An e-Voting Model to Preserve Vote Integrity Employing SHA3 Algorithm

2021 ◽  
pp. 439-447
Author(s):  
B. Patel ◽  
D. Bhatti
Keyword(s):  
Voting Model

Elemental tests of the traditional rational voting model

Public Choice ◽  
2008 ◽  
Vol 137 (1-2) ◽  
pp. 173-195 ◽  
Author(s):  
Darren Grant ◽  
Michael Toma
Keyword(s):  
Voting Model

scholarly journals Landscape formation in a spatial voting model

1997 ◽  
Vol 55 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Ken Kollman ◽  
John H. Miller ◽  
Scott E. Page
Keyword(s):  
Spatial Voting ◽  
Landscape Formation ◽  
Spatial Voting Model ◽  
Voting Model

A Group Rule—Utilitarian Approach to Voter Turnout: Theory and Evidence

2004 ◽  
Vol 94 (5) ◽  
pp. 1476-1504 ◽  
Author(s):  
Stephen Coate ◽  
Michael Conlin
Keyword(s):  
Voter Turnout ◽  
Theoretical Work ◽  
Coefficient Estimates ◽  
Expressive Voting ◽  
Model Based ◽  
Utilitarian Approach ◽  
Voting Model

This paper explores a group rule–utilitarian approach to understanding voter turnout, inspired by the theoretical work of John C. Harsanyi (1980) and Timothy J. Feddersen and Alvaro Sandroni (2002). It develops a model based on this approach and studies its performance in explaining turnout in Texas liquor referenda. The results are encouraging: the comparative static predictions of the model are broadly consistent with the data, and a structurally estimated version of the model yields reasonable coefficient estimates and fits the data well. The structurally estimated model also outperforms a simple expressive voting model.

scholarly journals U-Vote: Ubiquitous Voting Model For General Election in Global Pandemic

2020 ◽  
Author(s):  
Slamet Risnanto ◽  
Yahaya Abd Rahim ◽  
Kodrat Mahatma ◽  
Asep Effendi ◽  
Hendra Garnida ◽  
...  
Keyword(s):  
General Election ◽  
Global Pandemic ◽  
Voting Model

Multi-Candidate Voting Model Based on Blockchain

2021 ◽  
pp. 1-10
Author(s):  
Dongliang Xu ◽  
Wei Shi ◽  
Wensheng Zhai ◽  
Zhihong Tian
Keyword(s):  
Model Based ◽  
Voting Model

TARIFF FORMATION IN A MULTIDIMENSIONAL VOTING MODEL

1989 ◽  
Vol 1 (1) ◽  
pp. 61-79 ◽  
Author(s):  
Wolfgang Mayer ◽  
Raymond Riezman
Keyword(s):  
Voting Model

scholarly journals Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy

2021 ◽  
Vol 11 ◽  
Author(s):  
Kailyn Stenhouse ◽  
Michael Roumeliotis ◽  
Robyn Banerjee ◽  
Svetlana Yanushkevich ◽  
Philip McGeachy
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dose Rate ◽  
High Dose Rate ◽  
Gradient Boosting ◽  
Classification Algorithms ◽  
High Dose ◽  
Discriminative Performance ◽  
The Individual ◽  
Voting Model

PurposeTo develop and validate a preliminary machine learning (ML) model aiding in the selection of intracavitary (IC) versus hybrid interstitial (IS) applicators for high-dose-rate (HDR) cervical brachytherapy.MethodsFrom a dataset of 233 treatments using IC or IS applicators, a set of geometric features of the structure set were extracted, including the volumes of OARs (bladder, rectum, sigmoid colon) and HR-CTV, proximity of OARs to the HR-CTV, mean and maximum lateral and vertical HR-CTV extent, and offset of the HR-CTV centre-of-mass from the applicator tandem axis. Feature selection using an ANOVA F-test and mutual information removed uninformative features from this set. Twelve classification algorithms were trained and tested over 100 iterations to determine the highest performing individual models through nested 5-fold cross-validation. Three models with the highest accuracy were combined using soft voting to form the final model. This model was trained and tested over 1,000 iterations, during which the relative importance of each feature in the applicator selection process was determined.ResultsFeature selection indicated that the mean and maximum lateral and vertical extent, volume, and axis offset of the HR-CTV were the most informative features and were thus provided to the ML models. Relative feature importances indicated that the HR-CTV volume and mean lateral extent were most important for applicator selection. From the comparison of the individual classification algorithms, it was found that the highest performing algorithms were tree-based ensemble methods – AdaBoost Classifier (ABC), Gradient Boosting Classifier (GBC), and Random Forest Classifier (RFC). The accuracy of the individual models was compared to the voting model for 100 iterations (ABC = 91.6 ± 3.1%, GBC = 90.4 ± 4.1%, RFC = 89.5 ± 4.0%, Voting Model = 92.2 ± 1.8%) and the voting model was found to have superior accuracy. Over the final 1,000 evaluation iterations, the final voting model demonstrated a high predictive accuracy (91.5 ± 0.9%) and F1 Score (90.6 ± 1.1%).ConclusionThe presented model demonstrates high discriminative performance, highlighting the potential for utilization in informing applicator selection prospectively following further clinical validation.

Searching for Expertise: Experiments with the Voting Model

2008 ◽  
Vol 52 (7) ◽  
pp. 729-748 ◽  
Author(s):  
C. Macdonald ◽  
I. Ounis
Keyword(s):  
Voting Model

scholarly journals Long route to consensus: Two-stage coarsening in a binary choice voting model

2020 ◽  
Vol 102 (1) ◽  
Author(s):  
Sudip Mukherjee ◽  
Soumyajyoti Biswas ◽  
Parongama Sen
Keyword(s):  
Binary Choice ◽  
Two Stage ◽  
Voting Model

Feature-Preserving Tensor Voting Model for Mesh Steganalysis

2021 ◽  
Vol 27 (1) ◽  
pp. 57-67
Author(s):  
Hang Zhou ◽  
Kejiang Chen ◽  
Weiming Zhang ◽  
Chuan Qin ◽  
Nenghai Yu
Keyword(s):  
Tensor Voting ◽  
Feature Preserving ◽  
Voting Model
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