Relevant bibliographies by topics / Dempster-Shafer theory (DST)

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  2. Dissertations / Theses
  3. Book chapters
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Academic literature on the topic 'Dempster-Shafer theory (DST)'

Author: Grafiati
Published: 2 August 2022
Last updated: 31 July 2025

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

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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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Dissertations / Theses on the topic "Dempster-Shafer theory (DST)"

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Tong, Zheng. "Evidential deep neural network in the framework of Dempster-Shafer theory." Thesis, Compiègne, 2022. http://www.theses.fr/2022COMP2661.

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Les réseaux de neurones profonds (DNN) ont obtenu un succès remarquable sur de nombreuses applications du monde réel (par exemple, la reconnaissance de formes et la segmentation sémantique), mais sont toujours confrontés au problème de la gestion de l'incertitude. La théorie de Dempster-Shafer (DST) fournit un cadre bien fondé et élégant pour représenter et raisonner avec des informations incertaines. Dans cette thèse, nous avons proposé un nouveau framework utilisant DST et DNNs pour résoudre les problèmes d'incertitude. Dans le cadre proposé, nous hybridons d'abord DST et DNN en branchant un
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Taroun, Abdulmaten. "Decision Support System (DSS) for construction project risk analysis and evaluation via evidential reasoning (ER)." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/decision-support-system-dss-for-construction-project-risk-analysis-and-evaluation-via-evidential-reasoning-er(1eb74da2-ded1-4ea7-8f50-1fc6edd12353).html.

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This research explores the theory and practice of risk assessment and project evaluationand proposes novel alternatives. Reviewing literature revealed a continuous endeavourfor better project risk modelling and analysis. A number of proposals for improving theprevailing Probability-Impact (P-I) risk model can be found in literature. Moreover,researchers have investigated the feasibility of different theories in analysing projectrisk. Furthermore, various decision support systems (DSSs) are available for aidingpractitioners in risk assessment and decision making. Unfortunately, they are sufferi
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Book chapters on the topic "Dempster-Shafer theory (DST)"

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Beynon, Malcolm J. "Effective Intelligent Data Mining Using Dempster-Shafer Theory." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch188.

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The efficacy of data mining lies in its ability to identify relationships amongst data. This chapter investigates that constraining this efficacy is the quality of the data analysed, including whether the data is imprecise or in the worst case incomplete. Through the description of Dempster-Shafer theory (DST), a general methodology based on uncertain reasoning, it argues that traditional data mining techniques are not structured to handle such imperfect data, instead requiring the external management of missing values, and so forth. One DST based technique is classification and ranking belief
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Dutta, Palash. "Fuzzy-DSS Human Health Risk Assessment Under Uncertain Environment." In Handbook of Research on Investigations in Artificial Life Research and Development. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5396-0.ch015.

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It is always utmost essential to accumulate knowledge on the nature of each and every accessible data, information, and model parameters in risk assessment. It is noticed that more often model parameters, data, information are fouled with uncertainty due to lack of precision, deficiency in data, diminutive sample sizes. In such environments, fuzzy set theory or Dempster-Shafer theory (DST) can be explored to represent this type of uncertainty. Most frequently, both types of uncertainty representation theories coexist in human health risk assessment and need to merge within the same framework.
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Hung, Nguyen Duy, Nam-Van Huynh, Thanaruk Theeramunkong, and Tho-Quy Nhu. "Composite Argumentation Systems with ML Components." In Computational Models of Argument. IOS Press, 2022. http://dx.doi.org/10.3233/faia220150.

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Today AI systems are rarely made without Machine Learning (ML) and this inspires us to explore what aptly called composite argumentation systems with ML components. Concretely, against two theoretical backdrops of PABA (Probabilistic Assumption-based Argumentation) and DST (Dempster-Shafer Theory), we present a framework for such systems called c-PABA. It is argued that c-PABA lends itself to a development tool as well and to demonstrate we show that DST-based ML classifier combination and multi-source data fusion can be implemented as simple c-PABA frameworks.
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Xu, Zhikang, Xiaodong Yue, and Ying Lv. "Trusted Fine-Grained Image Classification Based on Evidence Theory and Its Applications to Medical Image Analysis." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-4292-3.ch010.

