Voting Method Research Articles

Intelligent decision support system using nested ensemble approach for customer churn in the hotel industry

ABSTRACT Since customer retention costs much less than attracting new customer, the problem of customer churn is a major challenge in various fields of work and particularly Hotel Industry. In this research, a solution based on an intelligent decision support system using text mining and nested ensemble techniques is presented, which combines the advantages of stacking and voting methods. In the proposed system, after the text mining of the data collected from the hotels of Kish Island, the effective feature selection is done using the gravity search algorithm. In the first level of nested ensemble technique method, stacking deep learning methods are used. Voting is used in the MetaClassifier section, which includes Random Forest, Xgboost and Naïve Bayes methods. The results of the implementation and comparison of the proposed system, show that the performance of the proposed system has increased the accuracy by 0.04 compared to the best existing method.

Crop mapping through a hybrid machine learning and deep learning method

A major function of satellite imagery is crop identification and separation, which is of key importance in acreage estimation, yield estimation, and agricultural management. This study aimed to examine the capability of the object-based method and machine learning and deep learning algorithms in combining optical and radar images to generate crop-type maps. For this purpose, Sentinel-1 and Sentinel-2 satellite images were used for the 2019 crop year in the northwest of Ardabil, Iran. Image segmentation was performed through the multiscale method and various spectral, textural, and radar backscattering characteristics. The main features were identified using Random Forest feature selection and fed to support vector machine, random forest, and convolutional neural network algorithms. The results of these algorithms were combined via the majority voting method. The results showed that the convolutional neural network algorithm performed better than other methods with an overall accuracy of 88.03% and a kappa coefficient of 85.99%. Meanwhile, the support vector machine method showed poor performance compared to the other two methods. According to the findings, combining the results of these algorithms through the majority voting method yielded the best crop identification result with an overall accuracy of 90.34% and a Kappa coefficient of 88.72%.

The incomplete analytic hierarchy process and Bradley–Terry model: (In)consistency and information retrieval

Several methods of preference modeling, ranking, voting and multi-criteria decision-making include pairwise comparisons. It is usually simpler to compare two objects at a time, furthermore, some relations (e.g., the outcome of sports matches) are naturally known for pairs. This paper investigates and compares pairwise comparison models and the stochastic Bradley–Terry model. It is proved that they provide the same priority vectors for consistent (complete or incomplete) comparisons. For incomplete comparisons, all filling in levels are considered. Recent results identified the optimal subsets and sequences of multiplicative/additive/reciprocal pairwise comparisons for small sizes of items (up to n=6). Simulations of this paper show that the same subsets and sequences are optimal in the case of the Bradley–Terry and the Thurstone models as well. This somehow surprising coincidence suggests the existence of a more general result. Further models of information and preference theory are subject to future investigation to identify optimal subsets of input data.

Multi-model weighted voting method based on convolutional neural network for human activity recognition

Multi-model weighted voting method based on convolutional neural network for human activity recognition

Developing reliable classification of dual IDO1/TDO inhibitors using data fusion and majority voting

Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) are promising dual-targeting inhibitors in cancer and neurodegenerative diseases treatment. Data fusion of receptor-based and ligand-based information of dual IDO1/TDO inhibitors were employed for active/inactive classification performance. A reliable decision making procedure was used here to identify active/inactive dual IDO1/TDO inhibitors using majority voting method and pools of individual classifications instead of individual models. All classification models were validated using prediction set, cross-validation and y-scrambling tests. The classification outcomes indicate that the sensitivity, specificity, precision, accuracy, G-mean and F1 score values increases up to ∼90% using data fusion and majority voting method. Compare to individual classification models with a single prediction point, the majority voting method has more reliable results due to the integration of the pool of individual classification models. This classification strategy may lead to more reliable identification of active/inactive dual-targeting inhibitors in cancer immunotherapy. Communicated by Ramaswamy H. Sarma

Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method

When developing a solution for fault diagnosis, cost is a critical factor that must be considered to ensure a practical and feasible final product. To reduce expenses, the authors conducted a feasibility study using a low-cost MEMS accelerometer in conjunction with an Arduino Uno. This study aimed to determine the effect of sample length on fault classification by varying it from 50 to 5000 to identify the optimal value for this particular application. After finalizing the parameters, the vibration signal was acquired using the MEMS sensor and Arduino Uno. The resulting classification accuracy using the decision tree was 90.2%, a satisfactory result that can be further improved for industrial applications. To enhance the accuracy of decision tree classifiers, the authors proposed a novel approach known as the probabilistic voting method. By implementing this method and utilizing an Arduino Uno and MEMS sensor (ADXL335), they were able to drastically cut down costs while achieving remarkable classification accuracy. The implementation of the probabilistic voting method further elevated the accuracy to an astounding 98.5%, setting a new benchmark in the field.

Neonatal Hypoxic-Ischemic Encephalopathy Grading from Multi-Channel EEG Time-Series Data Using a Fully Convolutional Neural Network

A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels.

Dynamic wide‐area cooperative protection: A new approach

AbstractThis paper introduces a new dynamic wide‐area cooperative protection based on cooperative control of distributed multi‐agent systems and graph theoretical methods. For power system cooperative protection, a new voting method is designed that manages each agent's cooperation according to each protection area. Cooperative protection areas work together and make a wide area of cooperative protection for the specific protection scheme. As the power system configuration varies, the voting management and the wide‐area cooperative protection dynamically change. The proposed protection method decisions are collective, while a centralized master does not exist, and local information is used. A fault detection code and a wide‐area fault detection code are presented for fault detection. The suggested dynamic wide‐area cooperative protection scheme is tested on an IEEE 39‐bus test system. The proposed protection performance is studied as backup protection for distance relays during normal, stable power swings, unstable power swings, and load encroachment conditions.

An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM

Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. Accurate and reliable predictive tools that enable early risk stratification can facilitate interventions to improve patient outcomes; however, such tools are currently lacking. In this study, we developed and validated novel ensemble learning models based on soft voting and stacking methods to predict in-hospital mortality from IS in the ICU using two public databases: MIMIC-IV and eICU-CRD. Additionally, we identified the key predictors of mortality and developed a user-friendly online prediction tool for clinical use. The soft voting ensemble model, named ICU-ISPM, achieved an AUROC of 0.861 (95% CI: 0.829–0.892) and 0.844 (95% CI: 0.819–0.869) in the internal and external test cohorts, respectively. It significantly outperformed the APACHE scoring system and was more robust than individual models. ICU-ISPM obtained the highest performance compared to other models in similar studies. Using the SHAP method, the model was interpretable, revealing that GCS score, age, and intubation were the most important predictors of mortality. This model also provided a risk stratification system that can effectively distinguish between low-, medium-, and high-risk patients. Therefore, the ICU-ISPM is an accurate, reliable, interpretable, and clinically applicable tool, which is expected to assist clinicians in stratifying IS patients by the risk of mortality and rationally allocating medical resources. Based on ICU-ISPM, an online risk prediction tool was further developed, which was freely available at: http://ispm.idrblab.cn/.

DB-COVIDNet: A Defense Method against Backdoor Attacks

With the emergence of COVID-19 disease in 2019, machine learning (ML) techniques, specifically deep learning networks (DNNs), played a key role in diagnosing the disease in the medical industry due to their superior performance. However, the computational cost of deep learning networks (DNNs) can be quite high, making it necessary to often outsource the training process to third-party providers, such as machine learning as a service (MLaaS). Therefore, careful consideration is required to achieve robustness in DNN-based systems against cyber-security attacks. In this paper, we propose a method called the dropout-bagging (DB-COVIDNet) algorithm, which works as a robust defense mechanism against poisoning backdoor attacks. In this model, the trigger-related features will be removed by the modified dropout algorithm, and then we will use the new voting method in the bagging algorithm to achieve the final results. We considered AC-COVIDNet as the main inducer of the bagging algorithm, which is an attention-guided contrastive convolutional neural network (CNN), and evaluated the performance of the proposed method with the malicious COVIDx dataset. The results demonstrated that DB-COVIDNet has strong robustness and can significantly reduce the effect of the backdoor attack. The proposed DB-COVIDNet nullifies backdoors before the attack has been activated, resulting in a tremendous reduction in the attack success rate from 99.5% to 3% with high accuracy on the clean data.

