4 results match your criteria Applied Artificial Intelligence[Journal]

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An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group.

Appl Artif Intell 2020 14;34(14):1100-1114. Epub 2020 Oct 14.

Department of Radiation Oncology, University of Toronto, Toronto, Canada.

In this manuscript we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. Read More

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October 2020

Automatic Identification of Character Types from Film Dialogs.

Appl Artif Intell 2016 Nov;30(10):942-973

Austrian Research Institute for Artificial Intelligence OFAI, Vienna, Austria.

We study the detection of character types from fictional dialog texts such as screenplays. As approaches based on the analysis of utterances' linguistic properties are not sufficient to identify all fictional character types, we develop an integrative approach that complements linguistic analysis with interactive and communication characteristics, and show that it can improve the identification performance. The interactive characteristics of fictional characters are captured by the descriptive analysis of semantic graphs weighted by linguistic markers of expressivity and social role. Read More

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November 2016

Robust Feature Selection Technique using Rank Aggregation.

Appl Artif Intell 2014 Jan;28(3):243-257

College of Science and Engineering University of Minnesota at Twin Cities.

Although feature selection is a well-developed research area, there is an ongoing need to develop methods to make classifiers more efficient. One important challenge is the lack of a universal feature selection technique which produces similar outcomes with all types of classifiers. This is because all feature selection techniques have individual statistical biases while classifiers exploit different statistical properties of data for evaluation. Read More

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January 2014

Maintaining Engagement in Long-term Interventions with Relational Agents.

Appl Artif Intell 2010 Jul;24(6):648-666

Northeastern University College of Computer and Information Science, 360 Huntington Ave, WVH202, Boston, MA 02115.

We discuss issues in designing virtual humans for applications which require long-term voluntary use, and the problem of maintaining engagement with users over time. Concepts and theories related to engagement from a variety of disciplines are reviewed. We describe a platform for conducting studies into long-term interactions between humans and virtual agents, and present the results of two longitudinal randomized controlled experiments in which the effect of manipulations of agent behavior on user engagement was assessed. Read More

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