Yes / Risk assessment methods have been widely used in various industries, and they play a significant role in improving the safety performance of systems. However, the outcomes of risk assessment approaches are subject to uncertainty and ambiguity due to the complexity and variability of system behaviour, scarcity of quantitative data about different system parameters, and human involvement in the analysis, operation, and decision-making processes. The implications for improving system safety are slowly being recognised; however, research on uncertainty handling during both qualitative and quantitative risk assessment procedures is a growing field. This paper presents a review of the state of the art in this field, focusing on uncertainty handling in fault tree analysis (FTA) based risk assessment. Theoretical contributions, aleatory uncertainty, epistemic uncertainty, and integration of both epistemic and aleatory uncertainty handling in the scientific and technical literature are carefully reviewed. The emphasis is on highlighting how assessors can handle uncertainty based on the available evidence as an input to FTA.

Yes / Critical technological systems exhibit complex dynamic characteristics such as time-dependent behavior, functional dependencies among events, sequencing and priority of causes that may alter the effects of failure. Dynamic fault trees (DFTs) have been used in the past to model the failure logic of such systems, but the quantitative analysis of DFTs has assumed the existence of precise failure data and statistical independence among events, which are unrealistic assumptions. In this paper, we propose an improved approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and stochastic dependencies among events. In the proposed framework, DFTs are used for dynamic failure modeling. Quantitative evaluation of DFTs is performed by converting them into generalized stochastic Petri nets. When failure data are unavailable, expert judgment and fuzzy set theory are used to obtain reasonable estimates. The approach is demonstrated on a simplified model of a cardiac assist system. / DEIS H2020 Project under Grant 732242.

Yes / Fault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques.

Yes / Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.

Yes / Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines intuitionistic fuzzy set theory and expert elicitation to enable quantitative analysis of TFTs of dynamic systems with uncertain data. Experts’ opinions are taken into account to compute the failure probability of the basic events of the TFT as intuitionistic fuzzy numbers. Subsequently, for the algebraic approach, the intuitionistic fuzzy operators for the logic gates of TFT are defined to quantify the TFT. On the other hand, for the quantification of TFTs via PN and BN-based approaches, the intuitionistic fuzzy numbers are defuzzified to be used in these approaches. As a result, the framework can be used with all the currently available TFT analysis approaches. The effectiveness of the proposed framework is illustrated via application to a practical system and through a comparison of the results of each approach. / This work was supported in part by the Mobile IOT: Location Aware project (grant no. MMUE/180025) and Indoor Internet of Things (IOT) Tracking Algorithm Development based on Radio Signal Characterisation project (grant no. FRGS/1/2018/TK08/MMU/02/1). This research also received partial support from DEIS H2020 project (grant no. 732242).

No / In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with cal- culating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainabil- ity score, return, and risk of the portfolio, subject to several constraints. The ε-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach. / Institution of Eminence, University of Delhi, Delhi-110007 under Faculty Research Program / The full-text of this article will be released for public view at the end of the publisher embargo on 16 Jul 2024.

Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a "Not" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.

The GUHA method is one of the oldest methods of exploratory data analysis, which is regarded as part of the data mining or knowledge discovery in databases (KDD) scienti_c area. Unlike many other methods of data mining, the GUHA method has firm theoretical foundations in logic and statistics. In scope of the method, finding interesting knowledge corresponds to finding special formulas in satisfactory rich logical calculus, which is called observational calculus. The main topic of the thesis is application of the "fuzzy paradigm" to the GUHA method By the term "fuzzy paradigm" we mean approaches that use many-valued membership degrees or truth values, namely fuzzy set theory and fuzzy logic. The thesis does not aim to cover all the aspects of this application, it emphasises mainly on: - Association rules as the most prevalent type of formulas mined by the GUHA method - Usage of fuzzy data - Logical aspects of fuzzy association rules mining - Comparison of the GUHA theory to the mainstream fuzzy association rules - Implementation of the theory using the bit string approach The thesis throughoutly elaborates the theory of fuzzy association rules, both using the theoretical apparatus of fuzzy set theory and fuzzy logic. Fuzzy set theory is used mainly to compare the GUHA method to existing mainstream approaches to formalize fuzzy association rules, which were studied in detail. Fuzzy logic is used to define novel class of logical calculi called logical calculi of fuzzy association rules (LCFAR) for logical representation of fuzzy association rules. The problem of existence of deduction rules in LCFAR is dealt in depth. Suitable part of the proposed theory is implemented in the Ferda system using the bit string approach. In the approach, characteristics of examined objects are represented as strings of bits, which in the crisp case enables efficient computation. In order to maintain this feature also in the fuzzy case, a profound low level testing of data structures and algoritms for fuzzy bit strings have been carried out as a part of the thesis.

