Dempster-Shafer Research Articles

Enhancing intelligent marketing systems: a multi-layer hypernetwork approach integrating evidence theory for influential node identification

PurposeThis study proposes an intelligent marketing system model based on a combination of multi-layer hypernetworks and evidence theory, aiming to address the shortcomings of traditional marketing models in accurately identifying key nodes. We propose a new method to improve the accuracy and response speed of intelligent marketing systems by combining evidence theory with multi-layer hypernetworks. We conducted an experiment using a certain car brand (SUV) as an example, which has a wide customer base in both domestic and international markets and has branches in multiple countries. By analyzing its sales data and user behavior, we evaluated the potential reduction in advertising costs and improvement in user satisfaction that may result from adopting this model.Design/methodology/approachThe proposed model begins with the development of a user interest model, which is subsequently converted into a user label model based on user behavior and a rating matrix. A multi-layer aggregation hypernetwork is then constructed to define the network’s topology. An identification framework is established using evidence theory, and the Dempster–Shafer (D-S) evidence combination method is applied to integrate local, positional and global network indicators. Simulation experiments are conducted to evaluate the model’s performance.FindingsThis study proposes an intelligent marketing system model that integrates multi-layer hypernetworks with Dempster–Shafer evidence theory to address the limitations of traditional marketing models in identifying influential nodes. The proposed model is tested in the automotive industry, specifically using sales and user behavior data from a well-known SUV brand operating globally. This industry provides a complex and competitive environment ideal for validating the model’s ability to improve marketing precision. The results demonstrate that the model significantly enhances the accuracy of key node identification, reduces advertising costs by 10–15% and improves customer satisfaction scores to over 90%. Furthermore, preliminary experiments in the retail and e-commerce sectors highlight the model’s adaptability and potential for broader application. By combining local, positional and global indicators, the model effectively optimizes marketing strategies, providing a novel framework for intelligent decision-making in diverse industries. This study selected a well-known SUV car brand as the experimental subject. This brand mainly sells SUV models and has a wide customer base worldwide. Its products are known for their high performance and reliability. The brand has millions of customers, and its main markets include North America, Europe and Asia. It has branches in multiple countries and has significant international influence. According to publicly available data, the brand’s annual revenue reaches billions of dollars.Originality/valueThe main contribution of the research is the proposal of a novel intelligent marketing optimization framework based on multi-layer hypernetworks and evidence theory, which can effectively solve the problems of data silos and information asymmetry faced in traditional marketing systems.

A new approach to construct basic probability assignment and its applications in data classification

In the present work, we have proposed a classification algorithm that uses Dempster Shafer (D-S) evidence theory since it has emerged as an effective tool in handling data classification problems. As basic probability assignment (BPA) is a pre-requisite for applying D-S theory, how to generate it is a hot issue. This paper proposes a novel method for generating basic probability assignments (BPAs) from training data. Dempster Shafer’s (D-S) rule of Combination is utilized for the unification of these BPAs and finally, classify each data item using these unified BPAs. Testing is carried out using some popular benchmark data sets consisting of three classes. Evaluated results show that the classification accuracy is comparatively high.

Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions

Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience.

Bayesian network for predicting mandibular third molar extraction difficulty

BackgroundThis study aimed to establish a model for predicting the difficulty of mandibular third molar extraction based on a Bayesian network to meet following requirements: (1) analyse the interaction of the primary risk factors; (2) output quantitative difficulty-evaluation results based on the patient’s personal situation; and (3) identify key surgical points and propose surgical protocols to decrease complications.MethodsRelevant articles were searched to identify risk factors. Clinical knowledge and experience were used to analyse the risk factors to establish the Bayesian network. First, the qualitative mechanism knowledge, including the effect of risk factors on the extraction difficulty and the causal relationships between risk factors, was analysed to establish the framework of the Bayesian network. Then, the quantitative knowledge, including the occurrence probability of the parent nodes and the conditional probability table of the nodes with causal relationships, was given by the surgeon experience and calculated using the Dempster-Shafer evidence theory. According to the framework and likelihoods and relationships of risk factors, the Bayesian network model was established.ResultsThis Bayesian network model analysed the weight by sensitivity of each risk factor and expressed the interaction relationship among risk factors as well as the effect of risk factors on extraction difficulty quantitatively. This Bayesian network model showed quantitative analysis results for extraction difficulty and key risk factors. The Bayesian network model revealed that the relationship to the inferior alveolar nerve, surgeon experience and patient anxiety were the most important risk factors for extraction difficulty. By integrating these patient-specific risk factors across the entire surgical process, this model could be used during preoperative planning to identify high-risk cases and to optimize resource allocation; during intraoperative management to tailor surgical techniques; and during postoperative follow-up to establish targeted follow-up protocols for high-risk patients. Moreover, this Bayesian network model can flexibly improve inclusion factors and conditional probabilities with the development of relevant research and expert opinions, as well as change states and probabilities of relevant nodes based on actual clinical conditions.ConclusionsA model for predicting the difficulty of mandibular third molar extraction was established based on a Bayesian network.

