Browsing by Subject "classification"

Now showing 1 - 20 of 29

Access status: Open Access ,
A brief review of recent developments in the integration of deep learning with GIS
(Wydawnictwa AGH, 2022) Mohan, Shyama; Giridhar, M.V.S.S.
The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.
Access status: Open Access ,
A hybrid statistical approach for texture images classification based on scale invariant features and mixture gamma distribution
(2020) Benlakhdar, Said; Rziza, Mohammed; Oulad Haj Thami, Rachid
Image classification refers to an important process in computer vision. The purpose of this paper is to propose a novel approach named GGD-GMM and based on statistical modeling in the wavelet domain to describe textured images and rely on a number of principles that give its internal coherence and originality. Firstly, we propose arobust algorithm based on the combination of the wavelet transform and Scale Invariant Feature Transform. Secondly, we implement the aforementioned algorithm and fit the result using the finite mixture gamma distribution (GMM). The results, obtained for two benchmark datasets show that the proposed algorithm has a good relevance as it provides higher classification accuracy than some other well-known models (Kohavi, 1995). Moreover, it shows other advantages relied upon Noise-resistant and rotation invariant.
Access status: Open Access ,
An empirical analysis of changes in the Błędów Desert using machine learning methods
(Wydawnictwa AGH, 2025) Czernik, Anna; Borowiec, Natalia; Marmol, Urszula
The aim of the study was to determine changes in the land cover of the Błędów Desert, which is a habitat for rare flora and fauna species protected under the Natura 2000 program. Invasive plants, which pose a threat to protected species, are present in this area. Additionally, human activities can have negative impacts on the desert ecosystem. Therefore, the land manager is obligated to carry out actions aimed at maintaining the appropriate size and character of the desert. The analysis was conducted using satellite imagery from the Sentinel-2 mission, which provides images with high temporal and spatial resolution. The study covered the years 2015–2022 and took into account seasonal variability due to the presence of green vegetation. Change detection methods based on data integration, including photointerpretation and machine learning classification, were used for land cover analysis. Five representative land cover classes were defined, enabling a quantitative presentation of changes in the Błędów Desert and a qualitative assessment of the classification performed. The results of the study indicate variability in land cover depending on the season, with an increasing number of protected plant species, including grasslands. Simultaneously, a slight increase in the desert area was noted, manifesting as an increase in sand in forested areas. The results obtained demonstrate the effective implementation of the Natura 2000 program objectives.
Access status: Restricted ,
Application of deep learning approach for identification and classification of scale defects during hot forming process
(Data obrony: 2019-09-19) Furman, Szymon
Wydział Inżynierii Metali i Informatyki Przemysłowej
Access status: Open Access ,
Approach to classifying data with highly localized unmarked features using neural networks
(Wydawnictwa AGH, 2019) Grzeszczuk, Rafał
To face the increasing demand of quality healthcare, cutting-edge automation technology is being applied in demanding areas such as medical imaging. This paper proposes a novel approach to classification problems on datasets with sparse highly localized features. It is based on the use of a saliency map in the amplification of features. Unlike previous efforts, this approach does not use any prior information about feature localization. We present an experimental study based on the Diabetic Retinopathy classification problem, in which our method has shown to achieve an over 20%-higher accuracy in solving a two-class Diabetic Retinopathy classification problem than a naive approach based solely on residual neural networks. The dataset consists of 35,120 images of various qualities, inconsistent resolutions, and aspect ratios.
Access status: Restricted ,
Automatyczne rozpoznawanie tablic rejestracyjnych
(Data obrony: 2017-01-19) Zygmunt, Michał
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Classification of traffic over collaborative IoT/cloud platforms using deep-learning recurrent LSTM
(Wydawnictwa AGH, 2021) Patil, Sonali A.; Raj, Arun L.
The Internet of Things (IoT) and cloud-based collaborative platforms have emerged as new infrastructures over the recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT/cloud-based collaborative platforms for optimally utilizing channel capac ity for transmitting benign traffic and blocking malicious traffic. The traffic classification mechanism should be dynamic and capable enough for classifying network traffic in a quick manner so that malevolent traffic can be identified at earlier stages and benign traffic can be speedily channelized to the destined nodes. In this paper, we present a deep-learning recurrent LSTM RNet-based technique for classifying traffic over IoT/cloud platforms using the Word2Vec approach. Machine-learning techniques (MLTs) have also been employed for comparing the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is clas sified into three classes: Tor-Normal, NonTor-Normal, and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet accurately classifies such traffic and also helps reduce network latency as well as enhance data transmission rates and network throughput.
