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Journal articles on the topic 'Dempster-Shafer theory (DST)'

Author: Grafiati

Published: 2 August 2022

Last updated: 31 July 2025

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1

Belmessous, Khadidja, Faouzi Sebbak, M’hamed Mataoui, Mustapha Reda Senouci, and Walid Cherifi. "Dempster-Shafer Theory in Recommender Systems: A Survey." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 05 (2024): 747–80. http://dx.doi.org/10.1142/s0218488524500181.

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Due to the limitations associated with the use of a single type of data during the recommendation process, recent research has focused on developing new fusion-based recommenders that make use of multiple heterogeneous sources of information to provide more accurate suggestions. However, the realistic and flexible methods available to users for expressing their preferences for products and services inherently generate uncertain, imperfect, and ambiguous data that feed recommenders and thus affect their accuracy. As a result, Recommender Systems (RS) make significant use of soft mathematical to

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Dutta, Palash. "Dempster Shafer Structure-Fuzzy Number Based Uncertainty Modeling in Human Health Risk Assessment." International Journal of Fuzzy System Applications 5, no. 2 (2016): 96–117. http://dx.doi.org/10.4018/ijfsa.2016040107.

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In risk assessment, generally model parameters are affected by uncertainty arises due to vagueness, imprecision, lack of data, small sample sizes etc. Fuzzy set theory and Dempster-Shafer theory (In short DST) of evidence should be explored to handle this type of uncertainty. Representation of parameters of risk assessment models may be Dempster-Shafer structure (in short DSS) and fuzzy numbers. To deal with such situations, it is important to device new techniques. This paper presents two algorithms to combine Dempster-Shafer structure with generalized/normal fuzzy focal elements, generalized

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Siemiątkowska, Barbara, and Bogdan Harasymowicz-Boggio. "Place Classification using Dempster-Shafer Theory." Foundations of Computing and Decision Sciences 42, no. 3 (2017): 257–73. http://dx.doi.org/10.1515/fcds-2017-0013.

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AbstractThe paper presents a novel place labeling method. It is assumed that an indoor mobile robot equipped with a camera or RGB-D sensor ambulates an indoor environment. The places visited by the robot are classified based on objects which have been recognized. Each object (or set of objects) votes for a set of room classes. Data aggregation is performed using Dempster-Shafer theory (DST), which can be regarded as a generalization of the Bayesian theory. The possibility of taking into account the uncertainty of data is the main advantage of the described method. The classic Dempster’s rule o

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Wahyuni, Ias Sri, and Rachid Sabre. "Local Distance and Dempster-Dhafer for Multi-Focus Image Fusion." Signal & Image Processing : An International Journal 13, no. 1 (2022): 29–43. http://dx.doi.org/10.5121/sipij.2022.13103.

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This work proposes a new method of fusion image using Dempster-Shafer theory and local variability (DST-LV). This method takes into account the behaviour of each pixel with its neighbours. It consists in calculating the quadratic distance between the value of the pixel I (x, y) of each point and the value of all the neighbouring pixels. Local variability is used to determine the mass function defined in DempsterShafer theory. The two classes of Dempster-Shafer theory studied are : the fuzzy part and the focused part. The results of the proposed method are significantly better when comparing th

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Kurniawan, Rahmad, Nazri Mohd Zakree Ahmad, Abdullah Siti Norul Huda Sheikh, Zulaiha Ali Othman, and Salwani Abdullah. "BAYESIAN NETWORK AND DEMPSTER-SHAFER THEORY FOR EARLY DIAGNOSIS OF EYE DISEASES." COMPUSOFT: An International Journal of Advanced Computer Technology 09, no. 04 (2020): 3642–51. https://doi.org/10.5281/zenodo.14912173.

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An accurate self-diagnosis expert system would prevent the progression of chronic eye disease. However, developing an expert system for medical diagnose requires a robust reasoning capability. In the knowledge acquisition phase, a knowledge engineer faces several issues. For example, an eye disease may contain several similar symptoms to another eye disease. Even worse, a patient may input a set of symptoms that can be attributable to several diseases, and these symptoms may not be readily quantifiable. Dempster-Shafer Theory (DST) and Bayesian Network (BN) are two commonly used techniques for

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Skoruchi, Amirhossein, and Emran Mohammadi. "Uncertain portfolio optimization based on Dempster-Shafer theory." Management Science Letters 12, no. 3 (2022): 207–14. http://dx.doi.org/10.5267/j.msl.2022.1.001.

