Borda Count Research Articles

DHHoE: Deep hybrid homogenous ensemble for digital histological breast cancer classification

AbstractThe progress of deep learning architectures, machine learning models and pathology slide digitization is an encouraging step toward meeting the growing demand for more precise classification and prediction diagnosis for the breast tumours. The BreakHis dataset with four magnification factors (40X, 100X, 200X and 400X), as well as seven deep learning architectures used for feature extraction (DenseNet 201, Inception ResNet V2, Inception V3, ResNet 50, MobileNet V2,VGG16 and VGG19), four machine learning models for classification (MLP, SVM, DT, and KNN), and two combination rules (hard and weighted voting) were investigated in this paper to design and evaluate a new proposed approach consisting of building deep hybrid homogenous ensemble. Additionally, the best proposed models were compared to deep stacked, deep bagging, deep boosting, and deep hybrid heterogenous ensemble to choose the best strategy in building deep ensemble learning techniques. The four performance measures accuracy, precision, recall, and F1‐score were used in the empirical evaluations, as well as 5‐fold cross‐validation, the Scott Knott statistical test, and the Borda Count voting method. The results demonstrated the new approach's potential since it outscored both singles and other deep ensemble learning strategies, achieving accuracy values of 98.3% and 97.7% for the MFs 40X, 100X and 200X, 400X, respectively. The empirical results demonstrated that the proposed ensembles are impactful for histopathological breast cancer images classification, and they provided a promising tool to assist pathologists in the diagnosis of breast cancer.

Deep learning based one step and multi‐steps ahead forecasting blood glucose level

AbstractEnabling diabetic patients to predict their Blood Glucose Levels (BGL) is a crucial aspect of managing their metabolic condition, as it allows them to take appropriate measures to avoid hypo or hyperglycemia. Machine Learning (ML) and Deep Learning (DL) techniques have made this possible, and this paper evaluates and compares the performance of five distinct ML/DL models including: Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Support Vector Regression (SVR), Gated Reccurent Unit (GRU) and Deep Belief Network (DBN) for forecasting BGL, by applying two different forecasting methods, namely One Step Ahead (OSF) and Multi‐Step Ahead (MSF) comprising five different variants. The performance is evaluated based on four metrics: Mean Absolute Error (MAE), Mean Magnitude Relative Error (MMRE), Root Mean Square Error (RMSE) and Predictive Level (PRED). Additionally, the statistical significance of the regressors was evaluated using the Scott‐Knott (SK) test, while the Borda Count (BC) voting system was employed to rank them. The results indicate that the best performance was achieved with OSF using GRU. Furthermore, the effectiveness of an MSF strategy depends on the ML/DL technique used, and the best combinations were DBN with DirRec, DBN with Recursive, SVR with Recursive and SVR with DirRec. Additionally, DirRec was found to be the best strategy, as it consistently ranked first regardless of the ML/DL technique used.

An evolutionary bargaining framework for allocating water and reclaimed wastewater in agricultural regions

Abstract This paper presents a new framework for modeling the bargaining process among stakeholders by coupling social choice and bargaining methods. Based on this framework, two methods of evolutionary bargaining coupled with Borda count (BBC) and evolutionary bargaining coupled with pairwise voting (BPV) are proposed, and the results of applying them to resolve the challenging problems of allocating water and reclaimed wastewater in agricultural regions are analyzed. After proposing some candidate scenarios of allocating water and reclaimed wastewater, non-dominated scenarios are determined. Then, in the first level of bargaining, using a social choice technique, each stakeholder chooses the most desirable scenario out of the non-dominated ones, regardless of the utilities of other stakeholders. The selected scenarios by all stakeholders can provide them an estimate of other stakeholders' expected utilities. This enables each stakeholder in the next step of bargaining to suggest a scenario that improves their own utility, while providing a minimum acceptable utility of other stakeholders. If the bargaining results in more than one scenario, a social choice method is applied to find the most preferred scenario. The applicability and performance of the proposed framework are evaluated by applying it to the Varamin plain, in the south-east of Tehran, Iran.

Geothermal flow in Northern Morocco: A machine learning approach

To model the geothermal flow in northern Morocco, we apply a machine learning (ML) approach by analyzing the geological and geophysical data at boreholes, where direct heat flow data is available. The data includes geothermal flow, geothermal gradient, Bouguer gravity, aero-magnetic field, tectonic fracture density and the proximity level to the major faults, thermal spring data (location, temperature, density), geochronological age of formations, Earthquake data (density, magnitude, location), and the proximity level to the recent volcanism in Morocco. These data represent factors that generally control the spatial variation of geothermal flow. For this purpose, we used four ML techniques: Multi-Layer Perceptron, Support Vector Regression, K-Nearest Neighbors, and Decision trees, and examined the impact of parameter settings for each ML technique on the performance results. In specifically, we examine two parameter tuning methods: Grid Search (GS) and the Python Tool's default parameters. To determine the significance of the performance differences and rank ML techniques according to their performances, the Skott-Knott test, and the Borda Count voting system were investigated. We identified the optimized MLP by means of GS as the best ML technique. The cartographic representation of predicted geothermal flow values, by the optimized MLP (GS + MLP) model over the northern Morocco, shows areas of high geothermal flow values. These areas often correspond to magmatic intrusions at depth connected to the regional geodynamic context. The lowest values of geothermal flow are less than 40 mW m−2 and are mainly predicted in the Anti-Atlas chain of Precambrian age and in which is part of the West African Craton. This method is a new approach that may help to identify areas with high geothermal potential based on geological and geophysical data.

Prediction of breast cancer using machine learning algorithms on different datasets

Breast cancer is a disease that is becoming more and more common day by day, causing emotional and behavioral reactions and having fatal consequences if not detected early. At this point, traditional methods are insufficient, especially in early diagnosis. In this context, this study aimed to predict breast cancer by using machine learning (ML) algorithms on different datasets and to demonstrate the applicability of these algorithms. Algorithm performances were compared on balanced and unbalanced datasets, taking into account the performance metrics obtained in applications on different datasets. In addition, a model based on the Borda Voting method was developed by including the results obtained from four different algorithms (NB, KNN, DT, and RF) in the process. The prediction values obtained from each algorithm were written in different columns on the same excel file and the most repetitive value was accepted as the final result value. The developed model was tested on real data consisting of 60 records and the results were analyzed. When the results were examined, it was seen that higher performance was obtained with the proposed RF model compared to similar studies in the literature. Finally, the prediction results obtained with the developed model revealed the applicability of ML algorithms in the diagnosis of breast cancer.

Impact of a priori positive information on the results of voting methods

The aim of this paper is to present the results of experiments relating to voting methods based on the bounded rationality theory. The research demonstrated that a positive nudge changes the voting results. The study focused on three methods of voting: the Borda Count method, the Condorcet winner method and the anti-manipulation method. In a laboratory experiment, the subjects were asked to select the best musician. They were to manipulate their voting so that a predetermined winner is chosen. In the first voting, the subjects did not receive any a priori information, while in the second voting, some a priori information was provided, i.e. the true, objective ranking of the musicians. What followed was another voting. It was initially assumed that the participants would manipulate their voting the same way as in the first voting. The results, however, were different. The obtained second ranking of musicians was closest to the true, objective ranking, thus proving that the manipulation effect was neutralised by the a priori positive information about the true, objective order.

Estimate of the cost per responder for treatment with biological therapies of moderate-to-severe plaque psoriasis in Colombia for first-year and maintenance periods

ABSTRACT Introduction Psoriasis is a chronic systemic inflammatory disease manifesting as erythematous and desquamative dermatoses. Objectives This study estimated the cost per responder (CPR) for the treatment of moderate-to-severe plaque psoriasis with biologic therapies approved by the Colombian regulatory agency. Methods This secondary study used a modeling based CPR estimation to evaluate psoriasis therapies in Colombia. We calculated CPR of achieving Psoriasis Area and Severity Index (PASI) scores of 75, 90, and 100 for biological treatments based on the number needed to treat (NNT), reported in previously published network meta-analyses. We calculated CPR for the first year and for the maintenance period. We ranked alternatives using the estimated CPR from each literature source using the Borda count method. Results Adalimumab, infliximab and etanercept were the least expensive alternatives. Ixekizumab, guselkumab and secukinumab were the treatments with the lowest NNT for PASI 75, 90, and 100. For both first year and maintenance periods, adalimumab, infliximab, guselkumab and ixekizumab had the lowest CPR. Sensitivity analyzes showed consistent results. Conclusions The application of CPR analysis of biologics to treat plaque psoriasis demonstrated that adalimumab, infliximab, guselkumab, and ixekizumab had the lowest CPR in the first year of treatment and during the maintenance period.

Predicting the potential distribution of wheatear birds using stacked generalization-based ensembles

Habitat suitability models, usually referred to as species distribution models (SDMs), are widely applied in ecology for many purposes, including species conservation, habitat discovery, and gain evolutionary insights by estimating the distribution of species. Machine learning algorithms as well as statistical models have been recently used to predict the distribution of species. However, they seemed to have some limitations due to the data and the models used. Therefore, this study proposes a novel approach for assessing habitat suitability based on ensemble learning techniques. Three heterogeneous ensembles were built using the stacked generalization method to model the distribution of four wheatear species (Oenanthe deserti, Oenanthe leucopyga, Oenanthe leucura, and Oenanthe oenanthe) located in Morocco. Initially, a set of base-learners were constructed by virtue of training for each specie's dataset six machine learning algorithms (Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), K-nearest neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GB), and Random Forest (RF)). Then, the predictions of these base learners were fed as training data to train three meta-learners (Logistic Regression (LR), SVC, and MLP). To evaluate and assess the performance of the proposed approaches, we used: (1) six performance criteria (accuracy, recall, precision, F1-score, AUC, and TSS), (2) Borda Count (BC) ranking method based on multiple criteria to rank the best-performing models, and (3) Scott Knott (SK) test to statistically compare the performance of the presented models. The results based on the six-evaluation metrics showed that stacked ensembles outperformed their singles in all species datasets, and the stacked model with SVC as a meta-learner outperformed the other two ensembles. The results showed the potential of using ensemble learning techniques to model species distribution and recommend the use of the stacked generalization technique as a combination strategy since it gave better results compared to single models in four wheatear species datasets. Moreover, to assess the impact of future climate changes on the distribution of the four wheatear species, the best-performing distribution model was selected and projected into the current and future climatic conditions. The distributions of the Moroccan wheatear birds were found to be slightly affected by future climate changes.

A Quantitative Analysis of Existing and Expected Status of the Ethics and Morality in High School Curriculum in Punjab

The study aimed to analyze the current and expected level of the ethics and morality in high school education. A Quantitative Survey Research Design was applied by the study. The required data needed to be specific and relevant, therefore, the stratified random sampling technique was used to collect data from a population of all stakeholders of education in the Punjab: teachers, parents, students, administrators, policy makers and curriculum developers. The data was collected from 500 participants who used Thurstone scale to show their priorities by scaling the 77 items of the 7 questions regarding their importance and these collected priorities were further analyzed by using Borda Count Method “to explore the degree of intensity of the prioritized items”. According to the result of the study, the current status of the concepts of ethics and morality taught at the secondary level is not up to the required standards and lack planning. Therefore, to develop moral attitude, the religious and spiritual values need to be inculcated through moral stories and disciplinary rules using lecture, group discussion and story-telling methods to ensure overall personality development with social adjustability. It is recommended by the researchers to revise the existing curriculum to make it practical and operational for the optimum effectiveness.

Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique

In order to improve classification accuracy and lower future computation and data collecting costs, feature selection is the process of choosing the most crucial features from a group of attributes and removing the less crucial or redundant ones. To narrow down the features that need to be analyzed, a variety of feature selection procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), and MI are six alternative feature selection methods used in this study. The provided dataset is aggregated using four rank aggregation strategies: "rank aggregation," "Borda Count (BC) methodology," "score and rank combination," and "unified feature scoring" based on the outcomes of the six feature selection method (UFS). These four procedures by themselves were unable to generate a clear selection rank for the characteristic. To produce different ranks of traits, this ensemble of aggregating ranks is carried out. For this, the bagging method of majority voting was applied.

