Majority Voting System Research Articles

Landscape of the metaplasmidome of deep-sea hydrothermal vents located at Arctic Mid-Ocean Ridges in the Norwegian-Greenland Sea: ecological insights from comparative analysis of plasmid identification tools.

Plasmids are one of the key drivers of microbial adaptation and evolution. However, their diversity and role in adaptation, especially in extreme environments, remains largely unexplored. In this study, we aimed to identify, characterize, and compare plasmid sequences originating from samples collected from deep-sea hydrothermal vents located in Arctic Mid-Ocean Ridges. To achieve this, we employed, and benchmarked three recently developed plasmid identification tools-PlasX, GeNomad, and PLASMe-on metagenomic data from this unique ecosystem. To date, this is the first direct comparison of these computational methods in the context of data from extreme environments. Upon recovery of plasmid contigs, we performed a multiapproach analysis, focusing on identifying taxonomic and functional biases within datasets originating from each tool. Next, we implemented a majority voting system to identify high-confidence plasmid contigs, enhancing the reliability of our findings. By analysing the consensus plasmid sequences, we gained insights into their diversity, ecological roles, and adaptive significance. Within the high-confidence sequences, we identified a high abundance of Pseudomonadota and Campylobacterota, as well as multiple toxin-antitoxin systems. Our findings ensure a deeper understanding of how plasmids contribute to shaping microbial communities living under extreme conditions of hydrothermal vents, potentially uncovering novel adaptive mechanisms.

Deep Ensemble Learning Model for Diagnosis of Lung Diseases from Chest X -Ray Images

Objectives: This study aims to develop a robust medical recognition system using deep learning for the identification of various lung diseases, including COVID-19, pneumonia, lung opacity, and normal states, from chest X-ray images. The focus is on implementing ensemble fixed features learning methods to enhance diagnostic capabilities, contributing to the development of a cost-effective and reliable diagnostic tool for combating the global epidemic of lung disorders. Methods: The study utilizes a Kaggle dataset containing COVID-19 chest radiography images. Raw X-ray images undergo preprocessing for contrast enhancement and noise removal while addressing dataset imbalance through near-miss resampling. Ensemble learning techniques, including two and three-level methods, are employed to harness the strengths of individual base learners—VGG16, InceptionV3, and MobileNetV2. The model's performance is evaluated using metrics such as accuracy, recall, precision, and F1-score. For remote access, a user interface and a shared web link are developed using Python Gradio. Findings: In two-level ensembles, features from base learners are concatenated and classified using a support vector machine. Three-level ensembles use concatenated features classified by three machine learning classifiers, employing a majority voting system for the final prediction. The two-level method achieved 93% accuracy, precision, recall, and F1 score. The three-level ensemble model demonstrates superior performance, achieving 94% accuracy in detecting four lung diseases, namely COVID-19, pneumonia, lung opacity, and normal states. Novelty: This research contributes to the field by showcasing the efficacy of deep learning technology, particularly ensemble learning, in enhancing the detection of lung diseases from raw chest X-ray images. The model employs three modified and efficient pretrained networks for automatic feature extraction, eliminating the need for manual feature engineering. The developed model stands as a promising decision-support tool for healthcare professionals, particularly in low-resource environments. Keywords: Convolutional Neural Network (CNN), Deep Learning (DL), Transfer Learning (TL), Ensemble learning (EL), Fixed feature extraction, Chest X­rays (CXR), Lung diseases

Fraud Detection in Medical Insurance Claims Using Majority Voting of Multiple Unsupervised Algorithms

This paper addresses the critical challenge of fraud detection in medical insurance claims, a pervasive issue causing significant financial losses in healthcare. The primary goal is to develop an advanced fraud detection approach by integrating multiple unsupervised machines learning algorithms, leveraging their collective strengths through a majority voting mechanism, where labelling of data is unavailable. Central to this approach is the ensemble of 18 novel unsupervised algorithms, specifically, anomaly detection models. The novelty lies in the majority voting system employed to aggregate the decisions from these diverse algorithms, enhancing the reliability and accuracy of fraud detection. To validate the effectiveness of the proposed system, a dual approach is employed. Firstly, human experts in the medical insurance field review a subset of claims to establish a benchmark for the model's performance. Secondly, the system's effectiveness is quantitatively assessed using key statistical metrics. The system utilizes real-world insurance claim data to ensure quality and relevance, where the two datasets were collected from countries in the Gulf region. The findings reveal significant improvement in fraud detection at various activity levels; from doctor, provider, and patient, where the patient model reached 79 % precision. The system not only aligns well with human expert judgments but also demonstrates superior performance on the specified statistical metrics, indicating effective clustering and anomaly detection. Some real use cases were captured by the model and deeply investigated by human experts, which demonstrated advantages by the proposed approach in detecting fraud at multiple levels, of providers, doctors, and patients.

