Intersections between some families of (U,N)- and RU-implications

(U,N)-implications and RU-implications are the generalizations of (S,N)- and R-implications to the framework of uninorms, where the t-norms and t-conorms are replaced by appropriate uninorms. In this work, we present the intersections that exist between (U,N)-implications and the different families of RU-implications obtainable from the well-established families of uninorms

Implications in bounded systems

Abstract A consistent connective system generated by nilpotent operators is not necessarily isomorphic to Łukasiewicz-system. Using more than one generator function, consistent nilpotent connective systems (so-called bounded systems) can be obtained with the advantage of three naturally derived negations and thresholds. In this paper, implications in bounded systems are examined. Both R- and S-implications with respect to the three naturally derived negations of the bounded system are considered. It is shown that these implications never coincide in a bounded system, as the condition of coincidence is equivalent to the coincidence of the negations, which would lead to Łukasiewicz logic. The formulae and the basic properties of four different types of implications are given, two of which fulfill all the basic properties generally required for implications

Development of a probabilistic graphical structure from a model of mental health clinical expertise

This thesis explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. Probabilistic graphical structures can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty, domains such as the mental health domain. In this thesis the advantages that probabilistic graphical structures offer in representing such domains is built on. The Galatean Risk Screening Tool (GRiST) is a psychological model for mental health risk assessment based on fuzzy sets. In this thesis the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. This thesis describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise by the decomposing of the GRiST knowledge structure in component parts, which were in turned mapped into equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgement

Knowledge representation and diagnostic inference using Bayesian networks in the medical discourse

For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based reasoning. In addition, the use of Bayesian networks allows to deal with uncertainty, fuzziness and incompleteness. Thus, the valid general concepts can be issued according to their probability. To this end, various inference mechanisms are introduced and subsequently evaluated within the context of a developed prototype. Tests are employed to assess the classification of diagnoses by the network

Development of a probabilistic graphical structure from a model of mental health clinical expertise

This thesis explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. Probabilistic graphical structures can be a combination of graph and probability theory that provide numerous advantages when it comes to the representation of domains involving uncertainty, domains such as the mental health domain. In this thesis the advantages that probabilistic graphical structures offer in representing such domains is built on. The Galatean Risk Screening Tool (GRiST) is a psychological model for mental health risk assessment based on fuzzy sets. In this thesis the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. This thesis describes how a chain graph can be developed from the psychological model to provide a probabilistic evaluation of risk that complements the one generated by GRiST’s clinical expertise by the decomposing of the GRiST knowledge structure in component parts, which were in turned mapped into equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgementsEThOS - Electronic Theses Online ServiceGBUnited Kingdo

Εφαρμογή ασαφών συμπερασματικών μοντέλων στην διαγνωστική αξιολόγηση των μαθηματικών

Αξιολόγηση του μαθητή θεωρείται η συνεχής παιδαγωγική διαδικασία με την οποία παρακολουθείται η πορεία της μάθησης, προσδιορίζονται τα τελικά αποτελέσματά της και ο βαθμός επίτευξης των διδακτικών στόχων του μαθήματος και του προγράμματος σπουδών. Η διαγνωστική αξιολόγηση πραγματοποιείται στην αρχή του σχολικού έτους και βασικός σκοπός της είναι να προσδιοριστεί το γνωστικό επίπεδο των μαθητών, ώστε να προσαρμοστεί ανάλογα η διδασκαλία Στόχος της εργασίας αυτής η κατασκευή ενός ασαφούς (Fuzzy) «διαγνωστικού» μοντέλου της γνωστικής ικανότητας ενός μαθητή που ξεκινάει την Α’ τάξη του ενιαίου Λυκείου. . Η υπολογιστική εφαρμογή του συστήματος έγινε με την χρήση του Fuzzy Logic Toolbox το οποίο είναι μια συλλογή συναρτήσεων κατασκευασμένων στο αριθμητικό υπολογιστικό περιβάλλον του MatLab