Aim/Purpose: This review paper aims to unveil some underlying machine-learning classification algorithms used for political election predictions and how stack ensembles have been explored. Additionally, it examines the types of datasets available to researchers and presents the results they have achieved. Background: Predicting the outcomes of presidential elections has always been a significant aspect of political systems in numerous countries. Analysts and researchers examining political elections rely on existing datasets from various sources, including tweets, Facebook posts, and so forth to forecast future elections. However, these data sources often struggle to establish a direct correlation between voters and their voting patterns, primarily due to the manual nature of the voting process. Numerous factors influence election outcomes, including ethnicity, voter incentives, and campaign messages. The voting patterns of successors in regions of countries remain uncertain, and the reasons behind such patterns remain ambiguous. Methodology: The study examined a collection of articles obtained from Google Scholar, through search, focusing on the use of ensemble classifiers and machine learning classifiers and their application in predicting political elections through machine learning algorithms. Some specific keywords for the search include “ensemble classifier,” “political election prediction,” and “machine learning”, “stack ensemble”. Contribution: The study provides a broad and deep review of political election predictions through the use of machine learning algorithms and summarizes the major source of the dataset in the said analysis. Findings: Single classifiers have featured greatly in political election predictions, though ensemble classifiers have been used and have proven potent use in the said field is rather low. Recommendation for Researchers: The efficacy of stack classification algorithms can play a significant role in machine learning classification when modelled tactfully and is efficient in handling labelled datasets. however, runtime becomes a hindrance when the dataset grows larger with the increased number of base classifiers forming the stack. Future Research: There is the need to ensure a more comprehensive analysis, alternative data sources rather than depending largely on tweets, and explore ensemble machine learning classifiers in predicting political elections. Also, ensemble classification algorithms have indeed demonstrated superior performance when carefully chosen and combined.

Nordic Studies on Alcohol and Drugs, Ahead of Print. Aim: The aim of this study was to identify and elucidate the differences between highly affiliated and low/non-affiliated participants in Alcoholics Anonymous (AA) meetings. Methods: A qualitative study of 24 participants was conducted in Romania between March and June 2021. Data were collected by means of […]

The post Affiliation to the Alcoholics Anonymous (AA) community: A qualitative study on differences between highly affiliated and low/non-affiliated individuals was curated by information for practice.

The Delaware Department of Elections is informing voters that automatically registered voters may declare their party at the State Primary Election, September 10, 2024. Delaware is a Closed Primary State, meaning that only voters affiliated with the Democratic or Republican parties may vote in their party’s Primary Election. Eligible Voters must vote at their assigned Polling Place, and Polling Places are open from 7 a.m. – 8 p.m. on Primary Election Day.

Of the 21 schools whose affiliation has been withdrawn, 16 are in Delhi while five of them are in Rajasthan's coaching hubs — Kota and Sikar

Bank of Italy Working Papers by Giorgio Albareto, Andrea Cardillo, Andrea Hamaui and Giuseppe Marinelli

The Department of Justice’s Antitrust Division issued the following statement today after announcing the closing of its investigation into Highmark’s affiliation agreement with West Penn Allegheny Health System (WPAHS).

A small group of Americans considers the religious affiliation of the hospitals they choose to be treated, but a majority said they didn't want religion to interfere in their healthcare choices.

Studies show that the fear of contagion leads individuals to become more conservative and less accepting of experimentation. They prefer quietly falling in line, however long, rather than taking on eccentricity.