The goal of text summarization is to automatically condense unstructured text into summaries, containing only the most important information.
Anomaly detection is a common problem that can be solved using machine learning techniques. Simple density based algorithms provide a good baseline for such projects, and can be used to solve a variety of problems from defect detection in manufacturing to network attacks in IT.
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.
In this blog post we talk about 5 aspects of machine learning that can be applied to transportation.
For human beings, interpreting what we see is so easy that we are hardly conscious of it; However, for computers, these tasks are very difficult problems to solve.
In this blog, we will briefly introduce image recognition with transfer learning. At its most fundamental, an image recognition algorithm takes images and outputs a label describing the image.
We will classify images from the Caltech 101 dataset with the Open Source Computer Vision (OpenCV) library.
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.