A new hybrid credit scoring ensemble model with feature enhancement and soft voting weight optimization

The explosive development of artificial intelligence (AI) has reshaped all aspects of life, including credit scoring. At the same time, the rapid expansion of the consumer finance industry has led to a huge demand. In this study, a new hybrid ensemble model with feature enhancement and soft voting weight optimization is proposed to achieve superior predictive power for credit scoring. For mining and characterizing the implicit information of the features, a new voting-based feature enhancement method is proposed to adaptively integrate the outlier detection and clustering capabilities through the weighted voting mechanism to form a feature-enhanced training set. To balance the feature-enhanced training set precisely and effectively, a new bagging-based undersampling method is proposed to obtain a balanced training set by undersampling from the negative binomial distribution through the bagging strategy. To maximize the performance of the model, a new weight-optimized soft voting method is proposed to optimize the soft voting weights of the base classifiers in the classifier ensemble using the COBYLA algorithm and then constructing the stacking-based ensemble model. Five datasets and five evaluation indicators were used for evaluation. The experimental results demonstrate the superior performance of the proposed model and prove its robustness and effectiveness.

Clinical Usability-Oriented Automatic Contour Quality Evaluation for Deep Learning Auto-Segmentation

Various auto-segmentations, including deep learning auto-segmentation (DLAS), are being increasingly adopted in radiotherapy. A common method to evaluate quality of auto-segmented contours uses thresholds of various quantitative metrics (e.g., dice similarity coefficient (DSC), mean distance to agreement (MDA), etc.) that are often averaged over all contour slices. This method fails to detect contour errors on individual slices, thus, does not reflect the current clinical practice (slice-by-slice evaluation) and the clinical usability (e.g., expected contour editing time). In addition, the use of multi-metrics is generally not easy to interpret. This work aims to develop a novel contour quality classification (CQC) model to evaluate auto-segmented contours based on their clinical applicability. The CQC method was designed to classify a contour on a slice into acceptable, minor edit or major edit category, based on the expected editing effort/time. Organ-specific supervised ensemble tree classification models were trained to relate the slice-based quality category with the combination of seven commonly used calculatable quantitative metrics (i.e., DSC, MDA, Hausdorff 95% distance, surface DSC, added path length (APL), slice area and relative APL). The proposed method was demonstrated by training CQC models using DLAS contours of five abdominal organs (i.e., pancreas, duodenum, stomach, and small and large bowels) from 50 MRI sets and evaluating on 20 MRI and 9 CT testing sets. These test datasets were labelled by six individual observers and the consensus labels were generated through majority vote method. The model performance was evaluated using accuracy (acc), and risk rate (RR, the percentage of unacceptable slices mislabeled as acceptable) and compared with inter-observer variation and baseline threshold-based method. Compared to the majority vote labels, the obtained CQC models achieved a mean accuracy of 95.8% ([94.5%-99.1%]) and 94.3% ([90.6%-96.9%]), and the mean RR of 0.8% ([0.3%-1.3%]) and 0.7% ([0%-1.1%]) for the MRI and CT testing sets, respectively. The CQC performance was comparable to the inter-observer variation and significantly higher than those from the threshold-based method with single or multiple metrics. The execution time on a typical abdominal dataset (e.g., 70 slices) took less than 3 seconds. Table 1 CQC models performance for different organs CONCLUSION: The proposed CQC model can classify the quality of a contour slice with high accuracy. This slice-based single-output evaluation method better reflects the current clinical practice and may be used to evaluate/compare performance of DLAS on any image modality, facilitating its clinical implementation and quality assurance.