District vote e proportional vote: what is the best alternative for Brazil?

Brazilian legislative elections are defined through the proportional voting system that seeks to preserve the diversity of political groups existing in Brazilian society. This system, however, is widely criticized for producing a political system with high party fragmentation and great instability in the relationship between the executive and legislative powers. In this sense, some analysts argue that the best alternative for Brazil would be to replace the current proportional voting system with a majority voting system, as this would reduce party fragmentation and allow for greater political stability. It is not in the context of this discussion that this article is inserted. It aims to discuss the advantages and disadvantages of majority and proportional voting systems and their inclusion in the Brazilian electoral system.

Do mass layoffs affect voting behaviour? Evidence from the UK

AbstractHow bad are mass layoffs politically? We study this question across both regional and individual‐level datasets. Using a difference‐in‐difference framework with differential timing on constituency‐level data for the UK, we find no evidence that mass layoff announcements negatively affect incumbents – either locally or nationally – in the General Elections 2010, 2015, 2017 and 2019. Using individual data on party preferences, we demonstrate that our results are not an artefact of the UK majority voting system and associated tactical voting. We also find no evidence that the null results can be explained by increased outmigration of affected individuals or changes of candidates by political parties. We find evidence that economic expectations are not strongly affected by mass layoffs, which, although a surprising finding by itself, might help to explain the absence of an effect on political outcomes.

The Applicability of Humanitarian Intervention and the Responsibility to Protect During Armed Conflicts: Russia-Ukraine War in Focus

Purpose: Humanitarian intervention and the responsibility to protect have been at the centre of debate in the UN, among states and civil societies. The failure of the International Community to use military force to intervene in the case of Syria and the ongoing atrocities in Ukraine has weakened the enthusiasm that was kindled by the authorized use of force in Libya. This paper aimed at critically examining the situation in Russia-Ukraine in order to propose a way forward for the protection of civilians in Ukraine through humanitarian intervention.
 Methodology: To this end, the paper recalls the responsibility to protect bestowed on States and the International Community, evaluating the positive impacts of humanitarian intervention in Kosovo and Libya. Adopting a purely doctrinal research method, this paper questions why the international community is reluctant in using military intervention in the Russia-Ukraine situation in order to protect civilians, curb human rights violations and maintain peace and security.
 Findings: The study found out that the fear to trigger war in the whole of Europe and the fact that Russia is a permanent member of the Security Council with veto power have weakened this enthusiasm to intervene.
 Conclusion: The study concludes that there is necessity for humanitarian intervention to protect civilians in Ukraine by NATO and other States.
 Recommendation: This study recommends the use of the majority voting system in decisions to apply humanitarian intervention instead of the veto system. The study also recommend that permanent members of the Security Council who are involved in gross violation of human rights like Russia should be suspended and eventually removed as a Permanent member if violations persist.

Anomaly Detection and Repairing for Improving Air Quality Monitoring.

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants' concentrations can support the identification of urban areas with poor air quality and raise citizens' awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms' performances.

Optimizing the Quality of Predicting the ill effects of Intensive Human Exposure to Social Networks using Ensemble Method

People may quickly obtain information through a variety of channels including social media, blogs, websites, and other online resources. These platforms have made it possible for information to be shared more easily. As a result, the amount of time that individuals spend on various social networking applications has increased. This research predicts the effects of human exposure to social networks in the near future. In this work a competent model for predicting the ill-effects is provided, that is both accurate and efficient. This model represents a combination of the independent models that have been operating independently so far. Thus each of these models makes a forecast and ultimate selection is decided based on whether or not there exists a majority of convergence of results from the operation of various models. By employing the majority voting system, this strategy attempts to take the benefits of the predictions produced by all the models while also to reduce the inaccuracies generated by each model. Theoretically, this model should outperform the use of individual models in terms of performance. Important features are extracted from the datasets using the proposed model, and the extracted features are then classified using an ensemble model that consists of four popular machine learning models: support vector machines (SVMs), logistic regression (logistic regression), random forest (random forest classification), and neural networks (NN). We have analyzed our prediction performance with the existing methods for number of times by changing the train set and test set data. In all the cases our novel method has been predicting with 3% to 4% improved performance in accuracy, precision, F-1 score, and specificity. From the dataset it has been achievable to attain the highest training and testing accuracy from among the existing models.

Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems

Condorcet’s Jury Theorem states that the correct outcome is reached in direct majority voting systems with sufficiently large electorates as long as each voter’s independent probability of voting for that outcome is greater than 1/2. Previous research has found that switching to a hierarchical system always leads to an inferior result. Yet, in many situations direct voting is infeasible (e.g., due to high implementation or infrastructure costs), and hierarchical voting may provide a reasonable alternative. This paper examines differences in accuracy rates of hierarchical and direct voting systems for varying group sizes, abstention rates, and voter competences. We derive three main results. First, we prove that indirect two-tier systems differ most from their direct counterparts when group size and number are equal (i.e., when each equals [Formula: see text], where Nd is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equals [Formula: see text], where n is the number of hierarchical levels. Second, we show that while direct majority rule always outperforms indirect voting for homogeneous electorates, hierarchical voting gains in accuracy when either the number of groups or the number of individuals within each group increases. Third, we prove that when voter abstention and competency are correlated within groups, hierarchical systems can outperform direct voting. The results have implications beyond voting, including information processing in the brain, collective cognition in animal groups, and information aggregation in machine learning.

An information fusion approach for increased reliability of condition monitoring With homogeneous and heterogeneous sensor systems

In machinery condition monitoring, it is often vital to consider information from multiple sources due to possible sensor failure or signal distortion, which may result in misclassification of the health status. An issue with multiple sensor data fusion, however, is that the classification can be affected by conflicting results between sensor signals. The proposed method uses a novel three-module approach to information fusion in order to address the problem. Features corresponding to signal integrity are extracted and employed for training a one-class support vector machine to detect unwanted distortions or sensor failures. Different classifiers are trained for the different sensor types available and each signal recorded is used to determine machine health. Decision-level fusion is conducted through a majority voting system using the integrity scores derived from the OCSVMs and the separate classification results. From this, a dynamically weighted fault diagnosis based on sensor signal quality is obtained. Experimental verification using vibration and acoustic emission signals show that the framework is viable and allows for an increased reliability in machinery health diagnosis.

O-005 Reducing inter-observer and intra-observer variability of embryo quality assessment using deep learning

Abstract Study question Does deep learning for embryo quality assessment reduce inter-observer variability? Summary answer An AUC of 87.65% was obtained for predicting blastocyst quality with a deep-learning algorithm trained by five embryologists who had good agreement between themselves What is known already Time-Lapse (TL) allows continuous observation of embryo development in a controlled and stable environment. Recently the use of deep learning, in particular convolutional neural networks have been introduced to enhance blastocyst image classification using the growing TL image and video data. Study design, size, duration A total of 409 embryos (5 images per embryo for a total of 2 045 images) were included in this retrospective study between 2016 and 2020. Participants/materials, setting, methods A machine-learning algorithm (Retinanet) was trained to recognize 2 045 blastocyst images from 409 embryos on 2560x1928 images and output 500x500 images with the blastocyst centered on the image. Five embryologists classified the blastocysts using Gardner’s grading system. Each image was associated with one final grade using a majority voting system. The dataset was split into a training and validation set (1 640 images plus data augmentation) and a testing set (405 images). Main results and the role of chance Fair agreement was found between the 5 embryologists when grading the embryo using Gardner’s grading system, with a maximum weighted kappa score of 39.60% reached. As for the intra-observer variability, we show that for the same embryologist grading the same embryo after a 3 month “wash out” period, in 12% of the cases the embryologist changes the grade and the fate of the embryo, meaning that an embryo that was transferred/frozen during the first annotation period was discarded during the second one, or an embryo that was discarded during the first annotation period was transferred/frozen during the second one. An Area Under the Curve (AUC) of 87.65% was obtained when testing the quality of 81 embryos (405 images) after training our algorithm on 54 038 images. For external validation we tested the algorithm with annotations of the test set from embryologists coming from another fertility center. An AUC of 82.72% was obtained. Limitations, reasons for caution The scarce number of images available in our training set compared with data sets from other more consequent clinics, and the fact that the algorithm was trained by embryologists does not suppress variability entirely. The GoogLeNet algorithm was not fined tune and was used as is. Wider implications of the findings AI is showing precious value the field of embryology, from enhancing blastocyst quality prediction to removing inter-observer subjectivity. A possible evolution to our framework would be to predict the Gardner’s grading system for each morphological parameter. Trial registration number not applicable

