Machine learning system for automated blood smear analysis

Machine Learning for Semi-Automated Gameplay Analysis

Compelling gameplay requires constant testing and refinement during the development process, amounting to a considerable investment in time and energy. This article presents an approach to gameplay analysis intended to support and augment the work of game designers, collecting and summarizing gameplay information from the game engine so designers can quickly evaluate the behaviour to make decis...

Dynamic evaluation of autofocusing for automated microscopic analysis of blood smear and pap smear.

Autofocusing is a fundamental procedure towards automated microscopic evaluation of blood smear and pap smear samples for clinical diagnosis. This paper presents comparison results of 16 selected focus algorithms based on 8000 static bright-field images and 1600 dynamic autofocusing trials using 10 blood smear and pap smear samples. Besides static behaviour, dynamic autofocusing performance is ...

Semi-Automated Gameplay Analysis by Machine Learning

While presentation aspects like graphics and sound are important to a successful commercial game, it is likewise important that the gameplay, the non-presentational behaviour of the game, is engaging to the player. Considerable effort is invested in testing and refining gameplay throughout the development process. We present an overall view of the gameplay management problem and, more concretel...

Machine Learning for Automated Reasoning

Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through sev...