Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures

In historical district at European cities it is a major problem how to take decision on renovating or replacing existing buildings. This problem is imminent in Budapest (Hungary) in many traditional districts such as the Ferencváros district where we selected a compound area for further examination. By financial aid for the renovation of these buildings which awarded by Municipal Assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. In this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of L-fuzzy object). Using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. Linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. Such linguistic calculations might be of help for the Municipal Assembly awarding financial support. A complete example wit 26 buildings is presented

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches

A Critical Evaluation of Remote Sensing Based Land Cover Mapping Methodologies

A novel, disaggregated approach to land cover survey is developed on the basis of land cover attributes; the parameters typically used to delineate land cover classes. The recording of land cover attributes, via objective measurement techniques, is advocated as it eliminates the requirement for surveyors to delineate and classify land cover; a process proven to be subjective and error prone. Within the North York Moors National Park, a field methodology is developed to characterise five attributes: species composition, cover, height, structure and density. The utility of land cover attributes to act as land cover ‘building blocks’ is demonstrated via classification of the field data to the Monitoring Landscape Change in the National Parks (MLCNP), National Land Use Database (NLUD) and Phase 1 Habitat Mapping (P1) schemes. Integration of the classified field data and a SPOT5 satellite image is demonstrated within per-pixel and object-orientated classification environments. Per-pixel classification produced overall accuracies of 81%, 80% and 76% at the field samples for the MLCNP, NLUD and P1 schemes, respectively. However, independent validation produced significantly lower accuracies. These decreases are demonstrated to be a function of sample fraction. Object-orientated classification, exemplified for the MLCNP schema at 3 segmentation scales, achieved accuracies approaching 75%. The aggregation of attributes to classes underutilises the potential of the remotely sensed data to describe landscape variability. Consequently, classification and geostatistical techniques capable of land cover attribute parameterisation, across the study area, are reviewed and exemplified for a sub-pixel classification. Land cover attributes provide a flexible source of field data which has been proven to support multiple land cover classification schemes and classification scales (sub-pixel, pixel and object). This multi-scaled/schemed approach enables the differential treatment of regions, within the remote sensing image, as a function of landscape characteristics and the users’ requirements providing a flexible mapping solution

Data Management for Dynamic Multimedia Analytics and Retrieval

Multimedia data in its various manifestations poses a unique challenge from a data storage and data management perspective, especially if search, analysis and analytics in large data corpora is considered. The inherently unstructured nature of the data itself and the curse of dimensionality that afflicts the representations we typically work with in its stead are cause for a broad range of issues that require sophisticated solutions at different levels. This has given rise to a huge corpus of research that puts focus on techniques that allow for effective and efficient multimedia search and exploration. Many of these contributions have led to an array of purpose-built, multimedia search systems. However, recent progress in multimedia analytics and interactive multimedia retrieval, has demonstrated that several of the assumptions usually made for such multimedia search workloads do not hold once a session has a human user in the loop. Firstly, many of the required query operations cannot be expressed by mere similarity search and since the concrete requirement cannot always be anticipated, one needs a flexible and adaptable data management and query framework. Secondly, the widespread notion of staticity of data collections does not hold if one considers analytics workloads, whose purpose is to produce and store new insights and information. And finally, it is impossible even for an expert user to specify exactly how a data management system should produce and arrive at the desired outcomes of the potentially many different queries. Guided by these shortcomings and motivated by the fact that similar questions have once been answered for structured data in classical database research, this Thesis presents three contributions that seek to mitigate the aforementioned issues. We present a query model that generalises the notion of proximity-based query operations and formalises the connection between those queries and high-dimensional indexing. We complement this by a cost-model that makes the often implicit trade-off between query execution speed and results quality transparent to the system and the user. And we describe a model for the transactional and durable maintenance of high-dimensional index structures. All contributions are implemented in the open-source multimedia database system Cottontail DB, on top of which we present an evaluation that demonstrates the effectiveness of the proposed models. We conclude by discussing avenues for future research in the quest for converging the fields of databases on the one hand and (interactive) multimedia retrieval and analytics on the other

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems