Virtual Reality-Based Fuzzy Spatial Relation Knowledge Extraction Method for Observer-Centered Vague Location Descriptions

Many documents contain vague location descriptions of observed objects. To represent location information in geographic information systems (GISs), these vague location descriptions need to be transformed into representable fuzzy spatial regions, and knowledge about the location descriptions of observer-to-object spatial relations must serve as the basis for this transformation process. However, a location description from the observer perspective is not a specific fuzzy function, but comes from a subjective viewpoint, which will be different for different individuals, making the corresponding knowledge difficult to represent or obtain. To extract spatial knowledge from such subjective descriptions, this research proposes a virtual reality (VR)-based fuzzy spatial relation knowledge extraction method for observer-centered vague location descriptions (VR-FSRKE). In VR-FSRKE, a VR scene is constructed, and users can interactively determine the fuzzy region corresponding to a location description under the simulated VR observer perspective. Then, a spatial region clustering mechanism is established to summarize the fuzzy regions identified by various individuals into fuzzy spatial relation knowledge. Experiments show that, on the basis of interactive scenes provided through VR, VR-FSRKE can efficiently extract spatial relation knowledge from many individuals and is not restricted by requirements of a certain place or time; furthermore, the knowledge obtained by VR-FSRKE is close to the knowledge obtained from a real scene.

Image Segmentation of Brain MRI Based on LTriDP and Superpixels of Improved SLIC

Non-uniform gray distribution and blurred edges often result in bias during the superpixel segmentation of medical images of magnetic resonance imaging (MRI). To this end, we propose a novel superpixel segmentation algorithm by integrating texture features and improved simple linear iterative clustering (SLIC). First, a 3D histogram reconstruction model is used to reconstruct the input image, which is further enhanced by gamma transformation. Next, the local tri-directional pattern descriptor is used to extract texture features of the image; this is followed by an improved SLIC superpixel segmentation. Finally, a novel clustering-center updating rule is proposed, using pixels with gray difference with original clustering centers smaller than a predefined threshold. The experiments on the Whole Brain Atlas (WBA) image database showed that, compared to existing state-of-the-art methods, our superpixel segmentation algorithm generated significantly more uniform superpixels, and demonstrated the performance accuracy of the superpixel segmentation in both fuzzy boundaries and fuzzy regions.

Modulation of Measles Virus NTAIL Interactions through Fuzziness and Sequence Features of Disordered Binding Sites

In this paper we review our recent findings on the different interaction mechanisms of the C-terminal domain of the nucleoprotein (N) of measles virus (MeV) NTAIL, a model viral intrinsically disordered protein (IDP), with two of its known binding partners, i.e., the C-terminal X domain of the phosphoprotein of MeV XD (a globular viral protein) and the heat-shock protein 70 hsp70 (a globular cellular protein). The NTAIL binds both XD and hsp70 via a molecular recognition element (MoRE) that is flanked by two fuzzy regions. The long (85 residues) N-terminal fuzzy region is a natural dampener of the interaction with both XD and hsp70. In the case of binding to XD, the N-terminal fuzzy appendage of NTAIL reduces the rate of α-helical folding of the MoRE. The dampening effect of the fuzzy appendage on XD and hsp70 binding depends on the length and fuzziness of the N-terminal region. Despite this similarity, NTAIL binding to XD and hsp70 appears to rely on completely different requirements. Almost any mutation within the MoRE decreases XD binding, whereas many of them increase the binding to hsp70. In addition, XD binding is very sensitive to the α-helical state of the MoRE, whereas hsp70 is not. Thus, contrary to hsp70, XD binding appears to be strictly dependent on the wild-type primary and secondary structure of the MoRE.

An outcome-based process optimization model using fuzzy-based association rules

Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. Findings The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. Research limitations/implications The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Originality/value Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.