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Nowadays, the selection and management of the optimal portfolio are the most primary fields of financial decision-making. Thereby, selecting a portfolio capable of providing the highest efficiency and, at the same time, the lowest investment risk has been turned into one of the most critical concerns among financial activists. However, in this selection, the two factors above are not the only determining ones. Various factors are affecting financial markets' behavior under different possible scenarios, which should be identified. In this paper, we examine the high sensitivity of the Iranian ca

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Sarabi-Jamab, Atiye, and Babak N. Araabi. "Information-Based Evaluation of Approximation Methods in Dempster-Shafer Theory." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 04 (2016): 503–35. http://dx.doi.org/10.1142/s0218488516500252.

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Complexity of computations, particularly due to large number of focal elements (FEs), in Dempster-Shafer theory (DST) motivates the development of approximation algorithms. Existing approximation methods include efficient algorithm for special hypothesis space, Monte Carlo based techniques, and simplification procedures. In this paper, the quality of the simplification-based approximation algorithms is evaluated using a new information-based comparison methodology. To this end, three structured testbeds are introduced. Each testbed is designed with an eye on a particular form of uncertainty as

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Gudiyangada Nachappa, Thimmaiah, Sepideh Tavakkoli Piralilou, Omid Ghorbanzadeh, Hejar Shahabi, and Thomas Blaschke. "Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory." Applied Sciences 9, no. 24 (2019): 5393. http://dx.doi.org/10.3390/app9245393.

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Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster–Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the mode

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Kazemi, Mohammad Reza, Saeid Tahmasebi, Francesco Buono, and Maria Longobardi. "Fractional Deng Entropy and Extropy and Some Applications." Entropy 23, no. 5 (2021): 623. http://dx.doi.org/10.3390/e23050623.

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Deng entropy and extropy are two measures useful in the Dempster–Shafer evidence theory (DST) to study uncertainty, following the idea that extropy is the dual concept of entropy. In this paper, we present their fractional versions named fractional Deng entropy and extropy and compare them to other measures in the framework of DST. Here, we study the maximum for both of them and give several examples. Finally, we analyze a problem of classification in pattern recognition in order to highlight the importance of these new measures.

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Xu, Wei Xiao, Ji Wen Tan, and Hong Zhan. "Research and Application of the Improved DST New Method Based on Fuzzy Consistent Matrix and the Weighted Average." Advanced Materials Research 1030-1032 (September 2014): 1764–68. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1764.

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Aiming at the existing defects of evidence dempster-shafer theory (DST) in dealing with high conflict evidence, we proposed a new method to improve DST. By introducing concept of fuzzy consistent matrix, calculate the weights of factors, and put different sources of evidence into distinguish, and finally cast more than one vote to prevent the phenomenon, the average convergence of evidence. What’s more, the improved DST new method is applied to the rolling bearing fault diagnosis of CNC machine workbench .The test results show that the improved new synthetic formula increases the accuracy of f

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Wang, Xiaochuan. "Robustness evaluation of coal mine based on FAHP and DST." Journal of Computational Methods in Sciences and Engineering 22, no. 1 (2022): 295–303. http://dx.doi.org/10.3233/jcm-215653.

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Enterprise quality management robustness describes the effectiveness of quality management error-proofing system. In accordance with fuzzy analytic hierarchy process (FAHP) and Dempster-Shafer theory (DST), this research constructs the evaluation model of the quality management robustness of coal mine establishes the evaluation index system from seven aspects and three levels, and puts forward the evaluation method. At last, the effectiveness of the error-proofing system of coal mining enterprise is verified.

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Konarski, Jerzy, and Rafał Orlikowski. "A Framework for Detection of Selfishness in Multihop Mobile Ad Hoc Networks." Journal of Telecommunications and Information Technology, no. 2 (June 26, 2023): 34–40. http://dx.doi.org/10.26636/jtit.2009.2.923.