Applying the Dempster–Shafer Fusion Theory to Combine Independent Land-Use Maps: A Case Study on the Mapping of Oil Palm Plantations in Sumatra, Indonesia

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author’s name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis.

A deep neural network-based multi-layer classifier ensembles for intrusion detection in fog-based Internet of Things environments

Intrusion Detection Systems (IDS) are regarded as an efficient security mechanism in the Internet of Things (IoT). IDSs in the IoT face critical challenges, such as a vast amount of data with many features, limited resources, detection of unknown attacks, and many false alarms. To overcome these issues, this paper proposes a novel framework, namely a Multi-Layer Multi-Classification System (MLMCS). The proposed framework consists of four steps: In the first step, the data collected from information sources are pre-processed. The second step divides the feature space into several subspaces to reduce the model's complexity. The third step proposes a multilayer base classifier called a Multi-Layer Classifier System (MLCS) for each feature subspace. Each MLCS is layered to transform a multi-class classification into several binary classifications so that a specific category of attacks has been detected in each layer based on the Group Method of Data Handling (GMDH) neural network. In the fourth step, the results obtained from MLCSs based on the Dempster-Shafer theory are combined to reduce the problems related to overfitting. The simulation results were gained using three datasets, i.e., NSL-KDD, UNSW-NB15, and TON-IoT. They indicated that the proposed method, compared with other methods, improved F1-score by 5.64%, 0.65%, and 0.11%, respectively. Furthermore, the false alarm rate was reduced by 2.66%, 2.52%, and 0.02%. Moreover, the impact of four well-known adversarial attacks on the dataset was investigated. It was observed that the MLMCS method, with an average 5.21% reduction, could perform well against these attacks.

Quality-aware fuzzy min-max neural networks for dynamic brain network analysis and its application to schizophrenia identification

Dynamic functional connections (dFCs) refer to the observed phenomenon that functional connectivity changes over a short time, which has become a pioneering method in the diagnosis of brain diseases. However, current dynamic brain network analysis methods are insufficient to address the fuzzy information of brain networks caused by the quality issues of rs-fMRI data, and the uncertainty arising from inconsistent data quality across different windows due to missing values and noise in individual windows. These results in unreliable integration of multiple windows. To this end, this paper proposes a dynamic brain network analysis method based on quality-aware fuzzy min–max neural networks (QFMMNet). The individual window of dFCs is treated as a view, and we define three convolution filters to extract features from the brain network under the multi-view learning framework, thereby obtaining multi-view evidence for dFCs. We design the multi-view fuzzy min–max neural networks (MVFMM) based on fuzzy sets to deal with the fuzzy information of the brain network, which takes evidence as input patterns to generate hyperboxes and serves as the classification layer of each view. A quality-aware ensemble module is introduced to deal with uncertainty, which employs Dempster-Shafer Theory (D-S theory) to directly model the uncertainty and evaluate the dynamic quality-aware weighting of each view. At the decision level of model, a weighted fusion strategy is employed for multi-view fusion based on the dynamic quality-aware weighting, thereby the quality of each view is fully considered to improve the trustworthiness of the decision. We verified the effectiveness of QFMMNet through comparison with advanced algorithms on three real schizophrenia datasets, and the results show that QFMMNet significantly improved the classification performance of brain disease diagnosis tasks. The accuracy on the Huaxi, COBRE, and Xiangya datasets reached 87.14%, 86.67%, and 85.27%, respectively.

