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Tytuł:
VMD and CNN-Based Classification Model for Infrasound Signal
Autorzy:
Lu, Quanbo
Li, Mei
Powiązania:
https://bibliotekanauki.pl/articles/31339812.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
infrasound signal
variational mode decomposition
convolutional neural network
Fast Fourier Transform
Opis:
Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.
Źródło:
Archives of Acoustics; 2023, 48, 3; 403-412
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Aspect-based sentiment classification model employing whale-optimized adaptive neural network
Autorzy:
Balaganesh, Nallathambi
Muneeswaran, K.
Powiązania:
https://bibliotekanauki.pl/articles/2173622.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
aspect-based sentiment analysis
whale optimization algorithm
artificial neural network
opinion mining
analiza nastrojów oparta na aspektach
algorytm optymalizacji wielorybów
sztuczna sieć neuronowa
eksploracja opinii
Opis:
Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e137271
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Aspect-based sentiment classification model employing whale-optimized adaptive neural network
Autorzy:
Balaganesh, Nallathambi
Muneeswaran, K.
Powiązania:
https://bibliotekanauki.pl/articles/2128172.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
aspect-based sentiment analysis
whale optimization algorithm
artificial neural network
opinion mining
analiza nastrojów oparta na aspektach
algorytm optymalizacji wielorybów
sztuczna sieć neuronowa
eksploracja opinii
Opis:
Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e137271, 1--8
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On the state of budgetary balance over time via the one-way classification model
Autorzy:
Ekhosuehi, V. U.
Oyegue, F. O.
Powiązania:
https://bibliotekanauki.pl/articles/406496.pdf
Data publikacji:
2015
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
budget
expenditure
fixed effects
one-way classification model
zero-sum constraint
Opis:
A study on the state of budgetary balance over time in an economy has been conducted. The planned revenues and expected expenditures contained in the budget statements over the years are used as economic instruments for the study. The one-way classification model in statistical theory is used as the theoretical underpinning to describe the budget equation. The economic implications of the signs of the fixed effects in the model are stated.
Źródło:
Operations Research and Decisions; 2015, 25, 3; 5-16
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pierwotne źródła zagrożeń: model klasyfikacji przyczyn występowania niebezpieczeństw
The Primal Sources of Threats: the Classification Model of Causes of Danger
Autorzy:
Płonka, Bogusław
Powiązania:
https://bibliotekanauki.pl/articles/1934636.pdf
Data publikacji:
2020-04-30
Wydawca:
Wyższa Szkoła Bezpieczeństwa Publicznego i Indywidualnego Apeiron w Krakowie
Tematy:
zagrożenie
ryzyko
źródła zagrożeń
bezpieczeństwo podmiotu
threat
risk
source of threats
subject of security
Opis:
Pojęcie zagrożenia stanowi centralny punkt refleksji nad bezpieczeństwem. W licznych publikacjach podejmowano już próby identyfikacji i klasyfikacji znanych zagrożeń. Podstawowy problem, który wyłania się w trakcie realizowania tego zadania, to wielka liczba potencjalnie grożących człowiekowi niebezpieczeństw. Uzasadnione jest zatem postawienie pytania o to, z czego wynika tak wielka liczba zagrożeń oraz czy można zidentyfikować ich podstawowe źródła. W niniejszym artykule autor analizuje relacje pomiędzy znaczeniami terminów „zagrożenie” i „ryzyko” oraz omawia różne sposoby ich definiowania. Przyjęcie odmiennych perspektyw analizowania, rozpatrywania i klasyfikowania zagrożeń pozwala na uchwycenie elementów stanowiących ich podstawowe, pierwotne źródło. Zaproponowany model klasyfikacji pierwotnych przyczyn występowania niebezpieczeństw uwzględnia trzy podstawowe źródła zagrożeń: zależność, współistnienie i zmianę. Prezentowane podejście może stanowić podstawę projektowania i wdrażania narzędzi diagnozowania bezpieczeństwa podmiotów.
The concept of threat is a central point of reflection on security. Numerous publications have already attempted to identify and classify known hazards. The fundamental problem that arises in the course of this task is a large number of dangers potentially threatening to a person. It is therefore justified to ask why such a large number of threats are posed and whether their main sources can be identified. In the text, the author analyzes the relationship between terms of threat and risk and discusses various ways of defining them. Adopting various perspectives for analyzing, considering and classifying dangers allows observe elements which constitute their primary source. The model for classifying primary causes of threats includes three basic sources of dangers: dependence, coexistence and change. The presented approach may be a basis for configuring and deploying tools for security subject diagnosis.
