Vagueness and Roughness

The paper proposes a new formal approach to vagueness and vague sets taking inspirations from Pawlak’s rough set theory. Following a brief introduction to the problem of vagueness, an approach to conceptualization and representation of vague knowledge is presented from a number of different perspectives: those of logic, set theory, algebra, and computer science. The central notion of the vague set, in relation to the rough set, is defined as a family of sets approximated by the so called lower and upper limits. The family is simultaneously considered as a family of all denotations of sharp terms representing a suitable vague term, from the agent’s point of view. Some algebraic operations on vague sets and their properties are defined. Some important conditions concerning the membership relation for vague sets, in connection to Blizard’s multisets and Zadeh’s fuzzy sets, are established as well. A classical outlook on a logic of vague sentences (vague logic) based on vague sets is also discussed

Romanian Language Technology — a view from an academic perspective

The article reports on research and developments pursued by the Research Institute for Artificial Intelligence "Mihai Draganescu" of the Romanian Academy in order to narrow the gaps identified by the deep analysis on the European languages made by Meta-Net white papers and published by Springer in 2012. Except English, all the European languages needed significant research and development in order to reach an adequate technological level, in line with the expectations and requirements of the knowledge society

The Fuzzification of Classical Structures: A General View

The aim of this survey article, dedicated to the 50th anniversary of Zadeh’s pioneering paper "Fuzzy Sets" (1965), is to offer a unitary view to some important spaces in fuzzy mathematics: fuzzy real line, fuzzy topological spaces, fuzzy metric spaces, fuzzy topological vector spaces, fuzzy normed linear spaces. We believe that this paper will be a support for future research in this field

Dynamic fuzzy multiple criteria decision making for performance evaluation

The paper proposes a dynamic fuzzy multiple criteria decision making (DFMCDM) method. The method considers the integrated weight of the decision makers with the subjective and objective preference and the effect of time weight. In the proposed method, a mathematical programming model is used to determine the integrated weight, and a basic unit-interval monotonic (BUM) function based approach is used to calculate the time weight. In addition, a distance measure of membership function is introduced to effectively measure the degree of difference between the alternatives in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). Finally, a numerical example is introduced to illustrate the proposed method

Handling a large number of preferences in a multi-level decision-making process

The complexity of a decision is related to the number of persons that are involved, as well as to the diversity of their preferences based on their knowledge, experience or area of expertise. Consequently, it is a challenge to adequately handle a large number of heterogeneous preferences considering that all the participants are considered to be an important source of information to make better motivated decisions. Addressing this challenge constitutes the main motivation in this dissertation because these days decision makers seem to be increasingly interested in the opinions (or preferences) given by persons around a community (and sometimes around the world) through different sources including social media channels. This PhD study provides a set of tools that helps a decision maker to make better motivated decisions by a proper handling of a large number of preferences, identifying and evaluating relevant preferences and handling multiple perspectives. Herein, by 'preference' is meant a greater interest expressed by an individual for a particular alternative over others; by 'relevant' is meant a variety of preferences which are significant (or important) to a particular person acting as a decision maker; and by 'perspective' is understood a position (e.g., social, technical, financial or environmental) adopted by a decision maker when expressing his/ her preferences or constraints

Mining fuzzy association rules in large databases with quantitative attributes.