Examining Occupant-Comfort Responses to Indoor Humidity Ratio in Conventional and Vernacular Dwellings: A Rural Indian Case Study

Optimum indoor humidity is often associated with comfort and overall well-being. Occupant comfort is often evaluated with a focus on “thermal comfort” using the PMV (predicted mean vote), PDD (predicted percentage of dissatisfied), and adaptive thermal comfort models. Humidity-determined comfort parameters, like skin and respiratory comfort, are well acknowledged in the scientific community, but strangely not considered for indoor comfort computations. This study proposes a new computational approach for describing and evaluating humidity-related skin comfort in buildings using skin temperature, evaporative loss, and skin wettedness as critical parameters. The Development and validation of the computational model was demonstrated through a case study in a rural Indian context. The case study involves real-time monitoring of indoor environmental parameters and humidity-determined occupant comfort votes recorded through a novel aggregated humidity comfort vote method. The simulation results were compared with the community comfort/health survey. It was observed that, even at neutral skin temperatures, an increase in skin wettedness increases the thermal sensation vote. Clothing varies according to gender, community, and personal preferences, influencing physiological parameters which determine comfort. The acceptable humidity ratio was found to be in the range of 17.4 to 22.6 g-wv/kg-da for Indian participants. Including humidity-related comfort parameters in building simulation tools would aid in selecting building materials for improved indoor comfort.

Implementation of AES-256 Algorithm in Android-Based E-Voting Data Security

E-voting is a method of voting and vote counting in general elections using digital devices. There are still many errors and fraud that occur during village head elections in Indonesia. Then the idea was created to carry out village head elections by utilizing existing technological developments, especially Android-based smartphone technology. The data source used in this study is secondary data which uses population data of citizens who will exercise their right to vote, vote data for citizens' voting rights, and data from the results of vote acquisition during the village head election process. In this study, a number of important data were needed in the village head election process which was carried out using documentation techniques. This research produced an Android-based application system for securing e-voting data for village head elections using the AES-256 cryptographic algorithm called Vodes. The application produced in this study was built using the Kotlin programming language on the Android frontend side by utilizing the Android Studio IDE as a tool in the development process. While on the backend side it uses the Javascript programming language using the Node js framework. The database used in the Vodes application uses MySQL. Based on the results of this study, it can be concluded that an e-voting data security application for village head elections has been created using the AES-256 cryptographic algorithm. The application produced in this study has succeeded in properly securing all aspects of the data in the village head election process

Joint Multi-Objective Optimization for Radio Access Network Slicing Using Multi-Agent Deep Reinforcement Learning

Radio access network (RAN) slices can provide various customized services for next-generation wireless networks. Thus, multiple performance metrics of different types of RAN slices need to be jointly optimized. However, existing efforts in multi-objective optimization problem (MOOP) for RAN slicing are only in the scalar form, which is difficult to achieve simultaneous optimization. In this paper, we consider a non-scalar MOOP for RAN slicing with three types of slices, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the high-bandwidth slice, the low-delay slice, and the wide-coverage slice over the same underlying physical network. We jointly optimize the throughput, the transmission delay, and the coverage area by user-oriented dynamic virtual base stations (vBSs)' deployment, and sub-channel and power allocation. An improved multi-agent deep deterministic policy gradient (IMADDPG) algorithm, having the characteristics of centralized training and distributed execution, is proposed to solve the above non-deterministic polynomial-time hard (NP-hard) problem. The rank voting method is introduced in the inference process to obtain near-Pareto optimal solutions. Simulation results verify that the proposed scheme can ensure better performance than the traditional scalar utility method and other benchmark algorithms. The proposed scheme has the advantage of flexibly approaching any point of the Pareto boundary, while the traditional scalar method only subjectively approaches one of the Pareto optimal solutions. Furthermore, our proposal strikes a compelling tradeoff among three types of RAN slices due to the non-dominance between Pareto optimal solutions.

Improving Plant Disease Recognition Through Gradient-Based Few-shot Learning with Attention Mechanisms

This study investigates the use of few-shot learning algorithms to improve classification performance in situations where traditional deep learning methods fail due to a lack of training data. Specifically, we propose a few-shot learning approach using the Almost No Inner Loop (ANIL) algorithm and attention modules to classify tomato diseases in the Plant Village dataset. The attended features obtained from the five separate attention modules are classified using a Multi Layer Perceptron (MLP) classifier, and the soft voting method is used to weigh the classification scores from each classifier. The results demonstrate that our proposed approach achieves state-of-the-art accuracy rates of 97.05% and 97.66% for 10-shot and 20-shot classification, respectively. Our approach demonstrates the potential for incorporating attention mechanisms in feature extraction processes and suggests new avenues for research in few-shot learning methods.

