Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule.

Robert Gagne (1968) defined a learning hierarchy as a set of specified intellectual capabilities or intellectual skills. The capabilities in the hierarchy have an ordered relationship to each other and the hierarchy, as a whole, bears some relation to a plan for effective instruction. The hierarchy is built in a manner to reflect that a lower level skill must be acquired or mastered before an upper-level one, that is, lower level capabilities are prerequisites for upper level ones. Intellectual capabilities or skills are the nodes of the hierarchy. Gagne (1968) defines them as cognitive strategies that denote capabilities for action. Additionally, they also depict a learning route, a path, from simple skills to a final complex capability. Learning hierarchies not only serve to represent effective instruction plans in terms of skills or capabilities, but also, they serve as diagnosis instruments for providing individual or personalized remediation to students. However, for classrooms with a large number of students, the application of learning hierarchies for individualized (remedial) instruction is a highly time consuming task. Learning hierarchies belong to the behaviorist view on cognition and www.ijera.com

Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

We study the semantics of fuzzy if-then rules called fuzzy attribute implications parameterized by systems of isotone Galois connections. The rules express dependencies between fuzzy attributes in object-attribute incidence data. The proposed parameterizations are general and include as special cases the parameterizations by linguistic hedges used in earlier approaches. We formalize the general parameterizations, propose bivalent and graded notions of semantic entailment of fuzzy attribute implications, show their characterization in terms of least models and complete axiomatization, and provide characterization of bases of fuzzy attribute implications derived from data.

Concept map is a graphical tool for representing knowledge. They have been used in many different areas, including education, knowledge management, business and intelligence. Constructing of concept maps manually can be a complex task; an unskilled person may encounter difficulties in determining and positioning concepts relevant to the problem area. An application that recommends concept candidates and their position in a concept map can significantly help the user in that situation. This paper gives an overview of different approaches to automatic and semi-automatic creation of concept maps from textual and non-textual sources. The concept map mining process is defined, and one method suitable for the creation of concept maps from unstructured textual sources in highly inflected languages such as the Croatian language is described in detail. Proposed method uses statistical and data mining techniques enriched with linguistic tools. With minor adjustments, that method can also be used for concept map mining from textual sources in other morphologically rich languages.

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS. Use of Artificial Intelligence (AI) in environmental modelling has increased with recognition of its potential. In this paper we examine the DIFFERENT TECHNIQUES of Artificial intelligence with profound examples of human perception, learning and reasoning to solve complex problems. However with the increase of complexity better methods are required. Keeping in view of the above some researchers introduced the idea of hybrid mechanism in which two or more methods can be combined which seems to be a positive effort for creating a more complex; advanced and intelligent system which has the capability to in- cooperate human decisions thus driving the landscape changes.

In group decision making (GDM) problems fuzzy preference relations (FPR) are widely used for representing decision makers' opinions on the set of alternatives. In order to avoid misleading solutions, the study of consistency and consensus has become a very important aspect. This article presents a simulated annealing (SA) based soft computing approach to optimize the consistency/consensus level (CCL) of a complete fuzzy preference relation in order to solve a GDM problem. Consistency level indicates as expert's preference quality and consensus level measures the degree of agreement among experts' opinions. This study also suggests the set of experts for the necessary modifications in their prescribed preference structures without intervention of any moderator.

Probabilistic soft logic (PSL) is a probabilistic modeling framework which uses first-order logic and soft truth values in the interval[0;1] for reasoning in relational domains. PSL uses the Łukasiewicz t-norm and t-conorm from fuzzy logic to model respectively conjunction and disjunction. A PSL rule such as Trusts(A;X)^Trusts(X;B)->Trusts(A;B) models that “A trusts B” is true to the degree to which there is a trusted third party X. In the current version of PSL there is no way to express that A should trust B if most trusted friends of A trust B. In this work, we propose an extension of PSL with fuzzy quantifiers to address this limitation.

Identity relations are at the foundation of many logic-based knowledge representations. We argue that the traditional notion of equality, is unsuited for many realistic knowledge representation settings. The classical interpretation of equality is too strong when the equality statements are re-used outside their original context. On the Semantic Web, equality statements are used to interlink multiple descriptions of the same object, using owl:sameAs assertions. And indeed, many practical uses of owl:sameAs are known to violate the formal Leibniz-style semantics. We provide a more flexible semantics to identity by assigning meaning to the subrelations of an identity relation in terms of the predicates that are used in a knowledge-base. Using those indiscernability-predicates, we define upper and lower approximations of equality in the style of rought-set theory, resulting in a quality-measure for identity relations.

In this paper we present architecture of a fuzzy expert system used for therapy of dyslalic children. With fuzzy approach we can create a better model for speech therapist decisions. A software interface was developed for validation of the system. The main objectives of this task are: personalized therapy (the therapy must be in according with child's problems level, context and possibilities), speech therapist assistant (the expert system offer some suggestion regarding what exercises are better for a specific moment and from a specific child), (self) teaching (when system's conclusion is different that speech therapist's conclusion the last one must have the knowledge base change possibility). Keywords: fuzzy expert systems, speech therapy 1. Introduction In this article we refer to LOGOMON system developed in TERAPERS project by the authors.