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Fine-grained image classification (FGIC) aims to classify object of images to the corresponding subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. Dempster-Shafer evidence theory (DST) is widely applied in reasoning with uncertainty and opinion fusion. Recently, researchers extended DST by combining it with deep learning to measure the uncertainty of deep neural networks and perform uncertainty classification. In this proposed chapter, the authors provide
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Davydenko, Yevhen, and Alyona Shved. "THE METHODOLOGY OF SYNTHESIS OF INFORMATION TECHNOLOGIES FOR DECISION SUPPORT UNDER COMPLEX FORMS OF IGNORANCE." In Theoretical and practical aspects of science development. Publishing House “Baltija Publishing”, 2023. http://dx.doi.org/10.30525/978-9934-26-355-2-11.

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An important problem of system analysis is the disclosure of uncertainties due to the variety of purposes, properties and features of the studied objects and processes. The analysis and management of various types of ignorance is of primary importance, since the processes of intelligent technologies creating always proceed under contradiction, incompleteness, inaccuracy, uncertainty connected with processes of obtaining and processing of datasets and expert knowledges. The purpose of the paper is to improve the theoretical and methodological foundations of the synthesis of information technolo
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Dutta, Palash. "Fuzzy-Probability." In Advanced Fuzzy Logic Approaches in Engineering Science. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5709-8.ch009.

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Human health risk assessment is an important and a popular aid in the decision-making process. The basic objective of risk assessment is to assess the severity and likelihood of impairment to human health from exposure to a substance or activity that under plausible circumstances can cause harm to human health. One of the most important aspects of risk assessment is to accumulate knowledge on the features of each and every available data, information and model parameters involved in risk assessment. It is observed that most frequently model parameters, data, and information are tainted with al
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Conference papers on the topic "Dempster-Shafer theory (DST)"

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Anugolu, Madhavi, Chandrasekhar Potluri, Alex Urfer, and Marco P. Schoen. "A Motor Point Identification Technique Based on Dempster Shafer Theory." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6102.

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The objective of this work is to identify the motor point location from the obtained sEMG signals using Dempster Shafer theory (DST). The proposed technique is applied on data obtained from a male test subject. In particular, the sEMG signals and its corresponding skeletal muscle force signals from the Flexor Digitorum Superficialis are acquired at a sampling rate of 2000 Hz using a Delsys Bangnoli- 16 EMG system. The acquired sEMG signals are rectified and filtered using a Discrete Wavelet Transforms (DWT) with a Daubechies 44 mother wavelet. For the system identification, an Output Error (OE
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Sri Wahyuni, Ias, and Rachid Sabre. "Dempster-Shafer and Multi-Focus Image Fusion using Local Distance." In 7th International Conference on Computer Science and Information Technology (CSTY 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112206.

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In this article, we give a new method of multi-focus fusion images based on Dempster-Shafer theory using local variability (DST-LV). Indeed, the method takes into account the variability of observations of neighbouring pixels at the point studied. At each pixel, the method exploits the quadratic distance between the value of the pixel I (x, y) of the point studied and the value of all pixels which belong to its neighbourhood. Local variability is used to determine the mass function. In this work, two classes of Dempster-Shafer theory are considered: the fuzzy part and the focused part. We show
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Zhao, C. M., J. Wei, Z. G. Xing, and Z. Wei. "Application of DSmT in Facial Expression Recognition." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86635.

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As the sample in facial expression database is small, the influence of the environment and the actual expression image processing can cause face feature information uncertainty and conflict of different information. This paper presents how to solve the small sample problem and the fusion of global feature recognition results and local feature recognition results based on DSmT (Dezert-Smarandache Theory) by matlab, the results show that DSmT can better handle the face expression of uncertainty information and contradictory information than DST (Dempster-Shafer Theory), recognition effect has be
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Popov, Mikhail A., and Maxim V. Topolnitskiy. "A Dempster-Shafer evidence theory-based approach to object classification on multispectral/hyperspectral images." In 2014 International Conference on Digital Technologies (DT). IEEE, 2014. http://dx.doi.org/10.1109/dt.2014.6868729.

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Reports on the topic "Dempster-Shafer theory (DST)"

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Skau, Erik, Gregory Bowers, Cassandra Armstrong, and Kelly Malone. Open World Dempster-Shafer Theory/The Transferable Belief Model with Intervals A Practitioner's Guide to DST and TBM. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2558017.

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