Longitudinal Study of a Website for Assessing American Presidential Candidates and Decision Making of Potential Election Irregularities Detection

Abstract

We employ the concept of word sense disambiguation to determine the inherent meaning of voter intentions regarding possible political candidates from the 2016 Presidential election. We present our findings based on a website (www.presidentselect.com) that we developed, where candidates can be examined and their true assets and competencies in three major areas of eligibility, education, and experience inputs can be deciphered. Data envelope analysis is used to determine underlying word instances for elected and successful outputs. We also utilize our web site results to longitudinally extend these findings for decision making of potential election fraud detection in the 2020 Presidential election, utilizing Benford’s Law. Our results shed light on these phenomenon and provide new insights into the word sense disambiguation literature.

An Intersection-Based Routing Scheme Using Q-Learning in Vehicular Ad Hoc Networks for Traffic Management in the Intelligent Transportation System

“…Vehicles, along with road infrastructure, make a new wireless network known as vehicular ad hoc network (VANET) [4,5]. The initial purpose of this network was drivers' safety and comfort in vehicular environments [6][7][8]. However, this view is slowly changing because the network is now used as an infrastructure for the intelligent transportation system and supports vehicles and any activity that require Internet in the smart city ecosystem.…”