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The paper discusses the need for a fully-distributed selfishness detection mechanism dedicated for multihop wireless ad hoc networks which nodes may exhibit selfish forwarding behavior. The main contribution of this paper is an introduction to a novel approach for detecting and coping with the selfish nodes. Paper describes a new framework based on Dempster-Shafer theory-based selfishness detection framework (DST-SDF) with some mathematical background and simulation analysis.

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Raja Fuad. "Diagnosis Tingkat Penyakit Kulit (Acne Vulgaris) Menggunakan Metode Dempster Shafer." Jurnal Nasional Teknologi Komputer 2, no. 4 (2022): 197–211. http://dx.doi.org/10.61306/jnastek.v2i4.108.

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Acne vulgaris merupakan salah satu penyakit kulit wajah yang sering muncul pada usia remaja dan dewasa. Penyakit ini dapat menurunkan rasa percaya diri penderitanya, serta dapat menimbulkan bekas luka permanen di wajah jika dibiarkan. Permasalahan tersebut dapat ditangani dengan deteksi dini yaitu dengan membangun aplikasi Decision Support System (DSS) yang dapat mendiagnosis tingkat keparahan acne vulgaris dengan menggunakan metode Dempster Shafer Theory (DST). Hasil penelitian berupa aplikasi DSS yang dapat digunakan untuk mendiagnosis tingkat keparahan akne vulgaris, yaitu dengan menghitung

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Liu, Zhe, and Sukumar Letchmunan. "An Improved Weighted Evidence Combination Based on Tangent Similarity and Its Application in Decision-Making." Computer and Decision Making: An International Journal 1 (August 2, 2024): 38–50. http://dx.doi.org/10.59543/comdem.v1i.10237.

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Dempster-Shafer theory (DST) has been widely recognized across multiple disciplines for its superior handling of uncertainty compared to traditional probability theory. Nonetheless, the application of Dempster's rule in the presence of conflicting evidence can lead to outcomes that are sometimes non-intuitive. To mitigate this issue, this paper proposes a new tangent similarity measure within DST to assess the conflict between evidences. The proposed measure adheres to several key properties, enhancing its ability to accurately reflect the similarity between evidences. Additionally, an improve

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15

Ganguly, Kunal. "Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk." International Journal of Productivity and Performance Management 63, no. 1 (2014): 85–102. http://dx.doi.org/10.1108/ijppm-10-2012-0117.

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Purpose – The purpose of this paper is to provide proactive supply chain performance method considering risk which can be used during the supplier selection/assessment process. Design/methodology/approach – In this paper, the effort is to present a model for evaluating the supply-related risk, which is based on the analytic hierarchy process (AHP) method and the Dempster-Shafer theory (DST). The proactive risk management methods used in this research is: seeking risk sources and identifying the variables to be used in the model, preprocessing the variables data to get the directions of the var

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Du, Yuanwei, and Susu Wang. "Multiple Criteria Group Decision-Making Method with Dempster–Shafer Theory and Probabilistic Linguistic Term Sets." Mathematical Problems in Engineering 2020 (December 7, 2020): 1–19. http://dx.doi.org/10.1155/2020/6537048.

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The motivation of this study is to propose a novel multiple criteria group decision-making (MCDGM) method based on Dempster–Shafer theory (DST) and probabilistic linguistic term sets (PLTSs) to handle the distinctions between compensatory information at the criterion level and noncompensatory information at the individual level in the process of information fusion. Initially, the information at the individual level is extracted by BPA functions. Then, they are fused with DST considering ignorance and DMs’ reliabilities. Next, the obtained BPA functions are transformed into interval-valued PLTS

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Choi, Sungwoon. "Analysis of System Reliability Using Intuitionistic Fuzzy Sets and Dempster–Shafer Theory(DST)." Journal of the Korea Management Engineers Society 25, no. 2 (2020): 35–53. http://dx.doi.org/10.35373/kmes.25.2.3.

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Blažević, Damir, Tomislav Keser, Hrvoje Glavaš, and Robert Noskov. "Power Transformer Condition-Based Evaluation and Maintenance (CBM) Using Dempster–Shafer Theory (DST)." Applied Sciences 13, no. 11 (2023): 6731. http://dx.doi.org/10.3390/app13116731.