Dans ces travaux de thèse nous proposons de tirer parti de la théorie des ensembles flous afin d'améliorer les interactions entre les systèmes de bases de données et les utilisateurs. Les mécanismes coopératifs visent à aider les utilisateurs à mieux interagir avec les SGBD. Ces mécanismes doivent faire preuve de robustesse : ils doivent toujours pouvoir proposer des réponses à l'utilisateur. Empty set (0,00 sec) est un exemple typique de réponse qu'il serait désirable d'éradiquer. Le caractère informatif des explications de réponses est parfois plus important que les réponses elles-mêmes : ce peut être le cas avec les réponses vides et pléthoriques par exemple, d'où l'intérêt de mécanismes coopératifs robustes, capables à la fois de contribuer à l'explication ainsi qu'à l'amélioration des résultats. Par ailleurs, l'utilisation de termes de la langue naturelle pour décrire les données permet de garantir l'interprétabilité des explications fournies. Permettre à l'utilisateur d'utiliser des mots de son propre vocabulaire contribue à la personnalisation des explications et améliore l'interprétabilité. Nous proposons de nous intéresser aux explications dans le contexte des réponses coopératives sous trois angles : 1) dans le cas d'un ensemble pléthorique de résultats ; 2) dans le contexte des systèmes de recommandation ; 3) dans le cas d'une recherche à partir d'exemples. Ces axes définissent des approches coopératives où l'intérêt des explications est de permettre à l'utilisateur de comprendre comment sont calculés les résultats proposés dans un effort de transparence. Le caractère informatif des explications apporte une valeur ajoutée aux résultats bruts, et forme une réponse coopérative. / In this thesis, we are interested in how we can leverage fuzzy logic to improve the interactions between relational database systems and humans. Cooperative answering techniques aim to help users harness the potential of DBMSs. These techniques are expected to be robust and always provide answer to users. Empty set (0,00 sec) is a typical example of answer that one may wish to never obtain. The informative nature of explanations is higher than that of actual answers in several cases, e.g. empty answer sets and plethoric answer sets, hence the interest of robust cooperative answering techniques capable of both explaining and improving an answer set. Using terms from natural language to describe data --- with labels from fuzzy vocabularies --- contributes to the interpretability of explanations. Offering to define and refine vocabulary terms increases the personalization experience and improves the interpretability by using the user's own words. We propose to investigate the use of explanations in a cooperative answering setting using three research axes: 1) in the presence of a plethoric set of answers; 2) in the context of recommendations; 3) in the context of a query/answering problem. These axes define cooperative techniques where the interest of explanations is to enable users to understand how results are computed in an effort of transparency. The informativeness of the explanations brings an added value to the direct results, and that in itself represents a cooperative answer.

Concept Maps (CMs) are considered a well-known pedagogy technique in creating curriculum, educating, teaching, and learning. Determining comprehension of concepts result from comparisons of candidate CMs against a master CM, and evaluate "goodness". Past techniques for comparing CMs have revolved around the creation of a subjective rubric. We propose a novel CM scoring scheme called MAnanA based on a Fuzzy Similarity Scaling (FSS) score to vastly remove the subjectivity of the rubrics in the process of grading a CM. We evaluate our framework against a predefined rubric and test it with CM data collected from the Introduction to Computer Security course at the University of New Orleans (UNO), and found that the scores obtained via MAnanA captured the trend that we observed from the rubric via peak matching. Based on our evaluation, we believe that our framework can be used to objectify CM analysis.