Access status: Open Access ,
Document controversy classification based on the Wikipedia category structure
(Wydawnictwa AGH, 2015) Jankowski-Lorek, Michał; Zieliński, Kazimierz
Dispute and controversy are parts of our culture and cannot be omitted on the Internet (where it becomes more anonymous). There have been many studies on controversy, especially on social networks such as Wikipedia. This free on-line encyclopedia has become a very popular data source among many researchers studying behavior or natural language processing. This paper presents using the category structure of Wikipedia to determine the controversy of a single article. This is the first part of the proposed system for classification of topic controversy score for any given text.
Access status: Open Access ,
Exploration of cellular automata: a comprehensive review of dynamic modeling across biology, computer and materials science
(Wydawnictwa AGH, 2023) Vodka, Oleksii O.; Shapovalova, Mariia I.
This paper delves into the expansive world of cellular automata (CA), abstract models of computation comprised of cells that interact based on predefined rules. Originating from John von Neumann’s work in the 1940s, CA has evolved into a multidisciplinary field with applications ranging from mathematical concepts to complex simulations of biological, physical, computer science, material science, and social systems. The paper reviews its historical development, emphasizing John Conway’s influential Game of Life and Burk’s seminar collection. The authors categorize and explore a myriad of CA topics, including self-replicating automata, the universality of computation, compromises in CA, variants, applications in biological systems, fault-tolerant computation, pattern recognition, CA games, fractals, dynamic properties, complexity, image processing, cryptography, bioinformatics, materials modeling, probabilistic automata, and contemporary research. The significance of cellular automata for materials modeling cannot be overstated and considerable attention has been devoted to the issues of modeling nucleation and recrystallization. The review aims to provide a comprehensive resource for both beginners and experts in the field, shedding light on cellular automata’s dynamic and diverse applications in various aspects of life and scientific inquiry.
Access status: Restricted ,
Falls prediction using time series analysis techniques and deep learning methods
(Data obrony: 2020-09-29) Błaszczyk, Maciej
Wydział Informatyki, Elektroniki i Telekomunikacji
Access status: Restricted ,
Geological mapping of North Wazirstan, Pakistan using remotely sensed images
(Data obrony: 2017-10-06) Nawaz, Adil
Wydział Geologii, Geofizyki i Ochrony Środowiska
Digitally enhanced OLI Landsat 8 images were applied for mapping of North Waziristan Pakistan. The territory is rough and without rich vegetation; the exposure of the Waziristan ophiolite, related sedimentary lithologies and inaccessibility to the area made the utilization of Landsat information helpful in this investigation. In the remote sensing investigation, Landsat 8 OLI data were used to make band ratios, band combinations, principal component and image classification methods. Multispectral images were prepared and investigated for this study. On the basis of the image classification techniques; unsupervised classification, five principle lithological units are marked which are giving satisfactory results (about 63.07 %.) when compared with referenced geological map using confusion matrix analysis. The outcomes are very satisfied and need to examine about the utility and confinements of remote sensing strategy on the investigation zone. Further to confirm the results of unsupervised classification, extra investigations might be helpful. As a results, issues confronted during the classification must be considered into all general accuracy.
