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Full-Text Articles in Physical Sciences and Mathematics
Global Deforestation Prediction: Summer Internship At Clark Labs, Tianze Li, Yahsee Joshi
Global Deforestation Prediction: Summer Internship At Clark Labs, Tianze Li, Yahsee Joshi
Sustainability and Social Justice
This paper is a description of our internship with Clark Labs in the summer of 2016. We worked as research assistants in the Deforestation Risk Prediction project for Ecosystem Services team. This goal of the project was to predict deforestation at global, continental and national level. Our responsibilities were to choose the variables that may influence the deforestation and to use Land Change Modeler in TerrSet to test the variables and create the deforestation prediction maps. We highly recommend this internship with Clark Labs to other GISDE students who are interested in land change analysis.
Predictive Modeling Of Adolescent Cannabis Use From Multimodal Data, Philip Spechler
Predictive Modeling Of Adolescent Cannabis Use From Multimodal Data, Philip Spechler
Graduate College Dissertations and Theses
Predicting teenage drug use is key to understanding the etiology of substance abuse. However, classic predictive modeling procedures are prone to overfitting and fail to generalize to independent observations. To mitigate these concerns, cross-validated logistic regression with elastic-net regularization was used to predict cannabis use by age 16 from a large sample of fourteen year olds (N=1,319). High-dimensional data (p = 2,413) including parent and child psychometric data, child structural and functional MRI data, and genetic data (candidate single-nucleotide polymorphisms, "SNPs") collected at age 14 were used to predict the initiation of cannabis use (minimum six occasions) by age 16. …
Infodemiology For Syndromic Surveillance Of Dengue And Typhoid Fever In The Philippines, Ma. Regina Justina E. Estuar, Kennedy E. Espina
Infodemiology For Syndromic Surveillance Of Dengue And Typhoid Fever In The Philippines, Ma. Regina Justina E. Estuar, Kennedy E. Espina
Department of Information Systems & Computer Science Faculty Publications
Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data as well as interoperate fragmented systems, leading to longer transfer and processing of data. As such, this study presents the use of realtime latent data from social media, particularly from Twitter, to complement existing disease surveillance efforts. By being able to classify infodemiological (health-related) tweets, this study is able to produce a range of possible disease incidences of Dengue and Typhoid Fever within the …