Election Voting Trend Prediction System

Competence ratings in US predict presidential election outcomes in Bulgaria

We describe the creation and verification of databases of all precinct boundaries used in the United States 2016, 2018, and 2020 November general elections, enhanced with election results for all partisan statewide offices. United States election officials report election results in the smallest geographic reporting known as the precinct. Scholars and practitioners find these election results valuable for numerous use cases. However, these data cannot be augmented with other geographically-bound data, such as U.S. Census data, without precinct boundaries. Here we describe the collection of precinct boundary data from state and local election officials, sometimes provided in GIS formats, images, text descriptions, and – in rare cases – verbally. We describe how we verify boundaries with other election data, such as geocoded voter registration files. Our open-source data has appeared in redistricting litigation argued before the United States Supreme Court; and has been used by state and local redistricting authorities, media organizations, advocacy groups, scholars, and a vibrant community of mapping enthusiasts.

Elections in Indonesia from period to period have undergone some changes. Elections legislative candidates not determined voters, but instead became a political elite authority in accordance with the order of the list of legislative candidates and their number sequence. To perform a prediction one of them with data mining. Data mining can be applied in the political sphere for example to predict the results of the legislative election and others. K-nearest neighbor algorithm is one of the data mining algorithm that performs classification based on learning object against which are closest to the object. Election-related research has been done with the k-nearest neighbor algorithm, but accuracy is obtained that method is still too low, so it takes an additional algorithm to improve accuracy. In this study, the proposed method, namely the method of k-nearest neighbor method combined with backward elimination as a selection of features. The dataset that will be used in the study comes from the KPU Sidoarjo that has special attributes 1 and 13 regular attributes. From the results of the analysis and computation of some methods, it can be concluded that the method of k-nearest neighbor method combined with backward elimination produced some conclusions. First, of the 14 attributes in the dataset, retrieved 8 most influential attribute. Second, the best accuracy are of 96.03% when k = 2 and tested by 10 fold cross validation.

Wishful thinking (WT) is the link between preference (wish) and expectation (prediction). New Zealand voters (N = 605) were asked to predict election outcomes 8 to 10 weeks prior to the 1990 general election and to state their preference for one of the two major parties (Labour or National). Strong and consistent WT effects were found, with more favorable outcomes being predicted for the preferred party. Level of confidence in their prediction also contributed to anticipated election outcomes. A minority of respondents who displayed accurate information about the outcome of the previous election showed no WT effect in their predictions. An instruction to be objective, designed to increase rationality, was ineffective in reducing WT. Independent of preference and WT, predictions of nationwide election outcomes also varied as a function of electorate of residence. Mechanisms contributing to WT, adaptive and maladaptive aspects of WT, and the human capacity for rational control are discussed.

Abstract. This system was developed to determine the accuracy of sentiment analysis on Twitter regarding the COVID-19 issue using the Nazief-Adriani and Porter stemmers with TF-IDF weighting, along with a classification process using K-Nearest Neighbor (KNN) that resulted in a comparison of 48.24% for Nazief-Adriani and 48.24% for Porter. Purpose: This research aims to determine the accuracy of the Nazief-Adriani and Porter stemmer algorithms in performing text preprocessing using a dataset from Indonesian-language Twitter. This research involves word weighting using TF-IDF and classification using the K-Nearest Neighbor (KNN) algorithm. Methods/Study design/approach: The experimentation was conducted by applying the Nazief-Adriani and Porter stemmer algorithm methods, utilizing data sourced from Twitter related to COVID-19. Subsequently, the data underwent text preprocessing, stemming, TF-IDF weighting, accuracy testing of training and testing data using K-Nearest Neighbor (KNN) algorithm, and the accuracy of both stemmers was calculated employing a confusion matrix table. Result/Findings: This study obtained reasonably accurate results in testing the Nazief-Adriani stemmer with an accuracy of 50.98%, applied to sentiment analysis of COVID-19-related Twitter data using the Indonesian language. As for the accuracy of the Porter stemmer, it achieved an accuracy rate of 48.24%. Novelty/Originality/Value: Feature selection is crucial in stemmer accuracy testing. Therefore, in this study, feature selection is carried out using the Nazief-Adriani and Porter stemmers for testing purposes, and the accuracy data classification is conducted using the K-Nearest Neighbor (KNN) algorithm