Split Cycle: a new Condorcet-consistent voting method independent of clones and immune to spoilers

We propose a Condorcet-consistent voting method that we call Split Cycle. Split Cycle belongs to the small family of known voting methods satisfying the anti-vote-splitting criterion of independence of clones. In this family, only Split Cycle satisfies a new criterion we call immunity to spoilers, which concerns adding candidates to elections, as well as the known criteria of positive involvement and negative involvement, which concern adding voters to elections. Thus, in contrast to other clone-independent methods, Split Cycle mitigates both “spoiler effects” and “strong no show paradoxes.”

Support for ranked choice voting across party and race: results from a national survey experiment

ABSTRACT This paper presents results from a series of survey experiments testing the effects of various arguments on relative evaluations of ranked-choice (RCV) and choose-one ballots. We examine data from the 2020 Collaborative Multiracial Post-Election Survey (CMPS), allowing for comparisons across racial groups. Reformers have emphasized the voter’s ability to have their vote transferred to a lower-ranked candidate as a positive aspect of the system. However, we find that an explanation of RCV’s vote-transfer properties does not increase public support. Furthermore, when given a choice between the single and ranked voting methods, a majority within each racial group prefers the single vote. However, Latino, Asian American, and MENA respondents express a stronger preference for RCV than white respondents. Democrats also evaluate RCV more favorably than Republicans. Furthermore, communicating that RCV helps elect more women and people of color increases support among Democrats but not Republicans across most racial groups. This is consistent with other findings around growing partisan differences in views toward diversity and descriptive representation. Finally, a message emphasizing voter confusion has little impact on public support for RCV. Arguments about the impact of electoral systems are found to influence public preferences more than descriptions of those systems’ key properties.

Secure Voting Website Using Ethereum and Smart Contracts

Voting is a democratic process that allows individuals to choose their leaders and voice their opinions. However, the current situation with physical voting involves long queues, paper-based ballots, and security challenges. Blockchain-based voting models have appeared as a method to address the limitations of traditional voting methods. As blockchain is distributed and decentralized, which uses hash functions for securing transactions, it dramatically improves the existing voting system. These digital platforms eliminate the need for physical presence, reduce paperwork, and ensure the integrity of votes through transparent and tamper-proof blockchain technology. This paper introduces a blockchain-based voting model to enhance accessibility, security, and efficiency in the voting process. The research focuses on developing a robust and user-friendly voting system by leveraging the advantages of decentralized technology. The proposed model employs Ethereum as the underlying blockchain platform through an innovative and iterative approach. The model uses Smart contracts to record and validate votes, while AI-based facial recognition technology is integrated to verify the identity of voters. Rigorous testing and analysis are conducted to validate the effectiveness and reliability of the proposed blockchain-based voting model. The system underwent extensive simulation scenarios and stress tests to evaluate its performance, security, and usability.

Multiclass Email Classification by Using Ensemble Bagging and Ensemble Voting

Email is a common communication technology in modern life. The more emails we receive, the more difficult and time consuming it is to sort them out. One solution to overcome this problem is to create a system using machine learning to sort emails. Each method of machine learning and data sampling result in different performance. Ensemble learning is a method of combining several learning models into one model to get better performance. In this study we tried to create a multiclass email classification system by combining learning models, data sampling, and several data classes to obtain the effect of Ensemble Bagging and Ensemble Voting methods on the performance of the macro average f1 score, and compare it with non-ensemble models. The results of this study show that the sensitivity of Naïve Bayes to imbalance data is helped by the Ensemble Bagging and Ensemble Voting method with ∆P (delta performance) of range 0.0001 – 0.0018. Logistic Regression has performance with Ensemble Bagging and Ensemble Voting by ∆P of range 0.0001-0.00015. Decision Tree has lowest performance compared to others with ∆P of -0.01