2,905 research outputs found

    Disaster Management

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    The study deals with semi automatic extraction of urban risk related base data and their different generic aspects. Emphasis is given to the building footprint map which is a major base data. The main objective of the study is to extract Building Footprints from High Resolution Imagery using a semi automated approach. In this context the research mainly focuses on developing an integrated extraction to generate the risk related base data in an urban area from high resolution remote sensing images. A multi scale object oriented fuzzy classification of various urban settings was carried out. The method was applied in Dehradun, Uttaranchal, India. The city lies in the high seismic risk zone, also experiencing rapid urbanization due to its newly attained status of a state capital. The extracted base data maps were empirically evaluated by comparing them with visually interpreted reference maps. The evaluation of the extracted base data was carried out by both the quantitative and quality assessment techniques. It was observed that the building footprints extracted from fused Ikonos (PAN+XS) image gave acceptable accuracy for providing better management and better preparedness for any future disasters. Though there are compound problems associated with extraction of information from high resolution images, it is demonstrated from the study that such extraction techniques can be used and improved upo

    Relational data clustering algorithms with biomedical applications

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    LearnFCA: A Fuzzy FCA and Probability Based Approach for Learning and Classification

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    Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering. This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide a literature review of it’s applications and various approaches adopted by researchers in the areas of dataanalysis, knowledge management with emphasis to data-learning and classification problems. We propose LearnFCA, a novel approach based on FuzzyFCA and probability theory for learning and classification problems. LearnFCA uses an enhanced version of FuzzyLattice which has been developed to store class labels and probability vectors and has the capability to be used for classifying instances with encoded and unlabelled features. We evaluate LearnFCA on encodings from three datasets - mnist, omniglot and cancer images with interesting results and varying degrees of success. Adviser: Dr Jitender Deogu

    Industrial Applications: New Solutions for the New Era

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    This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

    New Fundamental Technologies in Data Mining

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    The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
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