Predicting Flu Outbreaks with Twitter

Predicting Flu Outbreaks with Twitter

The use of Twitter and natural language processing opens up a promising new approach to flu surveillance. Such data-driven methods produce encouraging results and provide a faster way to identify flu surges.

Further, these Twitter-based methods can be very easily applied to numerous other domains such as Marketing, for identifying geospatial trends in brand image, as well as in Urban Planning for analyzing public attitudes towards various spaces and landmarks for example.

Archaeological Site Prediction Using Machine Learning

Archaeological Site Prediction Using Machine Learning

Machine Learning is still in its infancy in archaeology, but the approach holds enormous promise. Just as in finance and disaster mitigation, the use of satellite imagery has become a vital resource for archaeology. As more archaeologists around the world automate the process of identifying archaeological sites with satellite images, sites will be found at a faster rate than ever before.

Time-Series Analysis: Wearable Devices using DTW and kNN

Time-Series Analysis: Wearable Devices using DTW and kNN

Recently, there has been great success in time series analyses by applying dynamic time warping (DTW). Indeed, when combined with simple algorithms such as k-Nearest Neighbours, DTW has reproduced accuracies of  state-of-the-art algorithms, such as deep neural nets etc.

We will discuss here a supervised classification example of wearable devices and a possible use-case for DTW.

 

Image Recognition: Retinopathy

Image Recognition: Retinopathy

Almost every major company now has vested interest in image recognition algorithms: Tesla for autonomous driving, Amazon for product and price comparison, Google for its image search, and of course Facebook for facial recognition. 

The potential for this type of technology is limitless and has already been used in fields as disparate as sport science to astronomy. Here we discuss one recent and important example in the medical field: diabetic retinopathy.  

Diagnosis software, based on image recognition, allows quick and cheap diagnosis, saving human hours as well as human lives.