Browsing by Subject "data science"

Now showing 1 - 6 of 6

Access status: Open Access ,
A Python library for the Jupyteo IDE Earth observation processing tool enabling interoperability with the QGIS System for use in data science
(Wydawnictwa AGH, 2022) Bednarczyk, Michał
This paper describes JupyQgis - a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.
Access status: Open Access ,
Analityka kulturowa, czyli jak narzędzia »data science« zmieniły humanistykę
(Wydawnictwa AGH, 2022) Radomski, Andrzej
The article presents research paradigms that have radically changed the contemporary humanities. The most important of these is cultural analytics. It is based on Data Science methods. The author presents the assumptions of data science, and then the characteristics of digital humanities and cultural analytics. The second part of the article presents examples of research and projects conducted as part of cultural analysis. These are projects implemented at the DH Lab at Yale University, Software Studies Initiative, and Media Lab Katowice. Research conducted in these institutions transformed the humanities. Its characteristic features are the study of large data collections, research automation, the use of machine learning and knowledge visualization. The new humanities, the author claims, has become an exact science.
Access status: Restricted ,
Aplikacja do zdalnej obsługi instrumentów i automatyzacji pomiarów mikrofalowych
(Data obrony: 2020-09-07) Piekarz, Blanka
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Automatic failure detection methods for rotating machinery in the ThingSpeak environment
(Data obrony: 2021-01-28) Szczybura, Filip
Wydział Inżynierii Mechanicznej i Robotyki
Access status: Restricted ,
Implementation of associative neural graphs using parallel GPGPU operations.
(Data obrony: 2021-01-26) Szwast, Hubert
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Sparse data classifier based on first-past-the-post voting system
(Wydawnictwa AGH, 2022) Cudak, Magdalena; Piech, Mateusz; Marcjan, Robert
A point of interest (POI) is a general term for objects that describe places from the real world. The concept of POI matching (i.e., determining whether two sets of attributes represent the same location) is not a trivial challenge due to the large variety of data sources. The representations of POIs may vary depending on the basis of how they are stored. A manual comparison of objects is not achievable in real time, therefore, there are multiple solutions for automatic merging. However, there is no yet the efficient solution solves the missing of the attributes. In this paper, we propose a multi-layered hybrid classifier that is composed of machine-learning and deep-learning techniques and supported by a first-past-the-post voting system. We examined different weights for the constituencies that were taken into consideration during a majority (or supermajority) decision. As a result, we achieved slightly higher accuracy than the best current model (random forest), which also is based on voting.