Access status: Restricted ,
Identyfikacja osób na podstawie analizy tęczówki
(Data obrony: 2009-12-16) Mokrzycki, Jacek
Wydział Elektrotechniki, Automatyki, Informatyki i Elektroniki
Access status: Restricted ,
Implementacja sprzętowa metod normalizacji perspektywy oraz klasyfikatorów dla potrzeb wizyjnego systemu szacowania liczby ludzi w danej lokalizacji
(Data obrony: 2017-01-20) Wąsowicz, Wiktor
Wydział Elektrotechniki, Automatyki, Informatyki i Inżynierii Biomedycznej
Access status: Open Access ,
Initial evaluation of fused satellite images aplicability to vectorisation and classification
(2009) Pirowski, Tomasz; Baran, Joanna; Dzień, Michał
W publikacji przedstawiono wyniki badań związanych z integracją danych spektralnych o niższej rozdzielczości przestrzennej z obrazami panchromatycznymi o wyższej rozdzielczości przestrzennej. Analizy przeprowadzono na danych Landsat i IRS. Testowano cztery metody integracji danych. Zrealizowano dwa cele badań: określono walory fotointerpretacyjne kompozycji barwnych o podwyższonej rozdzielczości w praktycznym aspekcie wektoryzacji granic obiektów oraz wstępnie oceniono przydatności scalonych obrazów do procedur nadzorowanej klasyfikacji spektralnej. Danych referencyjnych do oceny poprawności wektoryzacji i klasyfikacji dostarczyła ortofotomapa ze zdjęć lotniczych.
Access status: Restricted ,
Klasyfikacja równań różniczkowych zwyczajnych rzędu drugiego dopuszczających dwuwymiarową algebrę Liego
(Data obrony: 2010-07-05) Krychniak, Katarzyna
Wydział Matematyki Stosowanej
Access status: Restricted ,
Lithological mapping of open pits NW of Krakow based on high resolution remotely sensed images
(Data obrony: 2017-07-07) Szefler, Dawid
Wydział Geologii, Geofizyki i Ochrony Środowiska
W niniejszej pracy przedstawiono jakościową ocenę efektywności zastosowania teledetekcyjnych zdjęć satelitarnych w celu utworzenia mapy litologicznej. Badanie wykonano na wysokorozdzielczym wielospektralnym zdjęciu pochodzącym z satelity World View 2, na podstawie którego wykonano mapę litologiczną odkrywek zlokalizowanych na północny-zachód od Krakowa. W tym celu zastosowano cyfrowe przetwarzanie obrazu, m. in. wzmocnienie odwzorowania, transformacje i klasyfikacje. Praca zawiera opis nienadzorowanej klasyfikacji, wykonanej na trzech zestawach danych: nieprzetworzonym oraz dwóch nieskorelowanych (PC i liniowo rozciągnięte) z różną ilością klas. Wyniki klasyfikacji zostały porównane do istniejącej mapy geologicznej przy użyciu krostabulacji. Zadowalające dokładności uzyskano dla nieprzetworzonego zdjęcia z 69 klasami.
Access status: Restricted ,
Mechanizm klasyfikacji danych tekstowych oparty o słownik semantyczny
(Data obrony: 2011-03-16) Kaleta, Zbigniew
Wydział Elektrotechniki, Automatyki, Informatyki i Elektroniki
Access status: Open Access ,
Metoda wymuszania wewnętrznych wzorców w jednokierunkowej sieci klasyfikującej
(Wydawnictwa AGH, 2006) Kolibabka, Marcin; Cader, Andrzej
Creating and later learning of one-way neural networks depends from many factors. Selection of many them has estimated and experimental character. The proposed in the article method allows to the weakness of the influence of the not optimal choice of the net structure, also speed and momentum values are less influential then in classic Back Propagation Method.
Access status: Restricted ,
Metody przeciwdziałania powstawaniu osuwisk
(Data obrony: 2018-02-01) Mężyk, Bartosz
Wydział Geologii, Geofizyki i Ochrony Środowiska
Poznanie podstawowych pojęć na temat ruchów masowych jest niezbędne do zrozumienia tematu powstawania osuwisk. Ważne w tym aspekcie jest zrozumienie mechanizmu powstawania osuwisk i ich klasyfikacja. Celem projektu inżynierskiego jest przed-stawienie sposobów przeciwdziałania powstawaniu osuwisk. Jednym ze sposobów ochrony terenów zagrożonych przed ruchami osuwiskowymi jest ich monitorowanie. W przypadku wystąpienia bezpośredniego zagrożenia stosuje się naturalne lub inżynierskie metody przeciwdziałania osuwiskom.
Access status: Open Access ,
Monitoring of land surface temperature from Landsat imagery - a case study of Al-Anbar Governorate in Iraq
(Wydawnictwa AGH, 2023) Morsy, Salem; Ahmed, Shaker
Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.