74 research outputs found
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
ïŹrst a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
Data modeling dealing with uncertainty in fuzzy logic
This paper shows models of data description that incorporate uncertainty like models of data extension EER, IFO among others. These database modeling tools are compared with the pattern FuzzyEER proposed by us, which is an extension of the EER model in order to manage uncertainty with fuzzy logic in fuzzy databases. Finally, a table shows the components of EER tool with the representation of all the revised models.The past and the future of information systems: 1976-2006 and beyondRed de Universidades con Carreras en InformĂĄtica (RedUNCI
Extending fuzzy semantic model by advanced decision rules
This paper extends FSM, a recently proposed semantic data model that supports fuzziness, imprecision and uncertainty of real-world. More precisely, the paper proposes four new concepts, decisional grouping, inhibition, multiplicity and selection, which allows enhancing the modeling of real-world applications. It integrates these concepts in FSM by the definition of new decision rules
Data modeling dealing with uncertainty in fuzzy logic
This paper shows models of data description that incorporate uncertainty like models of data extension EER, IFO among others. These database modeling tools are compared with the pattern FuzzyEER proposed by us, which is an extension of the EER model in order to manage uncertainty with fuzzy logic in fuzzy databases. Finally, a table shows the components of EER tool with the representation of all the revised models.The past and the future of information systems: 1976-2006 and beyondRed de Universidades con Carreras en InformĂĄtica (RedUNCI
Conceptual design and implementation of the fuzzy semantic model
FSM is one of few database models that support
fuzziness, uncertainty and impreciseness of real-world at the class
deïŹnition level. FSM authorizes an entity to be partially member
of its class according to a given degree of membership that reïŹects
the level to which the entity veriïŹes the extent properties of this
class. This paper deals with the conceptual design of FSM and
adresses some implementation issues.ou
Search for long-lived particles decaying to e± ÎŒâ Îœ
Long-lived particles decaying to e±ΌâÎœe±ΌâÎœ, with masses between 7 and 50GeV/c250GeV/c2 and lifetimes between 2 and 50ps50ps, are searched for by looking at displaced vertices containing electrons and muons of opposite charges. The search is performed using 5.4fbâ15.4fbâ1 of pp pp collisions collected with the LHCb detector at a centre-of-mass energy of sâ=13TeVs=13TeV. Three mechanisms of production of long-lived particles are considered: the direct pair production from quark interactions, the pair production from the decay of a Standard-Model-like Higgs boson with a mass of 125GeV/c2125GeV/c2, and the charged current production from an on-shell WW boson with an additional lepton. No evidence of these long-lived states is obtained and upper limits on the production cross-section times branching fraction are set on the different production modes
Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models
There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is âone-to-manyâ relationship between syntax and semantic of a proposition. This definition seems that it ignores âmany-to-manyâ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty.
This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic.
To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value.
Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine.
The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE.
Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data
Adaptive Cognitive Interaction Systems
Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthÀlt zahlreiche BeitrÀge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert
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