by Kuok, Chan Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (leaves 74-77).Abstract --- p.iAcknowledgments --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.2Chapter 1.2 --- Association Rule Mining --- p.3Chapter 2 --- Background --- p.6Chapter 2.1 --- Framework of Association Rule Mining --- p.6Chapter 2.1.1 --- Large Itemsets --- p.6Chapter 2.1.2 --- Association Rules --- p.8Chapter 2.2 --- Association Rule Algorithms For Binary Attributes --- p.11Chapter 2.2.1 --- AIS --- p.12Chapter 2.2.2 --- SETM --- p.13Chapter 2.2.3 --- "Apriori, AprioriTid and AprioriHybrid" --- p.15Chapter 2.2.4 --- PARTITION --- p.18Chapter 2.3 --- Association Rule Algorithms For Numeric Attributes --- p.20Chapter 2.3.1 --- Quantitative Association Rules --- p.20Chapter 2.3.2 --- Optimized Association Rules --- p.23Chapter 3 --- Problem Definition --- p.25Chapter 3.1 --- Handling Quantitative Attributes --- p.25Chapter 3.1.1 --- Discrete intervals --- p.26Chapter 3.1.2 --- Overlapped intervals --- p.27Chapter 3.1.3 --- Fuzzy sets --- p.28Chapter 3.2 --- Fuzzy association rule --- p.31Chapter 3.3 --- Significance factor --- p.32Chapter 3.4 --- Certainty factor --- p.36Chapter 3.4.1 --- Using significance --- p.37Chapter 3.4.2 --- Using correlation --- p.38Chapter 3.4.3 --- Significance vs. Correlation --- p.42Chapter 4 --- Steps For Mining Fuzzy Association Rules --- p.43Chapter 4.1 --- Candidate itemsets generation --- p.44Chapter 4.1.1 --- Candidate 1-Itemsets --- p.45Chapter 4.1.2 --- Candidate k-Itemsets (k > 1) --- p.47Chapter 4.2 --- Large itemsets generation --- p.48Chapter 4.3 --- Fuzzy association rules generation --- p.49Chapter 5 --- Experimental Results --- p.51Chapter 5.1 --- Experiment One --- p.51Chapter 5.2 --- Experiment Two --- p.53Chapter 5.3 --- Experiment Three --- p.54Chapter 5.4 --- Experiment Four --- p.56Chapter 5.5 --- Experiment Five --- p.58Chapter 5.5.1 --- Number of Itemsets --- p.58Chapter 5.5.2 --- Number of Rules --- p.60Chapter 5.6 --- Experiment Six --- p.61Chapter 5.6.1 --- Varying Significance Threshold --- p.62Chapter 5.6.2 --- Varying Membership Threshold --- p.62Chapter 5.6.3 --- Varying Confidence Threshold --- p.63Chapter 6 --- Discussions --- p.65Chapter 6.1 --- User guidance --- p.65Chapter 6.2 --- Rule understanding --- p.67Chapter 6.3 --- Number of rules --- p.68Chapter 7 --- Conclusions and Future Works --- p.70Bibliography --- p.7

Some Heronian mean operators with 2-tuple linguistic information and their application to multiple attribute group decision making

With respect to multi-attribute group decision-making problems, in which attribute values take the form of 2-tuple linguistic information, a new decision making method that considers the interrelationships of attribute values is proposed. Firstly, some new aggregation operators of 2-tuple linguistic information based on Heronian mean are proposed, such as 2-tuple linguistic Heronian mean operator (2TLHM) and 2-tuple linguistic weighted Heronian mean operator (2TLWHB), and some desired properties of the proposed operators are studied. Then, a method based on the 2TLHM and 2TLWHB operators for multiple attribute group decision making is developed. In this approach, the interrelationships of attribute values are considered. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles

Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has accelerated the growth of the Facility Management industry and its roles are broadening to encompass more responsibilities and skill sets. FM budgets and teams are becoming larger and more impactful as new technological trends are incorporated into data-driven strategies. This new scenario has motivated institutions such as the European Central Bank to initiate projects aimed at optimising the use of data to improve the monitoring, control and preservation of the assets that enable the continuity of the Bank's activities. Such projects make it possible to reduce costs, plan, manage and allocate resources, reinforce the control, and efficiency of safety and operational systems. To support the long-term maintenance strategy being developed by the Technical Facility Management section of the ECB, this thesis proposes a model to calculate the Left wear margin of the equipment. This is accomplished through the development of an algorithm based on a fuzzy logic system that uses Python language and presents the system's structure, its reliability, feasibility, potential, and limitations. For Facility Management, this project constitutes a cornerstone of the ongoing digital transformation program