Comprehensive Evaluation and Dynamic Evolution Characteristics of Tourism Ecological Security Using the DPSIR-DS Model in the Yangtze River Economic Belt, China

The Yangtze River Economic Belt (YREB) is positioned as the main battlefield of ecological priority and green development in China. It is of great significance to dynamically grasp the situation of tourism ecological security in YREB for promoting the green and high-quality development of regional tourism. Aiming at the disadvantage of either-or in evaluation research, this study established the index system based on the inter-period DPSIR framework, designed an integrated assessment model combining the entropy method, the membership function, and the D-S evidence theory model to assess the tourism ecological security level from the perspective of probability value, then revealed its spatiotemporal dynamics characteristics. Results showed that: (1) the regional security level is in a good trend of weakening fluctuations and stable improvement, in which the upstream and downstream improvement is more significant, but still at a lower level in general. (2) The security levels of the 11 areas included were all located at Risk level to Relative Security level with significant differences, including Zhejiang, Anhui, Hubei, and Chongqing were the key ecological nodes for tourism ecological security improvement. (3) The transitions among security levels were frequent, while the higher levels were relatively unstable, implying that the tourism ecology has not improved in essence. Finally, strategic suggestions for improving tourism ecological security are proposed in terms of building joint prevention and control mechanisms, clarifying risk factors, and innovating protection paths.

AGE DIFFERENCES IN THE BELIEF IN A JUST WORLD AND SOCIETY-LEVEL MODERATORS

Abstract Prior studies have associated belief in a just world with both positive and negative individual and societal consequences. Despite this, the determinants influencing individual differences in justice belief are underexplored. Understanding the developmental trajectory of justice belief can shed light on its influencing factors and potential roles at different stages of life. However, comprehensive research on this issue is scarce. To fill in this gap, we investigated age difference in justice belief with data from World Value Survey 6 (2010-2014) encompassing 83,683 individuals across 59 societies. Incorporating the individualism scores, society-level justice belief, human development index, and income Gini coefficient, we further examined the moderation effects of these societal factors by conducting multilevel analyses. The results showed that both the quadratic (γ40 =.054, p <.001) and linear effect (γ50 =.050, p =.022) of age on justice belief were significantly positive, suggesting a general increasing trend of justice belief with age. Individualism did not moderate the quadratic effect of age (γ41 = -.014, p =.368) but did moderate the linear effect (γ51 =.078, p =.010). Society-level justice belief moderated the quadratic effect of age (γ41 =.431, p <.001), but not the linear effect (γ51 = -.021, p =.245). This study illustrated the developmental trajectory of justice belief and revealed the moderation effects of some societal factors. It provides more insights into the developmental foundation and functions of justice belief. Future studies could further examine the mechanism.

Justices of Peace in Russian Empire as an Alternative to Formal Judicial Proceedings: 1775–1866

This article examines the establishment and functioning of the Justices of the Peace in the Russian Empire, initiated by Catherine II in 1775 and fully abolished in 1866. Utilizing a structural analysis method, the study investigates the judicial processes within these courts from 1775 to 1866. A formal-logical approach is employed to analyze the existing legislation of the Russian Empire concerning the jurisdiction of the Justices of the Peace. The research draws on administrative documents from the State Archive of Voronezh Oblast and legislative acts from the late 18th to mid-19th centuries. The novelty of this study lies in its perspective of viewing the Justices of the Peace as an alternative to formal judicial proceedings, which were primarily based on inquisitorial principles as an ideal type of legal process, alongside theories of formal evidence, where judicial decisions were made without regard for the internal convictions of the judge. The author concludes that the judicial process of the Justices of the Peace was primarily aimed at protecting private interests, with the nature of this alternative to formal proceedings varying depending on whether the case was criminal or civil.

Evidential Belief Function (EBF) Model for Landslide Susceptibility Analysis in Idukki District, Kerala, India

A Geo-statistical Dempster-Shafer Theory Evidential Belief Function (DST-EBF) model is selected for Landslide susceptibility evaluation. The aim of the study is to delineate zones prone to landslides within the Idukki district, Kerala using the EBF model and Geographical Information System (GIS) Technique. The objective is to integrate diverse datasets to assess and predict the landslide-prone areas, providing valuable insights for risk management and mitigation startgies. Topographical, Anthropogenic, and Geological factors are considered Landslide Conditioning Factors (LCFs), and Landslide Inventory data are used to establish and validate the Suscebtlity zones. Landslide inventory data is randomly divided into Training(70%) and Testing (30%) data. The resultant Landslide Susceptibility map is categorized into five zones using natural breaking classification method: Very low (20.96%), Low (29.71%), Medium (24.50%), High (18.63%), and Very high (6.20%), respectively, within the study area. The success rate and prediction rate were calculated using the AUC_ROC method and the EBF Model achieved the highest precision with a success rate of 0.935, and a prediction rate of 0.943 in the current study.