FACTORS CAUSING PARTAI DEMOKRAT INDONESIA VICTORY IN THE 2019 LEGISLATIVE ELECTION IN LAMPUNG PROVINCE

The Indonesian Democratic Party of Struggle in Lampung Province is one of several parties that participated in the 2019 legislative elections and through the rule of law, the PDI-P has the most seats in the Lampung Provincial DPRD, then has the right to propose its cadres to sit as Chair of the Lampung Provincial DPRD.. The focus of writing the paper is the factor that caused the PDI-P's victory in the 2019 Legislative Election in Lampung Province.Peinfluence figures have a dominant role in the electoral system that uses the majority vote system. Competence and credibility of candidates for imaging and electability of the candidates concerned. The key to the PDI-P's victory in the 2019 election in Lampung Province is having militancy capital or traditional voters who are loyal to the party's choice. The nationally valid traditional electorate is the first determining factor. Next is the high level of penetration of legislative candidates in influencing voters.

A Vote-Based Architecture to Generate Classified Datasets and Improve Performance of Intrusion Detection Systems Based on Supervised Learning

An intrusion detection system (IDS) is an important tool to prevent potential threats to systems and data. Anomaly-based IDSs may deploy machine learning algorithms to classify events either as normal or anomalous and trigger the adequate response. When using supervised learning, these algorithms require classified, rich, and recent datasets. Thus, to foster the performance of these machine learning models, datasets can be generated from different sources in a collaborative approach, and trained with multiple algorithms. This paper proposes a vote-based architecture to generate classified datasets and improve the performance of supervised learning-based IDSs. On a regular basis, multiple IDSs in different locations send their logs to a central system that combines and classifies them using different machine learning models and a majority vote system. Then, it generates a new and classified dataset, which is trained to obtain the best updated model to be integrated into the IDS of the companies involved. The proposed architecture trains multiple times with several algorithms. To shorten the overall runtimes, the proposed architecture was deployed in Fed4FIRE+ with Ray to distribute the tasks by the available resources. A set of machine learning algorithms and the proposed architecture were assessed. When compared with a baseline scenario, the proposed architecture enabled to increase the accuracy by 11.5% and the precision by 11.2%.

High-Capacity Reversible Data Hiding in Encrypted 3d Mesh Models Based on Multi-Msb Prediction

As a new generation of digital media for covert transmission, three-dimension (3D) mesh models are frequently used and distributed on the network. Facing the huge massive of network data, it is urgent to study a method to protect and store this large amounts of data. In this paper, we proposed a high capacity reversible data hiding in encrypted 3D mesh models. This method divides the vertices of all 3D mesh into embedded sets and sets based on the parity of the index. In addition, the multiple most significant bit (Multi-MSB) prediction reserved space is used to adaptively embed secret message, and the auxiliary information is compressed by arithmetic coding to further free up redundant space of the 3D mesh models. We use the majority voting system(MSV) principle to restore the original mesh model with high quality. The experimental results show that our method achieves a higher embedding capacity compared with state-of-the-art RDH-ED methods on 3D mesh models and can restore the original 3D mesh models with high quality.

Classification of the Trap-Neuter-Return Surgery Images of Stray Animals Using Yolo-Based Deep Learning Integrated with a Majority Voting System

Trap-neuter-return (TNR) has become an effective solution to reduce the prevalence of stray animals. Due to the non-culling policy for stray cats and dogs since 2017, there is a great demand for the sterilization of cats and dogs in Taiwan. In 2020, Heart of Taiwan Animal Care (HOTAC) had more than 32,000 cases of neutered cats and dogs. HOTAC needs to take pictures to record the ears and excised organs of each neutered cat or dog from different veterinary hospitals. The correctness of the archived medical photos and the different shooting and imaging angles from different veterinary hospitals must be carefully reviewed by human professionals. To reduce the cost of manual review, Yolo’s ensemble learning based on deep learning and a majority voting system can effectively identify TNR surgical images, save 80% of the labor force, and its average accuracy (mAP) exceeds 90%. The best feature extraction based on the Yolo model is Yolov4, whose mAP reaches 91.99%, and the result is integrated into the voting classification. Experimental results show that compared with the previous manual work, it can decrease the workload by more than 80%.

