Search Results for author:
Found 18 papers, 0 papers with code
Synthetic Data Generator for Adaptive Interventions in Global Health
Artificial Intelligence and digital health have the potential to transform global health.
Adaptive Interventions for Global Health: A Case Study of Malaria
Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200. 000 preventable deaths.
User Engagement in Mobile Health Applications
Mobile health apps are revolutionizing the healthcare ecosystem by improving communication, efficiency, and quality of service.
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities
Providing front-line health workers in low- and middle- income countries with recommendations and predictions to improve health outcomes can have a tremendous impact on reducing healthcare inequalities, for instance by helping to prevent the thousands of maternal and newborn deaths that occur every day.
A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries
This is the aim of the Safe Delivery App, a digital job aid and learning tool to enhance the knowledge, confidence and skills of health practitioners.
Midwifery Learning and Forecasting: Predicting Content Demand with User-Generated Logs
Every day, 800 women and 6, 700 newborns die from complications related to pregnancy or childbirth.
A Time Series Approach To Player Churn and Conversion in Videogames
A State Space time series approach is then used to model the daily conversion rates between the different groups, i. e., the probability of transitioning from one group to another.
Profiling Players with Engagement Predictions
The possibility of using player engagement predictions to profile high spending video game users is explored.
Understanding Player Engagement and In-Game Purchasing Behavior with Ensemble Learning
As video games attract more and more players, the major challenge for game studios is to retain them.
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning
The proposed conditional inference survival ensembles method could be readily used in operational environments for early identification of premium players and the parts of the game that may prompt them to become paying users.
The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition
Machine learning competitions such as those organized by Kaggle or KDD represent a useful benchmark for data science research.
Customer Lifetime Value in Video Games Using Deep Learning and Parametric Models
Nowadays, video game developers record every virtual action performed by their players.
A Machine-Learning Item Recommendation System for Video Games
Video-game players generate huge amounts of data, as everything they do within a game is recorded.
Game Data Mining Competition on Churn Prediction and Survival Analysis using Commercial Game Log Data
Game companies avoid sharing their game data with external researchers.
Databases
Games and Big Data: A Scalable Multi-Dimensional Churn Prediction Model
The emergence of mobile games has caused a paradigm shift in the video-game industry.
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data
In this article, we study free-to-play game data.
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn.
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science.