An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information

In the context of global warming, renewable energy sources, particularly wind and solar power, have garnered increasing attention in recent decades. Accurate forecasting of the energy output in microgrids (MGs) is essential for optimizing energy management, reducing maintenance costs, and prolonging the lifespan of energy storage systems. This study proposes an innovative approach to solar irradiance forecasting based on the theory of belief functions, introducing a novel and flexible evidential method for short-to-medium-term predictions. The proposed machine learning model is designed to effectively handle missing data and make optimal use of available information. By integrating multiple predictive models, each focusing on different meteorological factors, the approach enhances forecasting accuracy. The Yager combination method and pignistic transformation are utilized to aggregate the individual models. Applied to a publicly available dataset, the method achieved promising results, with an average root mean square error (RMS) of 27.83 W/m2 calculated from eight distinct forecast days. This performance surpasses the best reported results of 30.21 W/m2 from recent comparable studies for one-day-ahead solar irradiance forecasting. Comparisons with deep learning-based methods, such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs), demonstrate that the proposed approach is competitive with state-of-the-art techniques, delivering reliable predictions with significantly less training data. The full potential and limitations of the proposed approach are also discussed.

Image fusion of ultrasonic and thermography for delamination detection of CFRP based on data post-processing methods

ABSTRACT Delamination is crucial damage in carbon fibre reinforced polymer (CFRP) composites. Various non-destructive testing methods could detect the delamination with different merits. Ultrasonic can penetrate CFRP but has the dead zone effect, whereas thermography can reveal shallow defect but is insensitive to deep delamination. Therefore, in this work, the results of ultrasonic C-scan and locked-in thermography were fused to inspect the delamination. The ultrasonic C-scan images were processed by 6 dB drop criteria to quantify defect area. Principal component thermography, thermographic signal reconstruction and phase extraction were implemented on the thermographic images. C-scan images and thermographic images were fused using the proposed fusion algorithm that combines the evidence theory and minimum values. The performances of fused images were evaluated by the index of signal-to-noise ratio. The results show that the C-scan image fused with the thermographic phase image can significantly improve the signal-to-noise ratio of shallow defect detection and reduce the error with the actual defect area.

Is the existence of a relict primate in the Far North possible?

The author sets out to generalize the existing information about a humanoid creature in the Russian Far North by analyzing the reports of local residents, travelers, and researchers, as well as by studying local legends and folklore tales. The data is evaluated from the perspectives of folklore studies and the theory of testimonial evidence. The reliability of this evidence, which resonates well with the traces of prehistoric human activity found in Yakutia, cannot be fully ruled out. Given the low anthropogenic load and the scarcity of knowledge about these places, the possibility of the existence of animals, which are unknown to science, cannot be refuted. The author evaluates the theoretical possibility of the existence of a highly developed primate in northern and other areas of the planet, which cannot be accessed by conventional methods of zoology.

A Novel Framework for Belief and Plausibility Measures in Intuitionistic Fuzzy Sets: Belief and Plausibility Distance, Similarity, and TOPSIS for Multicriteria Decision Making

Dempster–Shafer Theory (DST) relies significantly on belief and plausibility measures to handle ambiguity and uncertainty; however, DST has been extended to fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) with only a few extensions focusing on belief and plausibility intuitionistic fuzzy distance (BP-distance) and similarity (BP-similarity) until now. In this work, we propose a novel framework for the belief and plausibility of intuitionistic fuzzy sets (BP-IFSs) and their BP-distance and BP-similarity measures. We modified steps 4 and 5 of the classical TOPSIS method, utilizing both distance and similarity measures to rank the alternatives that satisfy all necessary axioms of distance and similarity. We present numerical examples involving pattern recognition, linguistic variables, and clustering to illustrate the efficiency of these measures, and we develop belief and plausibility TOPSIS (BP-TOPSIS) using the proposed criteria and apply it to complex multicriteria decision-making (MCDM) challenges. The results demonstrate the practicality and effectiveness of our approach.

Basic Belief Assignment Determination Based on Radial Basis Function Network

In Dempster-Shafer evidence theory (DST), the determination of basic belief assignment (BBA) is an important yet challenging issue. The rational mass determination of compound focal elements is crucial for fully taking advantage of DST, i.e., the ability to represent the ambiguity. In this paper, for the compound focal elements, we select and construct the \enquote{compound-class samples} with ambiguous class membership. Then, we use these samples to construct an end-to-end model called Evidential Radial Basis Function Network (E-RBFN), with the input as the sample and the output as the corresponding BBA. The E-RBFN can directly determine the mass values for all focal elements (including the singleton and compound ones).Experimental results of evidence decision-based pattern classification on multiple UCI and image datasets show that our proposed method is rational and effective.