Źródło:
Kultura Bezpieczeństwa. Nauka – Praktyka – Refleksje; 2019, 36; 100-120
2299-4033
Pojawia się w:
Kultura Bezpieczeństwa. Nauka – Praktyka – Refleksje
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural networks as performance improvement models in intelligent CAPP systems
Autorzy:
Rojek, I.
Powiązania:
https://bibliotekanauki.pl/articles/971020.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
classification model
neural network
tool
manufacturing operation
Opis:
The paper presents neural networks as performance improvement models in intelligent computer aided process planning systems (CAPP systems). For construction of these models three types of neural networks were used: linear network, multi-layer network with error backpropagation, and the Radial Basis Function network (RBF). The models were compared. Due to the comparison, we can say which type of neural network is the best for selection of tools for manufacturing operations. Tool selection for manufacturing operation is a classification problem. Hence, neural networks were built as classification models, meant to improve tool selection for manufacturing. The study was done for selected manufacturing operations: turning, milling and grinding. Models for the milling operation were presented in detail.
Źródło:
Control and Cybernetics; 2010, 39, 1; 54-68
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Das Beschreibungsmodell deutscher und dänischer Derivate nach ihrer Prädikat-Argumentstruktur
The Classification Model of German and Danish Derivatives According to Their Predicate-Argument Structure
Autorzy:
Stopyra, Janusz
Powiązania:
https://bibliotekanauki.pl/articles/504706.pdf
Data publikacji:
2013
Wydawca:
Komisja Nauk Filologicznych Polskiej Akademii Nauk, Oddział we Wrocławiu
Tematy:
German and Danish derivatives
predicate-argument structure
systemic traits of vocabulary
word formation system
Opis:
The following article seeks to present the model of classification of German derivatives which can be traced to both Fillmore’s The Case for Case (1968) and the German formative description proposed by Hans Wellmann from 1984 to 1998 (Duden. Die Grammatik, Band 4). The model seems to remain in conformity with the rule of aligning semantic fields to particular parts of sentences. The particular rule applied for German language was formulated by Wolfgang Motsch (2004). Renata Grzegorczykowa and Jadwiga Puzynina (1999) applied the rule in their analysis of the Polish word formation system. The fundamental role of the model is to classify each and every occurrence of the derivatives in accordance to both their semantic role and the directly motivating sentence segment of an explicative phrase. Derivatives set in such classes can be additionally classified in accordance to the specifi ed suffixes. The discussed model allows systemizing the results of word formation of a particular language. The model enables distinguishing systemic traits of vocabulary, therefore providing the learner with a specific overview and a greater degree of transparency. While considering German and Danish languages, it can be concluded that the Danish word formation system remains in conformity with its German counterpart and is equally complicated in its nature. Danish derivational translating equivalents comprise 65% of Danish equivalents in the following study.
Źródło:
Academic Journal of Modern Philology; 2013, 2; 145-152
2299-7164
2353-3218
Pojawia się w:
Academic Journal of Modern Philology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Single-ended quality measurement of a music content via convolutional recurrent neural networks
Autorzy:
Organiściak, Kamila
Borkowski, Józef
Powiązania:
https://bibliotekanauki.pl/articles/1849158.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
audio data analysis
artefacts detection
convolutional neural networks
recurrent neural networks
classification model
Opis:
The paper examines the usage of Convolutional Bidirectional Recurrent Neural Network (CBRNN) for a problem of quality measurement in a music content. The key contribution in this approach, compared to the existing research, is that the examined model is evaluated in terms of detecting acoustic anomalies without the requirement to provide a reference (clean) signal. Since real music content may include some modes of instrumental sounds, speech and singing voice or different audio effects, it is more complex to analyze than clean speech or artificial signals, especially without a comparison to the known reference content. The presented results might be treated as a proof of concept, since some specific types of artefacts are covered in this paper (examples of quantization defect, missing sound, distortion of gain characteristics, extra noise sound). However, the described model can be easily expanded to detect other impairments or used as a pre-trained model for other transfer learning processes. To examine the model efficiency several experiments have been performed and reported in the paper. The raw audio samples were transformed into Mel-scaled spectrograms and transferred as input to the model, first independently, then along with additional features (Zero Crossing Rate, Spectral Contrast). According to the obtained results, there is a significant increase in overall accuracy (by 10.1%), if Spectral Contrast information is provided together with Mel-scaled spectrograms. The paper examines also the influence of recursive layers on effectiveness of the artefact classification task.
Źródło:
Metrology and Measurement Systems; 2020, 27, 4; 721-733
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of neuron image analysis to build classification model of corpora lutea of domestic cattle
Wykorzystanie neuronowej analizy obrazu w budowie modelu klasyfikacyjnego ciałek żółtych u bydła domowego
Autorzy:
Górna, K.
Zaborowicz, M.
Jaśkowski, B. M.
Idziaszek, P.