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

PurposeHistopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.Design/methodology/approachThree pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.FindingsThis study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.Originality/valueThe best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

Borda count in a forward agenda

Borda count in a forward agenda

Time-varying multi-objective smart home appliances scheduling using fuzzy adaptive dynamic SPEA2 algorithm

The smart city is an idea of upcoming revolution in cities, which includes smart home, smart mobility, smart living, and smart environment that can be operated using a smartphone connected to the internet. By adequately scheduling appliances, residential consumers can reduce their electricity expenses while increasing their comfort. Electricity cost and user comfort being conflicting in nature, can be formulated as a dynamic multi-objective optimization problem with varying user priority to use different home appliances at different times. Further to solve this problem, a Fuzzy Adaptive Dynamic SPEA2 with Borda Ranking Method (FDSPEA2-BR) is proposed based on the dynamic modification of popular SPEA2 algorithm. The algorithm includes an improved Borda count method along with Mamdani fuzzy rules to select the best solutions. The changes in crossover and mutation rate in the SPEA2 algorithm is now being controlled with fuzzy rules. Proposed FDSPEA2-BR is validated on fourteen benchmark dynamic multi-objective functions taken from the FDA, JY, dMOP, and DF test suites, results have validated the optimization model’s efficacy. Simulation study is performed on the scheduling of 11 smart home appliances varying over 10 time slots. The results in the form of time varying Pareto Fronts is demonstrated and the schedules corresponding to 5 diversified points on each Pareto Front are reported. The end user can make use of the optimized schedules that were thus obtained to modify his or her patterns of demand and energy use.

Global Epidemic of Ebola Virus Disease and the Importation Risk into China: An Assessment Based on the Risk Matrix Method

ObjectiveTo analyze the global epidemic status of the Ebola virus disease (EVD) and assess the importation risk into China. MethodsData from World Health Organization reports were used. We described the global epidemic status of EVD from 1976–2021, and assessed and ranked the importation risk of EVD from the diseaseoutbreaking countries into China using the risk matrix and Borda count methods, respectively. bo ResultsFrom 1976–2021, EVD mainly occurred in western and central Africa, with the highest cumulative number of cases (14,124 cases) in Sierra Leone, and the highest cumulative fatality rate (85%) in the Congo. Outbreaks of EVD have occurred in the Democratic Republic of the Congo and Guinea since 2018. The importation risk into China varies across countries with outbreaks of disease. The Democratic Republic of the Congo had an extremely high risk (23 Borda points), followed by Guinea and Liberia. Countries with a moderate importation risk were Nigeria, Uganda, Congo, Sierra Leone, Mali, and Gabon, while countries with a low importation risk included Sudan, Senegal, and C te d’Ivoire. ConclusionChina is under the risk of EVD importation with the globalization and severe epidemic status of EVD. Key attention need to be paid to the Democratic Republic of the Congo, Guinea, and Liberia. Therefore, it is necessary to prevent and prepare in advance for importation risk in China.

Improved PBFT Algorithm Based on Comprehensive Evaluation Model

Blockchain technology is well known due to the advent of Bitcoin. With the development of recent years, blockchain technology has been widely used in medicine, digital currency, energy, etc. The practical Byzantine fault-tolerant (PBFT) algorithm is a consensus algorithm widely used in consortium blockchains. Aiming to address the problems of the PBFT algorithm, low consensus efficiency due to high communication complexity, and malicious behavior of the primary node leading to consensus failure, an improved PBFT algorithm based on a comprehensive evaluation model (TB-PBFT) is proposed. First, nodes are divided into several groups based on the multi-formation control strategy of an unmanned aerial vehicle (UAV) cluster, which significantly reduces the communication complexity. Second, a comprehensive evaluation model combining the entropy method, TOPSIS method, and Borda count is proposed, which uses the behavior of nodes as an evaluation index, and the comprehensive score of nodes is obtained according to the preferences of other nodes. Finally, the highest ranking node is selected as the primary node through the comprehensive evaluation model to ensure the security and stability of the blockchain network. We analyze TB-PBFT algorithms and compare them with other Byzantine fault tolerance algorithms. Theoretical analysis and simulation results show that the TB-PBFT algorithm can improve node scalability and fault tolerance and reduce communication complexity and view switching probability. We also prove that the comprehensive evaluation model can improve the consensus success rate of the algorithm, and the feasibility and effectiveness of the improved consensus algorithm are verified. Hence, it can be applied to the consortium blockchain system effectively and efficiently.