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Transformers are the most important elements in the power system. Due to their mass and complexity, they require constant monitoring and maintenance. Maintenance of power transformers increases the availability of the power system. The large number of substations and the specifics of their locations make condition-based maintenance (CBM) useful as part of the system's on-demand response. Unlike other system responses, the transformer contains a large amount of uncertain information, both qualitative and numerical. A large amount of information is necessary to implement CBM, but due to the ofte

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Garg, Harish, R. Sujatha, D. Nagarajan, J. Kavikumar, and Jeonghwan Gwak. "Evidence Theory in Picture Fuzzy Set Environment." Journal of Mathematics 2021 (May 18, 2021): 1–8. http://dx.doi.org/10.1155/2021/9996281.

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Picture fuzzy set is the most widely used tool to handle the uncertainty with the account of three membership degrees, namely, positive, negative, and neutral such that their sum is bound up to 1. It is the generalization of the existing intuitionistic fuzzy and fuzzy sets. This paper studies the interval probability problems of the picture fuzzy sets and their belief structure. The belief function is a vital tool to represent the uncertain information in a more effective manner. On the other hand, the Dempster–Shafer theory (DST) is used to combine the independent sources of evidence with the

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Liu, Peide, Xiaoxiao Liu, Guiying Ma, Zhaolong Liang, Changhai Wang, and Fawaz E. Alsaadi. "A Multi-Attribute Group Decision-Making Method Based on Linguistic Intuitionistic Fuzzy Numbers and Dempster–Shafer Evidence Theory." International Journal of Information Technology & Decision Making 19, no. 02 (2020): 499–524. http://dx.doi.org/10.1142/s0219622020500042.

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In this paper, we propose a multi-attribute group decision-making (MAGDM) method based on Dempster–Shafer Evidence Theory (DST) and linguistic intuitionistic fuzzy numbers (LIFNs), in which both the expert weights and attribute weights are unknown. Firstly, we represent LIFNs as basic probability assignments (BPAs) by DST based on linguistic scale function (LSF), and a linear programming model is proposed to combine the objective weights and subjective weights of attributes to obtain the combined weights. At the same time, the experts’ weights are obtained through Jousselme distance. Secondly,

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Bezerra, Eduardo Devidson Costa, Ariel Soares Teles, Luciano Reis Coutinho, and Francisco José da Silva e Silva. "Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing." Sensors 21, no. 5 (2021): 1863. http://dx.doi.org/10.3390/s21051863.

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The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). I

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NUSRAT, ELHUM, and KOICHI YAMADA. "A DESCRIPTIVE DECISION-MAKING MODEL UNDER UNCERTAINTY: COMBINATION OF DEMPSTER-SHAFER THEORY AND PROSPECT THEORY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 01 (2013): 79–102. http://dx.doi.org/10.1142/s0218488513500050.

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In this paper, a descriptive decision-making model under uncertainty is proposed which incorporates two types of decision attitudes for uncertainty; one is an attitude about ignorance (optimism/pessimism) and the other one is about risk (risk-seeking and risk-aversion). At first, Evidential Decision Making Problem (EDMP) has been defined where Dempster-Shafer Theory (DST) has been used to represent uncertainty. Then probability approximation approach of solving EDMP is shown. For deciding the decision weights in different attitudes of decision maker, Ordered Weighted Averaging (OWA) operator h

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Szczuko, Piotr, Arkadiusz Harasimiuk, and Andrzej Czyżewski. "Evaluation of Decision Fusion Methods for Multimodal Biometrics in the Banking Application." Sensors 22, no. 6 (2022): 2356. http://dx.doi.org/10.3390/s22062356.

