Academic literature on the topic 'Dempster-Shafer theory (DST)'
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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 tools to deal with uncertainty. Among these tools is Dempster-Shafer Theory (DST), which has been shown to be effective at dealing with the inherent uncertainty in numerous applications. This article provides a survey of the use of DST in the RS field. Thus, after a brief introduction to recommender systems and the DST, this survey discusses recent DST applications in RS. It introduces a new taxonomy that encompasses the primary application context for DST-based RS solutions, as well as a comprehensive multi-criteria analysis of the peer-reviewed papers. The resulting comparisons are analyzed to draw conclusions, identify current study limitations, and define future research directions. This survey serves as a valuable resource for the entire research community that is interested in recommender systems and DST.
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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/normal fuzzy numbers within the same framework. Sampling technique for evidence theory and alpha-cut for fuzzy numbers are considered to execute the algorithms. Finally, results are obtained in the form of fuzzy numbers (normal/generalized) at different fractiles.
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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 of data aggregation has been criticized because it can lead to non-intuitive results. Many alternative methods have been proposed and several were tested during our experiments. Most place classification methods assume a closed world model, i.e. a test sample is assigned to the most probable class even if its corresponding probability is very small. An advantage of our system is the intrinsic capability of giving unknown class as an answer in such situations, which can be used by the robot to take appropriate actions.
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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 them to results of other methods.
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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 combining uncertain evidence. The literature review showed that there have been no studies, either using BNs or DST, to diagnose eye diseases with a comparative study about both methods, BNs and DST. This paper study the effectiveness and reliability of DST and BN as the reasoning engine of an expert system for early diagnose of eye disease. The primary sources of knowledge on eye diseases are the patient files and human experts. Data were collected from hospitals and ophthalmologists in Riau, Indonesia. BN and DST framework was used to model and estimate the probability of eye diseases in supporting decision making, i.e. diagnosis. Rule-Based Reasoning and the Forward Chaining methods are applied in developing the reasoning structure. The Expert System Development Life Cycle (ESDLC) methodology is used to structure, plan and control the process of developing the expert system. In this study, 20 physical symptoms of illness obtained from the patients' files are used for diagnosing six types of eye diseases. The result of this study is accomplished by comparing the expert system diagnostic results with a human expert diagnostic result. Based on the testing of 10 eye diseases cases, the accuracy of the BN is higher compared to DST.
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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 capital market to the exchange rate fluctuations in the different scenarios due to the lack of a unified view of the value of that rate among experts as one of the mentioned factors and obtain its value using Dempster–Shafer theory (DST). Then, a portfolio selection model that prefers stocks with higher ranks is proposed. Representative results of the real-life case study reveal that the submitted approach is productive and practically applicable.
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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 associated with a body of evidence (BoE) in DST, i.e., conflict and non-specificity. Three proposed testbeds along with the classic testbed are utilized to evaluate the accuracy and complexity of existing algorithms. In light of the proposed evaluation methodology, a new approximation method is presented as well. The proposed algorithm has the ability to choose the most informative FEs without being forced to select the FEs with the largest mass function. Comparison of overall qualitative performance of approximation algorithms provides accuracy versus computational time tradeoff to choose an appropriate approximation method in different applications. Moreover, experiments with testbeds indicate that our proposed algorithm enhances the accuracy and computational tractability simultaneously.
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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 model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster–Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers.
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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 fault diagnosis Ball, the conflict of evidence synthesis results better, to achieve better results.
