I am the co founder of Indiego Technologies. Oversee all projects are successfully executed and delivered to client satisfaction.

Co-Founder

Machine Learning and Neural Networks Expert

jini Cheriyan

Developed a computer vision application for an automotive industry from North America. The application detected sign boards placed on roads. The application was trained to identify and process the different meaning of sign boards.  


Detection of traffic signboards 

3D reconstruction of human retina from single fundus image 

Face Recognition

developed a framework (Graphical User Interface application) based on Deep Learning methods and TensorFlow that can be used to annotate and train custom data to create the Machine Learning model. The developed framework had the following features:

Annotation tool: To label the image
Train: To train the images along with the annotated data
Predict: For testing the images to check whether the objects are detected properly
The custom models were used to build different applications for:

Industrial Automation
Vehicle Identification for Insurance
Retail store to identify the packets in the shelf

Object Detection and Tracking

Optical Character Recognition (OCR)

Trained and developed a Twitter Sentiment Analysis supervised learning model using python, TensorFlow and NLP libraries. Key implementation steps included:

Extraction – Fetch tweets using Twitter API keys.
Preparation – Prepare dataframe for preprocessing the tweets to remove non-contextual words
Parsing – The tweet messages were processed and we generated the training dataset.
Feature Extraction – Using pre trained embedding’s using Golve.
Training – Using LSTM network to train the data.
Prediction – Trained models was used to analyze the sentiment of new tweets

Sentiment Analysis

Developed a computer vision application for a healthcare/microbiology client that that detects cells from microscopic images. Algorithms were used to identify whether the detected cells were live or dead. 
The application also provided the number of counts of live and dead cells. 

Live Dead cell count from microscopic image

developed a Computer vision and Deep Learning application that can detect and count the number of olives from the images captured from robotic arms to determine the yield of olive production.

Detection of olives for robotic arm

Developed a computer vision application for a healthcare client that that detects blood cells from microscopic images. Algorithms were used to identify whether the detected blood cells White blood cells or Red blood cells.
The application also provided the number of counts of live and dead cells.

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jini C.

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