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An evaluation of decision fusion methods based on Dempster-Shafer Theory (DST) and its modifications is presented in the article, studied over real biometric data from the engineered multimodal banking client verification system. First, the approaches for multimodal biometric data fusion for verification are explained. Then the proposed implementation of comparison scores fusion is presented, including details on the application of DST, required modifications, base probability, and mass conversions. Next, the biometric verification process is described, and the engineered biometric banking sys

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Soroush, Morteza Zangeneh, Keivan Maghooli, Seyed Kamaledin Setarehdan, and Ali Motie Nasrabadi. "A NOVEL METHOD OF EEG-BASED EMOTION RECOGNITION USING NONLINEAR FEATURES VARIABILITY AND DEMPSTER–SHAFER THEORY." Biomedical Engineering: Applications, Basis and Communications 30, no. 04 (2018): 1850026. http://dx.doi.org/10.4015/s1016237218500266.

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These days, emotion recognition has been receiving more attention due to the growth of the brain–computer interfaces (systems) (BCIs). Moreover, estimating emotions is widely used in different aspects such as psychology, neuroscience, entertainment, e-learning, etc. This paper aims to classify emotions through EEG signals. When it comes to emotion recognition, participants’ opinions toward induced emotions are really case-dependent and thus corresponding labels might be imprecise and uncertain. Furthermore, it is acceptable that mixture classifiers lead to higher accuracy (ACE) and lower uncer

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Chen, Yutong, and Yongchuan Tang. "Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion." Entropy 23, no. 4 (2021): 402. http://dx.doi.org/10.3390/e23040402.

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Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final u

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Du, Yuan-Wei, Yu-Kun Shan, Chang-Xing Li, and Rui Wang. "Mass Collaboration-Driven Method for Recommending Product Ideas Based on Dempster-Shafer Theory of Evidence." Mathematical Problems in Engineering 2018 (September 27, 2018): 1–10. http://dx.doi.org/10.1155/2018/1980152.

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In the mass collaboration mode, there exist a large number of product ideas with low value density and thousands of participants who are differed on their professional backgrounds, knowledge structures, and value orientations. It is impossible for each participant to give a comprehensive evaluation of each idea as that in traditional methods for the reasons as mentioned above. In order to solve this problem, a mass collaboration-driven method for recommending product ideas is proposed based on Dempster-Shafer theory of evidence (DST). Firstly, the method for computing basic probability assignm

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Pan, Qian, Deyun Zhou, Yongchuan Tang, Xiaoyang Li, and Jichuan Huang. "A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy." Entropy 21, no. 2 (2019): 163. http://dx.doi.org/10.3390/e21020163.

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Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transform

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Aguilar, Paulo Armando Cavalcante, Jerome Boudy, Dan Istrate, et al. "Evidential Network-Based Multimodal Fusion for Fall Detection." International Journal of E-Health and Medical Communications 4, no. 1 (2013): 46–60. http://dx.doi.org/10.4018/jehmc.2013010105.

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The multi-sensor fusion can provide more accurate and reliable information compared to information from each sensor separately taken. Moreover, the data from multiple heterogeneous sensors present in the medical surveillance systems have different degrees of uncertainty. Among multi-sensor data fusion techniques, Bayesian methods and Evidence theories such as Dempster-Shafer Theory (DST) are commonly used to handle the degree of uncertainty in the fusion processes. Based on a graphic representation of the DST called Evidential Networks, we propose a structure of heterogeneous multi-sensor fusi

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Zhang, Yu, Fanghui Huang, Xinyang Deng, and Wen Jiang. "A New Total Uncertainty Measure from A Perspective of Maximum Entropy Requirement." Entropy 23, no. 8 (2021): 1061. http://dx.doi.org/10.3390/e23081061.

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The Dempster-Shafer theory (DST) is an information fusion framework and widely used in many fields. However, the uncertainty measure of a basic probability assignment (BPA) is still an open issue in DST. There are many methods to quantify the uncertainty of BPAs. However, the existing methods have some limitations. In this paper, a new total uncertainty measure from a perspective of maximum entropy requirement is proposed. The proposed method can measure both dissonance and non-specificity in BPA, which includes two components. The first component is consistent with Yager’s dissonance measure.

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Huang, Min, and Chang Zhang. "A Novel Multi-Source Domain Adaptation Method with Dempster–Shafer Evidence Theory for Cross-Domain Classification." Mathematics 10, no. 15 (2022): 2797. http://dx.doi.org/10.3390/math10152797.