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 une couche de réseau neuronal basée sur DST suivie d'une couche utilitaire à la sortie d'un réseau neuronal convolutif pour la classification à valeur définie. Nous étendons également l'idée à la segmentation sémantique en combinant des réseaux entièrement convolutifs et DST. L'approche proposée améliore les performances des modèles DNN en attribuant des modèles ambigus avec une incertitude élevée, ainsi que des valeurs aberrantes, à des ensembles multi-classes. La stratégie d'apprentissage utilisant des étiquettes souples améliore encore les performances des DNN en convertissant des données d'étiquettes imprécises et non fiables en fonctions de croyance. Nous avons également proposé une stratégie de fusion modulaire utilisant ce cadre proposé, dans lequel un module de fusion agrège les sorties de la fonction de croyance des DNN évidents selon la règle de Dempster. Nous utilisons cette stratégie pour combiner des DNN formés à partir d'ensembles de données hétérogènes avec différents ensembles de classes tout en conservant des performances au moins aussi bonnes que celles des réseaux individuels sur leurs ensembles de données respectifs. De plus, nous appliquons la stratégie pour combiner plusieurs réseaux superficiels et obtenir une performance similaire d'un DNN avancé pour une tâche compliquée<br>Deep neural networks (DNNs) have achieved remarkable success on many realworld applications (e.g., pattern recognition and semantic segmentation) but still face the problem of managing uncertainty. Dempster-Shafer theory (DST) provides a wellfounded and elegant framework to represent and reason with uncertain information. In this thesis, we have proposed a new framework using DST and DNNs to solve the problems of uncertainty. In the proposed framework, we first hybridize DST and DNNs by plugging a DSTbased neural-network layer followed by a utility layer at the output of a convolutional neural network for set-valued classification. We also extend the idea to semantic segmentation by combining fully convolutional networks and DST. The proposed approach enhances the performance of DNN models by assigning ambiguous patterns with high uncertainty, as well as outliers, to multi-class sets. The learning strategy using soft labels further improves the performance of the DNNs by converting imprecise and unreliable label data into belief functions. We have also proposed a modular fusion strategy using this proposed framework, in which a fusion module aggregates the belief-function outputs of evidential DNNs by Dempster’s rule. We use this strategy to combine DNNs trained from heterogeneous datasets with different sets of classes while keeping at least as good performance as those of the individual networks on their respective datasets. Further, we apply the strategy to combine several shallow networks and achieve a similar performance of an advanced DNN for a complicated task
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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 sufferingfrom a low take-up. Instead, personal judgment and past experience are mainly used foranalysing risk and making decisions.In this research, a new risk model is proposed through extending the P-I risk model toinclude a third dimension: probability of impact materialisation. Such an extensionreflects the characteristics of a risk, its surrounding environment and the ability ofmitigating its impact. A new assessment methodology is devised. Dempster-ShaferTheory of Evidence (DST) is researched and presented as a novel alternative toProbability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature ofproject risks analysis. A DST-based assessment methodology was developed forstructuring the personal experience and professional judgment of risk analysts andutilising them for risk analysis. Benefiting from the unique features of the EvidentialReasoning (ER) approach, the proposed methodology enables analysts to express theirevaluations in distributed forms, so that they can provide degrees of belief in apredefined set of assessment grades based on available information. This is a veryeffective way for tackling the problem of lack of information which is an inherentfeature of most projects during the tendering stage. It is the first time that such anapproach is ever used for handling construction risk assessment. Monetary equivalent isused as a common scale for measuring risk impact on various project success objectives,and the evidential reasoning (ER) algorithm is used as an assessment aggregation toolinstead of the simple averaging procedure which might not be appropriate in allsituations. A DST-based project evaluation framework was developed using projectrisks and benefits as evaluation attributes. Monetary equivalent was used also as acommon scale for measuring project risks and benefits and the ER algorithm as anaggregation tool.The viability of the proposed risk model, assessment methodology and projectevaluation framework was investigated through conducting interviews with constructionprofessionals and administering postal and online questionnaires. A decision supportsystem (DSS) was devised to facilitate the proposed approaches and to perform therequired calculations. The DSS was developed in light of the research findingsregarding the reasons of low take-up of the existing tools. Four validation case studieswere conducted. Senior managers in separate British construction companies tested thetool and found it useful, helpful and easy to use.It is concluded that the proposed risk model, risk assessment methodology and projectevaluation framework could be viable alternatives to the existing ones. Professionalexperience was modelled and utilised systematically for risk and benefit analysis. Thismay help closing the gap between theory and practice of risk analysis and decisionmaking in construction. The research findings recommend further exploration of thepotential applications of DST and ER in construction management domain.