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

This doctoral project presents Harpia, a hybrid artificial intelligence planning system for UAVs (Unmanned Aerial Vehicles) with a focus on autonomy. Harpia aims to perform tasks for general-purpose applications with minimal human intervention. To facilitate understanding, the problem addressed is set on a farm where the autonomous system must be capable of carrying out missions safely. The system architecture is implemented using the Robotic Operating System (ROS) and includes functionalities such as task re-planning and trajectory planning with obstacle avoidance. Re-planning can occur after real-time mission changes or due to unpredictable UAV behavior. Harpia combines the Planning Domain Definition Language (PDDL) for task planning, a Bayesian Network (BN) for evaluating mission execution, a K-Nearest Neighbors (KNN) algorithm for selecting a trajectory planner, Principal Component Analysis (PCA), and a Decision Tree (DT) to assess the health of the aircraft. Therefore, the novelty of Harpia focuses on robustness for autonomous planning and re-planning of the sequence of tasks and trajectories for regions of interest. The main contributions include an autonomous system architecture to plan missions with minimal human intervention, unconstrained by specific tasks, and computationally simple to operate in diverse scenarios. Computational tests report results for 220 simulated scenarios, in which Harpia adequately handled all situations, for example, making decisions about task re-planning with 97.57% accuracy based on battery health and choosing the best planning trajectory for each case with at least 95% accuracy.

The results of two Dutch elections are predicted by counting political party mentions from tweets. In an attempt to improve the predictions, gender and age information from the Twitter users is automatically derived and used to adapt the party counts to the demographics in the election turnout. The prediction improves only slightly in one of the elections where the correlation between election outcome and Twitter-based prediction was relatively lower to begin with (0.86 versus 0.97). The relatively inaccurate estimation of Twitter user age may hinder a larger improvement.

k-nearest neighbor and centroid-based classification algorithms are frequently used in text classification due to their simplicity and performance. While k-nearest neighbor algorithm usually performs well in terms of accuracy, it is slow in recognition phase. Because the distances/similarities between the new data point to be recognized and all the training data need to be computed. On the other hand, centroid-based classification algorithms are very fast, because only as many distance/similarity computations as the number of centroids (i.e. classes) needs to be done. In this paper, we evaluate the performance of centroid-based classification algorithm and compare it to nearest mean and nearest neighbor algorithms on 9 data sets. We propose and evaluate an improvement on centroid based classification algorithm. Proposed algorithm starts from the centroids of each class and increases the weight of misclassified training data points on the centroid computation until the validation error starts increasing. The weight increase is done based on the training confusion matrix entries for misclassified points. Vie proposed algorithm results in smaller test error than centroid-based classification algorithm in 7 out of 9 data sets. It is also better than 10-nearest neighbor algorithm in 8 out of 9 data sets. We also evaluate different similarity metrics together with centroid and nearest neighbor algorithms. We find out that, when Euclidean distance is turned into a similarity measure using division as opposed to exponentiation. Euclidean-based similarity can perform almost as good as cosine similarity.

The AyuPredict model is a machine learning-based healthcare system designed to predict diseases and provide Ayurvedic treatment recommendations. This system aims to bridge the gap in healthcare accessibility, particularly in remote areas where medical facilities are scarce. The model leverages unsupervised learning algorithms, such as Random Forest, for disease prediction and supervised learning algorithms, like K-Nearest Neighbours (KNN), for recommending nearby hospitals. The system's architecture includes a user-friendly interface, disease prediction module, Ayurvedic treatment recommendation module, and hospital recommendation module. The model's performance is evaluated using accuracy metrics, with the Random Forest algorithm achieving an accuracy of 99.59% and F1 score of 99.58%. The KNN algorithm is used for hospital recommendations, providing a list of nearby hospitals based on user input. Future scope includes integrating virtual consultation platforms, voice assistants, and multilingual support to enhance accessibility and usability. The AyuPredict model has the potential to revolutionize healthcare services by providing accurate disease predictions and personalized treatment recommendations, ultimately improving patient outcomes

In this article, we perform sentiment analyses of Twitter location data. We use two case studies: US presidential elections of 2016 and UK general elections of 2017. For US elections, we plot state-wise user sentiment towards Hillary Clinton and Donald Trump. For UK elections, we download two disparate datasets, using keywords and location coordinates, looking for similar tendencies in sentiment towards political candidates and parties. The overall objective of the two case studies is to evaluate similarity between sentiment of location-based tweets and on-ground public opinion reflected in election results. We discover Twitter location sentiment does indeed corroborate with the election result in both cases. We also discover similar tendencies in Twitter sentiment towards political candidates and parties regardless of the methodology adopted for data collection.