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In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much information, the processing of domain shifts becomes more challenging, especially in learning a common domain-invariant representation for all domains. Moreover, it is counter-intuitive to treat multiple source domains equally as most existing MDA algorithms do. Therefore, the domain-specific distribution for each source–target domain pair is alig

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Ahmad, Tawsif, Ning Zhou, Ziang Zhang, and Wenyuan Tang. "Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models." Energies 17, no. 10 (2024): 2392. http://dx.doi.org/10.3390/en17102392.

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Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast err

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Kenn, Michael, Rudolf Karch, Dan Cacsire Castillo-Tong, Christian F. Singer, Heinz Koelbl, and Wolfgang Schreiner. "Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer." Journal of Personalized Medicine 12, no. 4 (2022): 570. http://dx.doi.org/10.3390/jpm12040570.

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Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different te

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Li, Wei, Deqiang Han, Jean Dezert, and Yi Yang. "Basic Belief Assignment Determination Based on Radial Basis Function Network." Chinese Journal of Information Fusion 1, no. 3 (2024): 175–82. https://doi.org/10.62762/cjif.2024.841250.

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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 corres

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Zhou, Jibiao, Xinhua Mao, Yiting Wang, Minjie Zhang, and Sheng Dong. "Risk Assessment in Urban Large-Scale Public Spaces Using Dempster-Shafer Theory: An Empirical Study in Ningbo, China." International Journal of Environmental Research and Public Health 16, no. 16 (2019): 2942. http://dx.doi.org/10.3390/ijerph16162942.

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Urban Large-scale Public Spaces (ULPS) are important areas of urban culture and economic development, which are also places of the potential safety hazard. ULPS safety assessment has played a crucial role in the theory and practice of urban sustainable development. The primary objective of this study is to explore the interaction between ULPS safety risk and its influencing factors. In the first stage, an index sensitivity analysis method was applied to calculate and identify the safety risk assessment index system. Next, a Delphi method and information entropy method were also applied to coll

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Yang, Miin-Shen, Zahid Hussain, and Mehboob Ali. "Belief and Plausibility Measures on Intuitionistic Fuzzy Sets with Construction of Belief-Plausibility TOPSIS." Complexity 2020 (August 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/7849686.

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Belief and plausibility measures in Dempster–Shafer theory (DST) and fuzzy sets are known as different approaches for representing partial, uncertainty, and imprecise information. There are several generalizations of DST to fuzzy sets proposed in the literature. But, less generalization of DST to intuitionistic fuzzy sets (IFSs), that can somehow present imprecise information better than fuzzy sets, was proposed. In this paper, we first propose a simple and intuitive way to construct a generalization of DST to IFSs with degrees of belief and plausibility in terms of degrees of membership and n

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Zhu, WenBo, Huicheng Yang, Yi Jin, and Bingyou Liu. "A Method for Recognizing Fatigue Driving Based on Dempster-Shafer Theory and Fuzzy Neural Network." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6191035.

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This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability of recognizing fatigue driving. This method measures driving states using multifeature fusion. First, FNN is introduced to obtain the basic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function. Second, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable information exists. Finally, the recognition result is given according to the comb

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Xue, Hongtao, Zhongxing Li, Huaqing Wang, and Peng Chen. "Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine." Shock and Vibration 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/407570.

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This paper proposed an intelligent diagnosis method for a centrifugal pump system using statistic filter, support vector machine (SVM), possibility theory, and Dempster-Shafer theory (DST) on the basis of the vibration signals, to diagnose frequent faults in the centrifugal pump at an early stage, such as cavitation, impeller unbalance, and shaft misalignment. Firstly, statistic filter is used to extract the feature signals of pump faults from the measured vibration signals across an optimum frequency region, and nondimensional symptom parameters (NSPs) are defined to represent the feature sig

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Zhou, Ying, Yongchuan Tang, and Xiaozhe Zhao. "A Novel Uncertainty Management Approach for Air Combat Situation Assessment Based on Improved Belief Entropy." Entropy 21, no. 5 (2019): 495. http://dx.doi.org/10.3390/e21050495.

