Identification of fake social profiles by analyzing terabytes of social networking data

Our Solution The client provides a social identifier, a free web identity credential controlled by the user. We designed and implemented an algorithm (based on research from Stanford University) on Hadoop using PIG, to evaluate the authenticity of an individual’s profile attributes. We also implemented various fraud detection and prevention solutions with viral social features […]

Cloud Security by Detecting Event Log Anomalies Using Machine Learning

ML-Based Anomaly Detection in Event Logs

Our Solution We set up a continuous feed of raw data logs, using AWS Kinesis from AWS CloudTrail. Data were then grouped by Time, Usertype, and Logtype into multiple batches. From these batches, variables were generated to be fed into a Machine Learning model that predicted if the data entry was an anomaly, using logistic […]

Reducing patient readmission risk with Machine Learning

Our Solution We analyzed the past patient history of readmissions. Then, we clustered patients based on clinical, social, and behavioral factors like associated clinical conditions, age, gender, weight, lifestyle, ethnicity, economic indicators, geography, etc. With this data, we derived a Machine Learning model to predict the risk of readmission for a patient. Finally, we tested […]

Using Deep Learning to help consumers get the best deals from local stores

Our Solution Data Science was used to track, predict, and forecast prices based on various factors. We built an application in Ruby on Rails that lets shoppers pick the best prices for products from highly popular Australian stores, such as Coles and Woolworths. The app delivered great user experience by leveraging React for the web […]

Determining clinical events timeline from a Doctor’s notes using Natural Language Processing

Our Solution We identified medically significant events like ‘infections’, ‘antibiotics’, ‘surgery’, ‘x-ray’, ‘lab test’, and more. We then leveraged NLP to arrive at the occurrence time of significant events by identifying temporal expressions like ‘today’, ‘two weeks ago’, etc., extracted from the multiple documents and correlated them with the date of document creation. We then […]

Generating audience analytics for a multichannel YouTube network

Our Solution We crawled millions of records to fetch data on audience analytics, with cross-linked data from G+ & Facebook. We then used Coefficient Correlation and Linear Regression to deliver analytics for 2000+ brands, using videos that covered just 3 brands.

Natural Language Processing (NLP) for discovering enterprise events

Our Solution Interesting enterprise events happening all across your IT ecosystem are converted into a live stream of events organized around Topics. Real-time analytics engines take action on these events like intelligent routing. For UX, we leveraged the principle of 20% of actions are performed 80% of the time. We enable this 80% via messaging, […]