KEPASTIAN HUKUM TERHADAP KEKUATAN ALAT BUKTI AKTA DI BAWAH TANGAN

This research aims to: 1) analyze and determine the legal certainty strength of private deeds as evidence in resolving civil disputes. 2) understand the legal consequences of using private deeds as evidence based on Article 1874 of the Civil Code. The research employs the normative method, which reviews secondary document studies such as laws and regulations and court decisions involving private deeds as evidence. It is based on the theory of evidence, legal certainty, and expert opinions, which are then analyzed using qualitative methods.The results of the legal analysis on the strength of private deeds as evidence in the District Court of Gorontalo City are as follows: 1) private deeds are considered legitimate evidence, but their evidentiary strength in judicial processes requires the involvement of several parties that must be included in the private deed, namely: a) Both parties involved, b) Authorized officials (Land Deed Officials, Notaries, and Local Government), and c) Witnesses. 2) According to Article 1874 of the Civil Code, all forms of private deeds carry legal consequences in judicial processes. Therefore, to mitigate negative outcomes from using private deeds as evidence, it is necessary to apply the appropriate procedures during their creation to ensure legal certainty in resolving civil cases

Conditioning w.r.t. random sets. Part 2: combination rules

Part 2 of this article is devoted to the analysis of a new class of combination rules on belief functions generated by conditioning w.r.t. random sets and their linear convex combinations. Each such rule obeys two requirements: the consensus requirement with the conjunction rule means that this rule coincides with Dempster's rule on non-conflicting sources of information, and the consensus requirement with a mixture rule means that the result must be a specialization of their mixture combination. We show that this new class of combination rules is closed under convex combinations of such rules and their compositions. This allows us to implement the conflict management using the introduced functionals for measuring the specialization relation on belief functions. We also introduce similar rules for idempotent combination rules on credal sets obeying the similar consensus requirements and study the results obtained using various examples.

Random Permutation Set Reasoning.

In artificial intelligence, it is crucial for pattern recognition systems to process data with uncertain information, necessitating uncertainty reasoning approaches such as evidence theory. As an orderable extension of evidence theory, random permutation set (RPS) theory has received increasing attention. However, RPS theory lacks a suitable generation method for the element order of permutation mass function (PMF) and an efficient determination method for the fusion order of permutation orthogonal sum (POS). To solve these two issues, this paper proposes a reasoning model for RPS theory, called random permutation set reasoning (RPSR). RPSR consists of three techniques, including RPS generation method (RPSGM), RPSR rule of combination, and ordered probability transformation (OPT). Specifically, RPSGM can construct RPS based on Gaussian discriminant model and weight analysis; RPSR rule incorporates POS with reliability vector, which can combine RPS sources with reliability in fusion order; OPT is used to convert RPS into a probability distribution for the final decision. Besides, numerical examples are provided to illustrate the proposed RPSR. Moreover, the proposed RPSR is applied to classification problems. An RPSR-based classification algorithm (RPSRCA) and its hyperparameter tuning method are presented. The results demonstrate the efficiency and stability of RPSRCA compared to existing classifiers.

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Multiple types of omics data contain a wealth of biomedical information which reflect different aspects of clinical samples. Multi-omics integrated analysis is more likely to lead to more accurate clinical decisions. Existing cancer diagnostic methods based on multi-omics data integration mainly focus on the classification accuracy of the model, while neglecting the interpretability of the internal mechanism and the reliability of the results, which are crucial in specific domains such as precision medicine and the life sciences. To overcome this limitation, we propose a trustworthy multi-omics dynamic learning framework (TMODINET) for cancer diagnostic. The framework employs multi-omics adaptive dynamic learning to process each sample to provide patient-centered personality diagnosis by using self-attentional learning of features and modalities. To characterize the correlation between samples well, we introduce a graph dynamic learning method which can adaptively adjust the graph structure according to the specific classification results for specific graph convolutional networks (GCN) learning. Moreover, we utilize an uncertainty mechanism by employing Dirichlet distribution and Dempster-Shafer theory to obtain uncertainty and integrate multi-omics data at the decision level, ensuring trustworthy for cancer diagnosis. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed algorithm are clearly validated. Our model has great potential for clinical diagnosis.