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Uncertain information exists in each procedure of an air combat situation assessment. To address this issue, this paper proposes an improved method to address the uncertain information fusion of air combat situation assessment in the Dempster–Shafer evidence theory (DST) framework. A better fusion result regarding the prediction of military intention can be helpful for decision-making in an air combat situation. To obtain a more accurate fusion result of situation assessment, an improved belief entropy (IBE) is applied to preprocess the uncertainty of situation assessment information. Data fus

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Gao, Zhang, and Liu. "Multi-Attribute Decision Making Based on Intuitionistic Fuzzy Power Maclaurin Symmetric Mean Operators in the Framework of Dempster-Shafer Theory." Symmetry 11, no. 6 (2019): 807. http://dx.doi.org/10.3390/sym11060807.

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It is well known that there are some unfavorable shortcomings in the ordinary operational rules (OORs) of intuitionistic fuzzy number (IFN), and there exists a close and forceful connection between the intuitionistic fuzzy set (IFS) and Dempster-Shafer Theory (DST). We can utilize this relationship to present a transparent and fruitful semantic framework for IFS in terms of DST. In the framework of DST, an IFN can be converted into a basic probability assignment (BPA) and operations on IFNs can be represented as operations on a belief interval (BI), which can break away from the revealed short

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Hussain, Shahid, Zahid Hussain, Rashid Hussain, et al. "A Novel Framework for Belief and Plausibility Measures in Intuitionistic Fuzzy Sets: Belief and Plausibility Distance, Similarity, and TOPSIS for Multicriteria Decision Making." Axioms 13, no. 12 (2024): 858. https://doi.org/10.3390/axioms13120858.

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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 dis

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Mr. S.V Hemanth, S Sneha Reddy, R Nithin, G Keerthi, and Shinde Vinayak Rao Patil. "Automated Bot Detection on Twitter UsingURL Patterns and Learning Automata." international journal of engineering technology and management sciences 8, no. 3 (2024): 205–10. http://dx.doi.org/10.46647/ijetms.2024.v08i03.025.

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The fight against fake news and propaganda on social media becomes increasinglydifficult as malicious bots impersonate real users. These imposters spread misinformation throughcompromised or inauthentic accounts, often tricking users with shortened URLs that contain virusesand lead to malicious websites. Therefore, distinguishing between these bots and genuine Twitterusers is crucial. Analyzing user interactions within the social network can be time-consuming. Thisresearch proposes a more efficient approach: LA-MSBD, an algorithm that relies on learningautomata. LA-MSBD focuses on URL-based fe

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Liu, Jiahuan, Ping Jian, Desheng Liu, and Wei Xiong. "Probabilistic Hesitant Fuzzy Evidence Theory and Its Application in Capability Evaluation of a Satellite Communication System." Entropy 26, no. 1 (2024): 94. http://dx.doi.org/10.3390/e26010094.

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Evaluating the capabilities of a satellite communication system (SCS) is challenging due to its complexity and ambiguity. It is difficult to accurately analyze uncertain situations, making it difficult for experts to determine appropriate evaluation values. To address this problem, this paper proposes an innovative approach by extending the Dempster-Shafer evidence theory (DST) to the probabilistic hesitant fuzzy evidence theory (PHFET). The proposed approach introduces the concept of probabilistic hesitant fuzzy basic probability assignment (PHFBPA) to measure the degree of support for propos

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Bethuel, Carl, Damien Arvor, Thomas Corpetti, et al. "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." Remote Sensing 17, no. 2 (2025): 234. https://doi.org/10.3390/rs17020234.

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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

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Zhou, Xuelian, and Yongchuan Tang. "Modeling and Fusing the Uncertainty of FMEA Experts Using an Entropy-Like Measure with an Application in Fault Evaluation of Aircraft Turbine Rotor Blades." Entropy 20, no. 11 (2018): 864. http://dx.doi.org/10.3390/e20110864.

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As a typical tool of risk analysis in practical engineering, failure mode and effects analysis (FMEA) theory is a well known method for risk prediction and prevention. However, how to quantify the uncertainty of the subjective assessments from FMEA experts and aggregate the corresponding uncertainty to the classical FMEA approach still needs further study. In this paper, we argue that the subjective assessments of FMEA experts can be adopted to model the weight of each FMEA expert, which can be regarded as a data-driven method for ambiguity information modeling in FMEA method. Based on this ne

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Zhang, Yiqian, Yutong Dai, and Bo Liu. "Identifying Qualified Public Safety Education Venues Using the Dempster–Shafer Theory-Based PROMETHEE Method under Linguistic Environments." Mathematics 11, no. 4 (2023): 1011. http://dx.doi.org/10.3390/math11041011.