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 simplex (CaRBS), which allows intelligent data mining through the acceptance of missing values in the data analysed, considering them a factor of ignorance, and not requiring their external management. Results presented here, using CaRBS and a number of simplex plots, show the effect of managing and not managing of imperfect data.
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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. For this purpose, this chapter presents two algorithms to combine Dempster-Shafer structure (DSS) with generalized/normal fuzzy focal elements, generalized/normal fuzzy numbers within the same framework. Computer codes are generated using Matlab M-files. Finally, human health risk assessment is carried out under this setting and it is observed that the results are obtained in the form of fuzzy numbers (normal/generalized) at different fractiles.
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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 a detailed introduction of how to integrate ENN to construct the trusted FGIC model. Compared with the traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. In addition, they introduce the application of FGIC in medical image analysis to achieve trusted fine-grained medical image classification.
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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 technologies for decision-making under complex forms of ignorance. Methodology of the study is based on system analysis methods, decision-making theory, theory of evidence (Dempster-Shafer theory, DST), theory of plausible and paradoxical reasoning (Dezert-Smarandache theory, DSmT). Results. The methodology for the synthesis of information technologies for the generation of individual and group management decisions under multi-criteria, multi alternatives and different types of ignorance has been proposed in this paper. In addition to certain types of ignorance, the situations occurring under the influence of complex forms of ignorance modeled by framework of Dempster-Shafer and Dezert-Smarandache theories have been analyzed. In this case, conditions of mutual exclusivity and/or mutual exhaustion of elements of the initial dataset are imposed. The paper proposes a procedure for choosing an expert evidence combination rule, which is based on the determination of quantitative indicators of uncertainty. The proposed technique allows to filter out from the initial set of combination rules acceptable for solving the problem under consideration that part of rules that does not satisfy the imposed restrictions and conditions. Next, for each pair of expert evidence that are combined, choose a rule that minimizes the value of the contradiction measure and maximizes the value of specificity measure for the result of combination. Practical implications. The proposed methodology and information technology provide a theoretical and practical foundations for intelligent support of processes of acquisition (synthesis), processing, and analysis data and expert knowledges under complex types of ignorance in decision support systems. Value/originality. The proposed methodology for synthesis of information technologies provides intellectual support for the processes of preparation and support of effective and optimal management decisions when solving non-criteria and multi-criteria problems under complex forms of ignorance generated by the simultaneous presence of two or more types of ignorance, such as importance + inconsistency (conflict), uncertainty + contradiction, uncertainty + inaccuracy.
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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 aleatory and epistemic uncertainty. In such situations, fuzzy set theory or probability theory or Dempster-Shafer theory (DSS) can be explored to represent uncertainty. If all the three types of uncertainty coexist how far computation of the risk is concern, two ways to deal with the situation either transform all the uncertainties to one type of format or need for joint propagation of uncertainties. Therefore, this article presents an effort to combine Probability distributions, fuzzy numbers (FNs) and DSS. Highlights of this study are: 1) The approaches presented here deal with the amalgamation of probability distributions where representations of parameters are of bell shaped fuzzy numbers (BFNs)/FNs; fuzzy numbers (FNs) of various types and shapes plus DSS with fuzzy focal elements of different types within the same framework; 2) Non-cancer human health risk assessment is carried out in this setting and 3) Risk values are obtained in the form of FNs at different fractiles. The techniques provided in this study are proficient to exploit in any mathematical models which represent real world problems, wherein model parameters are tainted with uncertainty where representations of uncertain model parameters are probability distributions with bell BFNs/FNs parameters; DSS with fuzzy focal elements of different types plus FNs with different shapes and types.
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) model structure is assumed to obtain the dynamic relation between the sEMG signal and the corresponding finger force signals. Subsequently, model based probabilities and fuzzy inference based probabilities are obtained for discrete sensor locations of a sEMG sensor array. Considering these evidences, a DST based motor point location identification method is proposed. The results based on one subject show the potential of the proposed theory and approach for affectively identifying motor point locations using an array sEMG sensor.
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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 that our method gives the significant and better result by comparing it to other methods.
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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 better performance.
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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.
Full textReports 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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