A methodology for structured ontology construction applied to intelligent transportation systems

The number of computers installed in urban and transport networks has grown tremendously in recent years, also the local processing capabilities and digital networking currently available. However, the heterogeneity of existing equipment in the field of ITS (Intelligent Transportation Systems) and the large volume of information they handle, greatly hinder the interoperability of the equipment and the design of cooperative applications between devices currently installed in urban networks. While the dynamic discovery of information, composition and invocation of services through intelligent agents are a potential solution to these problems, all these technologies require intelligent management of information flows. In particular, it is necessary to wean these information flows of the technologies used, enabling universal interoperability between computers, regardless of the context in which they are located. The main objective of this paper is to propose a systematic methodology to create ontologies, using methods such as a semantic clustering algorithms for retrieval and representation of information. Using the proposed methodology, an ontology will be developed in the ITS domain. This ontology will serve as the basis of semantic information to a SS (Semantic Service) that allows the connection of new equipment to an urban network. The SS uses the CORBA standard as distributed communication architecture

Data-stream driven Fuzzy-granular approaches for system maintenance

Intelligent systems are currently inherent to the society, supporting a synergistic human-machine collaboration. Beyond economical and climate factors, energy consumption is strongly affected by the performance of computing systems. The quality of software functioning may invalidate any improvement attempt. In addition, data-driven machine learning algorithms are the basis for human-centered applications, being their interpretability one of the most important features of computational systems. Software maintenance is a critical discipline to support automatic and life-long system operation. As most software registers its inner events by means of logs, log analysis is an approach to keep system operation. Logs are characterized as Big data assembled in large-flow streams, being unstructured, heterogeneous, imprecise, and uncertain. This thesis addresses fuzzy and neuro-granular methods to provide maintenance solutions applied to anomaly detection (AD) and log parsing (LP), dealing with data uncertainty, identifying ideal time periods for detailed software analyses. LP provides deeper semantics interpretation of the anomalous occurrences. The solutions evolve over time and are general-purpose, being highly applicable, scalable, and maintainable. Granular classification models, namely, Fuzzy set-Based evolving Model (FBeM), evolving Granular Neural Network (eGNN), and evolving Gaussian Fuzzy Classifier (eGFC), are compared considering the AD problem. The evolving Log Parsing (eLP) method is proposed to approach the automatic parsing applied to system logs. All the methods perform recursive mechanisms to create, update, merge, and delete information granules according with the data behavior. For the first time in the evolving intelligent systems literature, the proposed method, eLP, is able to process streams of words and sentences. Essentially, regarding to AD accuracy, FBeM achieved (85.64+-3.69)%; eGNN reached (96.17+-0.78)%; eGFC obtained (92.48+-1.21)%; and eLP reached (96.05+-1.04)%. Besides being competitive, eLP particularly generates a log grammar, and presents a higher level of model interpretability

Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

Structure and representation of ecological data to support knowledge discovery: A case study with bioacoustic data

Bird communities have long been surveyed as key indicators of ecosystem health and biodiversity. Adoption of Autonomous Recording Units (ARUs) to perform avian surveys has shifted the burden of species recognition from “birders” in the field, to “listeners” who review the ARU recordings at a later time. The number of recordings ARUs can produce has created a need to process large amounts of data. Although much research is devoted to fully automating the recognition process, expert humans are still required when entire bird communities must be identified. A framework for a Decision Support System (DSS) is presented which would assist listeners by suggesting likely species. A unique feature of the DSS is the consideration of the recording “context” of time, location and habitat as well as the bioacoustic features to match unknown vocalizations with reference species. In this thesis a data warehouse was built for an existing set of bioacoustic research data as a first–step to creating the DSS. The data set was from ARU deployments in the Lower Athabasca Region of Alberta, Canada. The Knowledge Discovery in Databases (KDD) and Dimensional Design Process protocols were used as guides to build a Kimball–style data warehouse. Data housed in the data warehouse included field data, data derived from GIS analysis, fuzzy logic memberships and symbolic representation of bioacoustic recording using the Piecewise Aggregate Approximation and Symbolic Aggregate approXimation (PAA/SAX). Examples of how missing and erroneous data were detected and processed are given. The sources of uncertainty inherent in ecological data are discussed and fuzzy logic is demonstrated as a soft–computing technique to accommodate this data. Data warehouses are commonly used for business applications but are very applicable for ecological data. As most instructions on building data warehouse are for business data, this thesis is offered as an example for ecologists interested in moving their data to a data warehouse. This thesis presents a case–study of how a data warehouse can be constructed for existing ecological data, whether as part of a DSS or a tool for viewing research data.Symbolic aggregate approximationBioacousticsDecision support systemData warehouseFuzzy logicBirdsAutonomous recording unitsPiecewise aggregate approximatio

Object detection, recognition and classification using computer vision and artificial intelligence approaches

Object detection and recognition has been used extensively in recent years to solve numerus challenges in different fields. Due to the vital roles they play, object detection and recognition has enabled quantum leaps in many industry fields by helping to overcome some serious challenges and obstacles. For example, worldwide security concerns have drawn the attention and stimulated the use of highly intelligent computer vision technology to provide security in different environments and in diverse terrains. In addition, some wildlife is at present exposed to danger and extinction worldwide. Therefore, early detection and recognition of potential threats to wildlife have become essential and timely. The extent of using computer vision and artificial intelligence to convert the seemingly insecure world to a more secure one has been widely accepted. Such technologies are used in monitoring, tracking, organising, analysing objects in a scene and for a number of other countless purposes. [Continues.

Characterization of unstructured video

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (p. 135-139).In this work, we examine video retrieval from a synthesis perspective in co-operation with the more common analysis perspective. Specifically, we target our algorithms for one particular domain- unstructured video material. The goal is to make this unstructured video available for manipulation in interesting ways. I.e, take video that may have been shot with no specific intent and use it in different settings. For example, we build a set of interfaces that will enable taking a collection of home videos and making Christmas cards, Refrigerator magnets, family dramas etc out of them. The work is divided into three parts. First, we study features and models for characterization of video. Examples are VideoBook with its extensions and Hidden Markov Models for video analysis. Secondly, we examine clustering as an approach for characterization of unstructured video. Clustering alleviates some of the common problems with "query-by- example" and presents groupings that rely on the user's abilities to make relevant connections. The clustering techniques we employ operate in the probability density space. One of our goals is to employ these techniques with sophisticated models such as Bayesian Networks and HMMs, which give similar descriptions. The clustering techniques we employ are shown to be optimal in an information theoretic and Gibbs Free Energy sense. Finally, we present a set of interfaces that use these features and groupings to enable browsing and editing of unstructured video content.by Giridharan Ranganathan Iyengar.Ph.D