On the reliability of a voting system under cyber attacks

Voting systems have been widely used in many areas due to their technical decision making abilities. However, when a voting system operates on any internet platform, it faces a number of potential vulnerabilities including malware and denial of service attacks. In this work, we study the reliability of a majority voting system under cyber threats. Three multiple attack scenarios are considered including targeted, random, and dynamic attacks. We show that under some conditions, the reliability of a voting system decreases with the number of attacked voting components for the targeted attacks; however, for the random attacks, the reliability of a voting system can increase with the number of attacked voting components. For the dynamic attacks, it is discovered that the reliability of a voting system is significantly impacted by the attack rate and the random proposition time. We further develop efficient algorithms for computing the reliability of voting systems which can be employed in practice.

Adapting Virtual Embodiment Through Reinforcement Learning.

In Virtual Reality, having a virtual body opens a wide range of possibilities as the participant's avatar can appear to be quite different from oneself for the sake of the targeted application (e.g., for perspective-taking). In addition, the system can partially manipulate the displayed avatar movement through some distortion to make the overall experience more enjoyable and effective (e.g., training, exercising, rehabilitation). Despite its potential, an excessive distortion may become noticeable and break the feeling of being embodied into the avatar. Past researches have shown that individuals have a relatively high tolerance to movement distortions and a great variability of individual sensitivities to distortions. In this article, we propose a method taking advantage of Reinforcement Learning (RL) to efficiently identify the magnitude of the maximum distortion that does not get noticed by an individual (further noted the detection threshold). We show through a controlled experiment with subjects that the RL method finds a more robust detection threshold compared to the adaptive staircase method, i.e., it is more able to prevent subjects from detecting the distortion when its amplitude is equal or below the threshold. Finally, the associated majority voting system makes the RL method able to handle more noise within the forced choices input than adaptive staircase. This last feature is essential for future use with physiological signals as these latter are even more susceptible to noise. It would then allow to calibrate embodiment individually to increase the effectiveness of the proposed interactions.

Age-group determination of living individuals using first molar images based on artificial intelligence

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

Two‐level pruning based ensemble with abstained learners for concept drift in data streams

AbstractMining data streams for predictive analysis is one of the most interesting topics in machine learning. With the drifting data distributions, it becomes important to build adaptive systems which are dynamic and accurate. Although ensembles are powerful in improving accuracy of incremental learning, it is crucial to maintain a set of best suitable learners in the ensemble while considering the diversity between them. By adding diversity‐based pruning to the traditional accuracy‐based pruning, this paper proposes a novel concept drift handling approach named Two‐Level Pruning based Ensemble with Abstained Learners (TLP‐EnAbLe). In this approach, deferred similarity based pruning delays the removal of under performing similar learners until it is assured that they are no longer fit for prediction. The proposed scheme retains diverse learners that are well suited for current concept. Two‐level abstaining monitors performance of learners and chooses the best set of competent learners for participating in decision making. This is an enhancement to traditional majority voting system which dynamically chooses high performing learners and abstains the ones which are not suitable for prediction. In our experiments, it has been demonstrated that TLP‐EnAbLe handles concept drift more effectively than other state‐of‐the‐art algorithms on nineteen artificially drifting and ten real‐world datasets. Further, statistical tests conducted on various drift patterns which include gradual, abrupt, recurring and their combinations prove efficiency of the proposed approach.

Automatic Registration Between Dental Cone-Beam CT and Scanned Surface via Deep Pose Regression Neural Networks and Clustered Similarities.

Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite for surgical planning for dental implants or orthognathic surgery. We propose a novel method that performs fully automatic registration between a cone-beam CT image and an optically scanned model. To build a robust and automatic initial registration method, deep pose regression neural networks are applied in a reduced domain (i.e., two-dimensional image). Subsequently, fine registration is performed using optimal clusters. A majority voting system achieves globally optimal transformations while each cluster attempts to optimize local transformation parameters. The coherency of clusters determines their candidacy for the optimal cluster set. The outlying regions in the iso-surface are effectively removed based on the consensus among the optimal clusters. The accuracy of registration is evaluated based on the Euclidean distance of 10 landmarks on a scanned model, which have been annotated by experts in the field. The experiments show that the registration accuracy of the proposed method, measured based on the landmark distance, outperforms the best performing existing method by 33.09%. In addition to achieving high accuracy, our proposed method neither requires human interactions nor priors (e.g., iso-surface extraction). The primary significance of our study is twofold: 1) the employment of lightweight neural networks, which indicates the applicability of neural networks in extracting pose cues that can be easily obtained and 2) the introduction of an optimal cluster-based registration method that can avoid metal artifacts during the matching procedures.