Elections in recent years have become topical issues and characterized by violence and conflicts leading to loss of lives and properties. The integrity and confidentiality of the declared collated results have been questioned by individuals and organisations with keen interest in the outcomes of every election. Different countries have adopted different Election Results Management Systems (RMS) to help present a credible, fair and transparent election results. These systems adopted are not without criticisms and suspicions. This research paper has presented various factors that need to be considered when selecting a Results Management System (RMS) for elections. A cloud-based using a Hybrid homomorphic encryption approach is proposed in managing the election results data security and transmission. The proposed scheme has demonstrated effectiveness in handling data integrity, data confidentiality, data privacy and access control. The proposed scheme presented has enhanced the security of election results data against Chosen Ciphetext Attacks (CCA) and Denial of Service (DoS) as well as other cyber related attacks. The time required for the entire election data encryption, transmission, decryption, upload time and download time has been greatly enhanced with the proposed system. The proposed system workflow algorithm, key generation algorithm, encryption algorithm and decryption have been presented in this research paper. The outcome of the research work indicates that only 0.00078 seconds is required to generate keys for about 100 users. About 0.705 seconds and 0.863 seconds is required for the encryption and decryption of a 500MB election results data. It was observed that the overall election results data transmission time is about 51.779 seconds which is less than one minute (60 seconds) for about 500MB data size of the election results data. This paper makes a case for the adoption and implementation of the proposed system since it performs better in terms of securing the election results data and transmission time in the cloud environment.

Right-wing populism in Luxembourg is largely confined to the Alternative Democratic Reform Party (Alternativ Demokratesch Reformpartei, ADR). The name is, however, a bit of a misnomer. While ideologically, the ADR maintains national–conservative positions consistent with other European right-wing populist parties, its views are not as extreme. The party retains relatively constant support, consistently gaining around 10% of the vote in national elections; in the 2023 election for the Chamber of Deputies, it gained 9.3%, while in the 2019 European Parliament election, the party gained 10%, just short of enough to obtain a seat. Given the rise in support for right-wing populist parties in other European countries (e.g., the AfD in Germany or the National Rally in France), the ADR was optimistic about its chances of gaining its first-ever seat in the European Parliament, which would require about 12% of the vote total. This contribution will investigate the results of the European Parliament election in Luxembourg, focusing on the ADR. It will discuss any ideological shifts in the party as well as its positioning on a host of issues where one finds a prominent voice for right-wing populist parties in Europe, including NATO, the war in Ukraine, migration, COVID-19 or the functioning of the European Union. The entry will also address the results of the election to determine how strong support for right-wing populism in Luxembourg is. Other relevant aspects of the election (e.g., campaign events, media coverage) will be discussed if they featured prominently in the campaign. Keywords: ADR; Luxembourgish; European integration; transnational migration; Luxembourg Compromise

Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest “generalized exemplar” (i.e., either a point or an axis-parallel rectangle). They combine the distance-based character of nearest neighbor (NN) classifiers with the axis-parallel rectangle representation employed in many rule-learning systems. An implementation of NGE was compared to the k-nearest neighbor (kNN) algorithm in 11 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were studied to understand the cause of its poor performance. These show that its performance can be substantially improved by preventing NGE from creating overlapping rectangles, while still allowing complete nesting of rectangles. Performance can be further improved by modifying the distance metric to allow weights on each of the features (Salzberg, 1991). Best results were obtained in this study when the weights were computed using mutual information between the features and the output class. The best version of NGE developed is a batch algorithm (BNGE FWMI) that has no user-tunable parameters. BNGE FWMI's performance is comparable to the first-nearest neighbor algorithm (also incorporating feature weights). However, the k-nearest neighbor algorithm is still significantly superior to BNGE FWMI in 7 of the 11 domains, and inferior to it in only 2. We conclude that, even with our improvements, the NGE approach is very sensitive to the shape of the decision boundaries in classification problems. In domains where the decision boundaries are axis-parallel, the NGE approach can produce excellent generalization with interpretable hypotheses. In all domains tested, NGE algorithms require much less memory to store generalized exemplars than is required by NN algorithms.

We propose relational data modeling as a tool for replacing the ad hoc and uncoordinated approaches commonly used throughout the social sciences to gather, store, and disseminate data. We demonstrate relational data modeling using global electoral and political institutional data. We define a relational data model as a map of concepts, their attributes, and the relationships between concepts developed using a formal language and according to a set of rules. To demonstrate the methodology, we design a simple relational data model of six concepts: countries, parties, elections, districts, institutions, and election results. Furthermore, we introduce a data model to solve the particularly vexing issue of party discontinuity (party splits, mergers, and alliances). We show how the solution facilitates computational tasks, such as the calculation of core measures of political phenomena (ex: electoral volatility). Ultimately, a relational data approach will play a central role in collective investments to develop advanced data capabilities, and thereby advance the accuracy, pace, and transparency of scholarship in the social sciences.