scholarly journals How Threats Shape the Politics of Marginalized: Evidence from a Natural Experiment and Machine Learning

2020 ◽  
Author(s):  
Jae Yeon Kim ◽  
Andrew Thompson
Keyword(s):  
Machine Learning ◽  
Information Seeking ◽  
Natural Experiment ◽  
Domestic Politics ◽  
R Package ◽  
Exogenous Shock ◽  
Indian American ◽  
Ethnic Newspapers ◽  
September 11 Attacks ◽  
Automated Text Classification

In this study, we used a natural experiment and machine learning to examine how threats prompt information seeking among marginalized populations. We traced how the September 11 attacks, an exogenous shock, increased the interest of Arab and Indian Americans in U.S. domestic politics. We classified 5,684 Arab American and Indian American newspaper articles using machine learning and estimated that three more articles on U.S. domestic politics were published daily in the post-9/11 period than in previous years. While the natural experiment design identifies the causal relationship between the intervention and the outcome variation, an automated text classification creates essential data for such a causal identification. This project also provides an accompanying R package that makes collecting data from the largest database of ethnic newspapers published in the U.S. easier and faster.

scholarly journals mlr3proba: An R Package for Machine Learning in Survival Analysis

2021 ◽  
Author(s):  
Raphael Sonabend ◽  
Franz J Király ◽  
Andreas Bender ◽  
Bernd Bischl ◽  
Michel Lang
Keyword(s):  
Machine Learning ◽  
Survival Analysis ◽  
General Model ◽  
R Package ◽  
Survival Modeling ◽  
Model Tuning

Abstract Motivation As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering, and more. mlr3proba provides a comprehensive machine learning interface for survival analysis and connects with mlr3’s general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modeling and evaluation. Availability mlr3proba is available under an LGPL-3 license on CRAN and at https://github.com/mlr-org/mlr3proba, with further documentation at https://mlr3book.mlr-org.com/survival.html.

scholarly journals treeheatr: an R package for interpretable decision tree visualizations

2020 ◽  
Author(s):  
Trang T. Le ◽  
Jason H. Moore
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Feature Space ◽  
R Package ◽  
Tree Structure ◽  
Decision Tree Model ◽  
Teaching Tool ◽  
Tree Model ◽  
Machine Learning Methods ◽  
Link Type

AbstractSummarytreeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods.AvailabilityThe treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous [email protected]

Machine Learning Techniques for Underwater Wireless Sensor Networks

2021 ◽  
pp. 194-211
Author(s):  
Deepti Rani ◽  
Anju Sangwan ◽  
Anupma Sangwan ◽  
Tajinder Singh
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Information Seeking ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Wireless Sensor ◽  
Learning Techniques ◽  
Primary Focus

With the enormous growth of sensor networks, information seeking from such networks has become an invaluable source of knowledge for various organizations to enhance the comprehension of people interests. Not only wireless sensor networks (WSNs) but its various classes also remain the hot topics of research. In this chapter, the primary focus is to understand the concept of sensor network in underwater scenario. Various mechanisms are used to recognize the activities underwater using sensor which examines the real-time events. With these features, a few challenges are also associated with sensor networks, which are addressed here. Machine learning (ML) techniques are the perfect key of success to resolve such issues due to their feasibility and adaption in complex problem environment. Therefore, various ML techniques have been explained to enhance the operational performance of WSNs, especially in underwater WSNs (UWSNs). The main objective of this chapter is to understand the concepts of UWSNs and role of ML to address the performance issues of UWSNs.

scholarly journals Proxemic Effects on Information Seeking after the September 11 Attacks

2005 ◽  
Vol 22 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Patric R. Spence ◽  
David Westerman ◽  
Paul D. Skalski ◽  
Matthew Seeger ◽  
Robert R. Ulmer ◽  
...  
Keyword(s):  
Information Seeking ◽  
September 11 ◽  
September 11 Attacks

scholarly journals Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods

2020 ◽  
Vol 9 (1) ◽  
pp. 44 ◽  
Author(s):  
Wolfgang B. Hamer ◽  
Tim Birr ◽  
Joseph-Alexander Verreet ◽  
Rainer Duttmann ◽  
Holger Klink
Keyword(s):  
Machine Learning ◽  
Powdery Mildew ◽  
Regional Scale ◽  
R Package ◽  
Good Time ◽  
Climate Information ◽  
Learning Methods ◽  
Temporal Prediction ◽  
Machine Learning Methods ◽  
Weather And Climate

Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted.

scholarly journals Predictive and interpretable models via the stacked elastic net

2020 ◽  
Author(s):  
Armin Rauschenberger ◽  
Enrico Glaab ◽  
Mark van de Wiel
Keyword(s):  
Machine Learning ◽  
R Package ◽  
Elastic Net ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Biomedical Sciences ◽  
Molecular Features ◽  
Learning Techniques ◽  
Meta Learning ◽  
Interpretable Models

Abstract Motivation Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative, and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results Here we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularisation. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability. Availability and Implementation The R package starnet is available on GitHub: https://github.com/rauschenberger/starnet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Optimal Stratification and Allocation for the June Agricultural Survey

2018 ◽  
Vol 34 (1) ◽  
pp. 121-148
Author(s):  
Jonathan Lisic ◽  
Hejian Sang ◽  
Zhengyuan Zhu ◽  
Stephanie Zimmer
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Simulated Data ◽  
R Package ◽  
Computational Approach ◽  
Machine Learning Method ◽  
Learning Method ◽  
Computationally Efficient ◽  
Computational Speed ◽  
Efficient Machine

Abstract A computational approach to optimal multivariate designs with respect to stratification and allocation is investigated under the assumptions of fixed total allocation, known number of strata, and the availability of administrative data correlated with thevariables of interest under coefficient-of-variation constraints. This approach uses a penalized objective function that is optimized by simulated annealing through exchanging sampling units and sample allocations among strata. Computational speed is improved through the use of a computationally efficient machine learning method such as K-means to create an initial stratification close to the optimal stratification. The numeric stability of the algorithm has been investigated and parallel processing has been employed where appropriate. Results are presented for both simulated data and USDA’s June Agricultural Survey. An R package has also been made available for evaluation.

scholarly journals Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data

2018 ◽  
Author(s):  
Fadhl M. Al-Akwaa ◽  
Sijia Huang ◽  
Lana X. Garmire
Keyword(s):  
Machine Learning ◽  
R Package ◽  
Classification Algorithms ◽  
Feature Mapping ◽  
Metabolomics Data ◽  
Machine Learning Classification ◽  
Disease Phenotypes ◽  
Prediction Module ◽  
Significant Pathway ◽  
Pathway Deregulation

AbstractLilikoi (Hawaiian word for passion fruit) is a new and comprehensive R package for personalized pathway based classification modelling, using metabolomics data. Four basic modules are presented as the backbone of the package: 1) Feature mapping module, which standardizes the metabolite names provided by users, and map them to pathways. 2) Dimension transformation module, which transforms the metabolomic profiles to personalized pathway-based profiles using pathway deregulation scores (PDS). 3) Feature selection module which helps to select the significant pathway features related to the disease phenotypes, and 4) Classification and prediction module which offers various machine-learning classification algorithms. The package is freely available under the GPLv3 license through the github repository at: https://github.com/lanagarmire/lilikoi

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