Dissertation for the degree of Doctor of Science in Technology--Helsinki University of Technology, Espoo, 2002. / "ISSN 0781-5816." Includes bibliographical references (p. 65-71). Available online as a PDF file via the World Wide Web.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 188-210). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 188-210). Also available on the Internet.

Thesis (Ph. D.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Mechanical Engineering, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 1997. / Vita. Includes bibliographical references.

Thesis (M.Sc.)--Memorial University of Newfoundland, 1999. / Bibliography: leaves 109-120.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 121-126). Also available in print.

This thesis investigates a fuzzy syntactic approach to the interpretation of noisy two-dimensional images. This approach is based on a modification of the attributed graph grammar formalism to utilise fuzzy membership functions in the applicability predicates. As far as we are aware, this represents the first such modification of graph grammars. Furthermore, we develop a method for programming the resultant fuzzy attributed graph grammars through the use of non-deterministic control diagrams. To do this, we modify the standard programming mechanism to allow it to cope with the fuzzy certainty values associated with productions in our grammar. Our objective was to develop a flexible framework which can be used for the recognition of a wide variety of image classes, and which is adept at dealing with noise in these images. Programmed graph grammars are specifically chosen for the ease with which they allow one to specify a new two-dimensional image class. We implement a prototype system for Optical Music Recognition using our framework. This system allows us to test the capabilities of the framework for coping with noise in the context of handwritten music score recognition. Preliminary results from the prototype system show that the framework copes well with noisy images.

Risk management is an essential tool for ensuring the safety and timeliness of maritime operations and transportation. Some of the many risk factors that can compromise the smooth operation of maritime activities include harsh weather and pirate activity. However, identifying and quantifying the extent of these risk factors for a particular vessel is not a trivial process. One challenge is that processing the vast amounts of automatic identification system (AIS) messages generated by the ships requires significant computational resources. Another is that the risk management process partially relies on human expertise, which can be timeconsuming and error-prone. In this thesis, an existing Risk Management Framework (RMF) is augmented to address these issues. A parallel/distributed version of the RMF is developed to e ciently process large volumes of AIS data and assess the risk levels of the corresponding vessels in near-real-time. A genetic fuzzy system is added to the RMF's Risk Assessment module in order to automatically learn the fuzzy rule base governing the risk assessment process, thereby reducing the reliance on human domain experts. A new weather risk feature is proposed, and an existing regional hostility feature is extended to automatically learn about pirate activity by ingesting unstructured news articles and incident reports. Finally, a geovisualization tool is developed to display the position and risk levels of ships at sea. Together, these contributions pave the way towards truly automatic risk management, a crucial component of modern maritime solutions. The outcomes of this thesis will contribute to enhance Larus Technologies' Total::Insight, a risk-aware decision support system successfully deployed in maritime scenarios.

Granular computing facilitates dealing with information by providing a theoretical framework to deal with information as granules at different levels of granularity (different levels of specificity/abstraction). It aims to provide an abstract explainable description of the data by forming granules that represent the features or the underlying structure of corresponding subsets of the data. In this thesis, a granular computing approach to the design of intelligent classification systems is proposed. The proposed approach is employed for different classification systems to investigate its efficiency. Fuzzy inference systems, neural networks, neuro-fuzzy systems and classifier ensembles are considered to evaluate the efficiency of the proposed approach. Each of the considered systems is designed using the proposed approach and classification performance is evaluated and compared to that of the standard system. The proposed approach is based on constructing information granules from data at multiple levels of granularity. The granulation process is performed using a modified fuzzy c-means algorithm that takes classification problem into account. Clustering is followed by a coarsening process that involves merging small clusters into large ones to form a lower granularity level. The resulted granules are used to build each of the considered binary classifiers in different settings and approaches. Granules produced by the proposed granulation method are used to build a fuzzy classifier for each granulation level or set of levels. The performance of the classifiers is evaluated using real life data sets and measured by two classification performance measures: accuracy and area under receiver operating characteristic curve. Experimental results show that fuzzy systems constructed using the proposed method achieved better classification performance. In addition, the proposed approach is used for the design of neural network classifiers. Resulted granules from one or more granulation levels are used to train the classifiers at different levels of specificity/abstraction. Using this approach, the classification problem is broken down into the modelling of classification rules represented by the information granules resulting in more interpretable system. Experimental results show that neural network classifiers trained using the proposed approach have better classification performance for most of the data sets. In a similar manner, the proposed approach is used for the training of neuro-fuzzy systems resulting in similar improvement in classification performance. Lastly, neural networks built using the proposed approach are used to construct a classifier ensemble. Information granules are used to generate and train the base classifiers. The final ensemble output is produced by a weighted sum combiner. Based on the experimental results, the proposed approach has improved the classification performance of the base classifiers for most of the data sets. Furthermore, a genetic algorithm is used to determine the combiner weights automatically.