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How to improve safety awareness is an important topic, and it is of great significance for the public to reduce losses in the face of disasters and crises. A public safety education venue is an important carrier to realize safety education, as it has the characteristics of professionalism, comprehensiveness, experience, interest, participation, and so on, arousing the enthusiasm of the public for learning. As a meaningful supplement to “formal safety education”, venue education has many advantages. However, there are problems in the current venue construction such as imperfect infrastructure,

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He, Hong, Xuelin Zhang, Xiaobin Xu, et al. "A Data Classifier Based on Maximum Likelihood Evidential Reasoning Rule." Mathematical Problems in Engineering 2023 (January 14, 2023): 1–14. http://dx.doi.org/10.1155/2023/5933793.

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In Dempster–Shafer evidence theory (DST), some classical evidence combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as to increase the accuracy of multiattribute classification decision making. However, most of them have not yet considered the interdependence among multiple pieces of evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous interdependence by introducing correlation factors into evidence combination. Hence, this paper designs a MAKER-based class

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Bougofa, M., A. Bouafia, A. Baziz, S. Aberkane, R. Kharzi, and A. Bellaouar. "Risk analysis-based reliability assessment approach under epistemic uncertainty using a dynamic evidential network." IOP Conference Series: Earth and Environmental Science 896, no. 1 (2021): 012035. http://dx.doi.org/10.1088/1755-1315/896/1/012035.

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Abstract Probabilistic modeling is widely used in industrial practices, particularly for assessing complex systems’ safety, risk analysis, and reliability. Conventional risk analysis methodologies generally have a limited ability to deal with dependence, failure behavior, and epistemic uncertainty such as parameter uncertainty. This work proposes a risk-based reliability assessment approach using a dynamic evidential network (DEN). The proposed model integrates Dempster-Shafer theory (DST) for describing parameter uncertainty with a dynamic Bayesian network (DBN) for dependency representation

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Dymova, Ludmila, Krzysztof Kaczmarek, Pavel Sevastjanov, Łukasz Sułkowski, and Krzysztof Przybyszewski. "An Approach to Generalization of the Intuitionistic Fuzzy Topsis Method in the Framework of Evidence Theory." Journal of Artificial Intelligence and Soft Computing Research 11, no. 2 (2021): 157–75. http://dx.doi.org/10.2478/jaiscr-2021-0010.

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Abstract A generalization of technique for establishing order preference by similarity to the ideal solution (TOPSIS) in the intuitionistic fuzzy setting based on the redefinition of intuitionistic fuzzy sets theory (A IFS) in the framework of Dempster-Shafer theory (DST) of evidence is proposed. The use of DST mathematical tools makes it possible to avoid a set of limitations and drawbacks revealed recently in the conventional Atanassov’s operational laws defined on intuitionistic fuzzy values, which may produce unacceptable results in the solution of multiple criteria decision-making problem

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Wu, Chong, Zijiao Zhang, and Wei Zhong. "A Group Decision-Making Approach Based on DST and AHP for New Product Selection under Epistemic Uncertainty." Mathematical Problems in Engineering 2019 (June 19, 2019): 1–16. http://dx.doi.org/10.1155/2019/4635374.

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Selecting the most appropriate new product(s) is regarded as a critical decision which greatly influences the development of manufacturing enterprises. In order to improve the accuracy of selection, more experts are required to be invited to predict key indicators for new products selection. Due to limited knowledge, experts use fuzzy numbers more confidently than using numerical values in the prediction. Therefore, new product selection is a multiattribute group decision-making process under epistemic uncertainty. The purpose of this paper is to introduce a new hybrid decision-making approach

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Chen, Xingyuan, and Yong Deng. "An Evidential Software Risk Evaluation Model." Mathematics 10, no. 13 (2022): 2325. http://dx.doi.org/10.3390/math10132325.

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Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Sh

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