The purpose of this paper is to tackle semantic heterogeneity problem between land administration domain ontologies using name based ontology matching approach. The majority of ontology matching solutions use one or more string similarity measures to determine how similar two concepts are. Due to wide variety of available general purpose techniques it is not always clear which ones to use for a specific domain. The goal of this research is to evaluate several most applicable string similarity measures for use in land administration domain ontology matching. To support the research ontology matching tool prototype is developed, where the proposed algorithms are implemented. The practical results of ontology matching for State Land Service of Latvia are presented and analyzed. Matching of Land Administration Domain Model international standard, present Latvian Land Administration ontology is conducted.
The main goal of this paper is to collect information about pathfinding algorithms A*, BFS, Dijkstra's algorithm, HPA* and LPA*, and compare them on different criteria, including execution time and memory requirements. Work has two parts, the first being theoretical and the second practical. The theoretical part details the comparison of pathfinding algorithms. The practical part includes implementation of specific algorithms and series of experiments using algorithms implemented. Such factors as various size two dimensional grids and choice of heuristics were taken into account while conducting experiments.
<p class="R-AbstractKeywords"><span lang="EN-US">Ontology alignment, or ontology matching, is a technique to map different concepts between ontologies. For this purpose at least two ontologies are required. In certain scenarios, such as data integration, heterogeneous database integration and data model compatibility evaluation, a need to transform a relational database schema to an ontology can arise. </span></p><p class="R-AbstractKeywords"><span lang="EN-US">To conduct a successful transformation it is necessary to identify the differences between relational database schema and ontology information representation methods, and then to define transformation rules. The most straight forward but time consuming way to carry out transformation is to do it manually. Often this is not an option due to the size of data to be transformed. For this reason there is a need for an automated solution.</span></p><p class="R-AbstractKeywords"><span lang="EN-US">The automatic transformation of OWL ontology from relational database schema is presented in this paper; the data representation differences between relational database schema and OWL ontologies are described; the transformation rules are defined and the transformation tool’s prototype is developed to perform the described transformation.</span></p>
A system simulation is a one of the approaches to understand business processes or to explain them to other people. It is an excellent decision making solution to provide data-driven conclusions based on system modelling and experiments. This paper proposes simulation results of a school canteen. The aim of the research was to investigate the relation between a food waste amount and meal time duration. The proposed simulation was based on business process analysis, business process modelling, a Monte Carlo method and expert knowledge. The frequency distributions were constructed based on children meal duration observation completed by their mothers. It is a magnificent citizen science solution to involve mothers in the research because they can additionally better understand their children meal preferences and habits. Therefore, a questionnaire for citizens was developed, which can be applied to collect statistical data for model accuracy improvement and extension.
This manuscript describes urban objects segmentation using edge detection methods. The goal of this research was to compare an efficiency of edge detection methods for orthophoto and LiDAR data segmentation. The following edge detection methods were used: Sobel, Prewitt and Laplacian, with and without Gaussian kernel. The results have shown, that LiDAR data is better, because it does not contain shadows, which produce a noise.
Modern reviews of challenges related to deep learning application in agriculture mention restricted access to open datasets with high-resolution natural images taken in field conditions. Therefore, artificial intelligence solutions trained on these datasets containing low-resolution images and disease symptoms in the advanced stage are not suitable for early detection of plant diseases. The study aims to train a convolutional neural network for apple scab detection in an early stage of disease development. In this study a dataset was collected and used to develop a convolutional neural network based on the sliding-window method. The convolutional neural network was trained using the transfer-learning approach and MobileNetV2 architecture tuned on for embedded devices. The quality analysis in laboratory conditions showed the following accuracy results: F 1 score 0.96 and Cohen’s kappa 0.94; and the occlusion maps — correct classification features.
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