capturing unit and a processing unit. The data capturing unit captures the stream and pre-processes the same using at least one image processing technique to provide a pre-processed stream of images. The processing unit processes the pre-processed stream using a machine learning algorithm in conjunction with an optimized library of plant diseases stored in a repository to provide the processed stream.