Multi-objective decision-making based on theories of cooperative game and social choice to incentivize implementation of low-impact development practices

Urbanization growth leads to shifting hydrological factors which in turn represent a challenge for urban stormwater management. In this situation, policy strategies can be employed by governments to incentivize stakeholders to use green infrastructure for effective runoff management. This study presents a novel framework evaluating the application of Low-Impact Development (LID) practices for runoff quality and quantity management from a socio-economic point of view. The proposed framework investigates the potential of cooperative game theory to find a fair allocation based on the concept of justice. In this regard, first, the Non-dominated Sorting Genetic Algorithm (NSGA-II) was coupled with the Storm Water Management Model (SWMM) to find optimal runoff management scenarios considering minimization of runoff volume, peak flow, Total Suspended Solid (TSS) load, and costs of LIDs implementation. In the next step, in order to conflict resolution, stakeholders' preferences and interactions were incorporated into the cooperative game theory to determine management scenarios' worth of coalition. In the third step, benefits are allocated among stakeholders by defining three different sharing rules. Then, to select the most capable runoff management scenario, social choice methods of Condorcet choice, Borda scoring, and median voting rule support cooperative game decisions. The proposed methodology is explored in District 10 of Tehran municipality in Iran. According to the findings, solutions with stricter plans for runoff management correspond to more worth of coalition. In addition, the higher worth of coalition, the higher the reduction of peak flow, TSS load, and the total runoff volume. The results also reveal that the Borda scoring procedure tends to select scenarios with less reduction in TSS load, runoff volume, and peak flow in comparison with the others, and as expected, the minimum value for the worth of coalition. Additionally, the cooperative game targets a management scenario with the highest worth of coalition and the maximum value for the reduction in runoff volume, peak flow, and TSS removal. Our framework warrants runoff volume, TSS load, and peak flow can be reduced by 32.8, 31.9, and 32.7 percent, respectively. This framework, in addition to incentivize the stakeholders for implementing runoff management strategies, can effectively consider their conflict of interests in the decision-making process.

An investigation to offer conclusive recommendations on suitable benefit/cost criteria-based normalization methods for TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.

A Comparison of the Entrepreneurial Performance of Asian-Oceanian Countries via the Multi-Criteria Decision-Making Techniques of Critic, Aras, Waspas, Mairca and Borda Count Methods

A Comparison of the Entrepreneurial Performance of Asian-Oceanian Countries via the Multi-Criteria Decision-Making Techniques of Critic, Aras, Waspas, Mairca and Borda Count Methods

The Fallacy of Borda Count Method - Why it is Useless with Group Intelligence and Shouldn’t be Used with Big Data including Banking Customer Services

Borda Count Method is an important theory in the field of voting theory. The basic idea and implementation methodology behind the approach is simple and straight forward. Borda Count Method has been used in sports award evaluations and many other scenarios, and therefore is an important aspect of our society. An often ignored ground truth is that online cultural rating platforms such as Douban.com and Goodreads.com often adopt integer rating values for large scale public audience, and therefore leading to Poisson/Pareto behavior. In this paper, we rely on the theory developed by Wang from 2021 to 2023 to demonstrate that online cultural rating platform rating data often evolve into Poisson/Pareto behavior, and individualistic voting preferences are predictable without any data input, so Borda Count Method (or, Range Voting Method) has intrinsic fallacy and should not be used as a voting theory method.

RALO: Accessible Learning Objects Assessment Ecosystem Based on Metadata Analysis, Inter-Rater Agreement, and Borda Voting Schemes

The increasing number of people are living with disability in the World and their access to formal education is considered a challenge for the development of the online education and educational resources. This problem is considered one of the 17 sustainable development goals that are focused on inclusive and equitable quality education. Nevertheless, the existing proposals for mainstream accessibility in virtual education are still complex to apply. However, the models, standards, and good practices to contribute to the virtual educational process and the design of learning for all are identified. For these reasons, in this paper, we describe an accessibility evaluation proposal based on 4 interaction domains: user analysis and interaction, intelligent systems, knowledge databases, and evaluation. In the same way, we describe a set of tools that constitute a Repository of Accessible Learning Objects (RALO) from the perspective of accessibility and adaptability metadata. In this line, the knowledge database follows the regulation and educational models focused on the students with disabilities needs and preferences from the conception of universal design. The validation of the proposal is based on the interaction study and analysis of regular and disabled students and teachers who developed the Learning Objects (LO). To determine whether there was consensus among the teacher’s scores, we used Kendall’s Coefficient of Concordance W.