Okoń, P.
Boniecki, P.
Przybył, J.
Powiązania:
https://bibliotekanauki.pl/articles/337157.pdf
Data publikacji:
2016
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
Tematy:
neural modeling
computer image analysis
corpus luteum
ovaries
domestic cattle
modelowanie neuronowe
komputerowa analiza obrazu
ciałko żółte
jajnik
bydło domowe
Opis:
The paper presents the results of studies on the usefulness of the texture images USG (ultrasonography) analysis by GLCM (Gray Level Co-Occurrence Matrix) in neural modeling. Tests pertained to the efficacy of the classification of the corpora lutea located in ultrasound images of the domestic cattle ovaries performed by artificial neural networks. The tests were performed using three different methods: the first one used unprocessed images - raw, the second method used image processing - unsharp mask. In the third method the raw images were processed by filter reducing the noise - despeckle filter. For each of the presented methods, the best generated neural network model had the structure of the MLP (Multi Layers Perceptron). The best results, in terms of artificial neural network were obtained in the case of ultrasound images that were not processed prior to texture analysis. As a result, it generated MLP neural model of structure 5:5-8-1:1.
W pracy zaprezentowano wyniki przeprowadzonych badań nad przydatnością analizy tekstury obrazów USG (UltraSonoGraphy) metodą GLCM (Gray Level Co-Occurrence Matrix) w modelowaniu neuronowym. Sprawdzano skuteczność klasyfikacji przez sztuczne sieci neuronowe ciałek żółtych znajdujących się na obrazach USG jajników bydła domowego. Badania wykonano za pomocą trzech różnych metod: w pierwszej wykorzystano obrazy nieprzetworzone - surowe, w drugiej posłużono się metodą przetwarzania obrazu - filtrem wyostrzającym. Natomiast w trzecim sposobie obrazy surowe zostały przetworzone filtrem redukującym zaszumienia. Dla każdej z zaprezentowanych metod, najlepszy wygenerowany model sieci neuronowej miał strukturę MLP (Multi Layer Perceptron). Najlepsze wyniki, pod względem jakości sztucznej sieci neuronowej uzyskano w przypadku obrazów USG, które nie były przetwarzane przed analizą tekstur. W efekcie wygenerowano model neuronowy MLP o strukturze 5:5-8-1:1.
Źródło:
Journal of Research and Applications in Agricultural Engineering; 2016, 61, 3; 162-166
1642-686X
2719-423X
Pojawia się w:
Journal of Research and Applications in Agricultural Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model klasyfikacyjny prognozowania wystąpienia masowych szkód w uprawach leśnych od pędraków chrabąszczy (Melolontha Fabr.)
Classification model for prediction of mass damage in young forest plantations caused by larvae of cockchafer (Melolontha Fabr.)
Autorzy:
Drozdowski, S.
Jankowski, P.
Byk, A.
Powiązania:
https://bibliotekanauki.pl/articles/993325.pdf
Data publikacji:
2013
Wydawca:
Polskie Towarzystwo Leśne
Tematy:
lesnictwo
ochrona lasu
uprawy lesne
szkodniki roslin
chrabaszcz
Melolontha
pedraki
szkody w lesie
prognozowanie
modele klasyfikacyjne
melolontha
logistic regression
root pests
young forest plantations
Opis:
Study presents a classification model for predicting the occurrence of mass damage in young forest plantations caused by insect pests on roots belonging to Melolontha genus. Logistic regression model was built on the basis of 10 taxation features describing 177 young stands. Habitat fertility, occurrence of Scots pine and European beech, and weed infestation of the habitat are the most significant features that influence mass occurrence of cockchafer larvae acting as insect pests on roots in plantations.
Źródło:
Sylwan; 2013, 157, 09; 678-685
0039-7660
Pojawia się w:
Sylwan
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sensor Actor Network Modeling utilizing the Holonic Architectural Framework
Autorzy:
Chiu, C.
Chaczko, Z.
Moses, P.
Powiązania:
https://bibliotekanauki.pl/articles/226170.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Extended Kohonen Maps (EKM)
Sensor Actor Networks (SANET)
Wireless Sensor Networks (WSN)
SANET Middleware
POE Classification Model
holonic architecture
Opis:
This paper discusses the results of utilizing advanced EKM modeling techniques to manage Sensor-Actor networks (SANETs) based upon the Holonic Architectural Framework. EKMs allow a quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize from a given input space to administer SANET routing and clustering functions with a control parameter space. Results demonstrate that in comparison to linear approximation techniques, indirect mapping with EKMs provide fluid control and feedback mechanisms by operating in a continuous sensory control space – thus enabling interactive detection and optimization of events in real-time environments.
Źródło:
International Journal of Electronics and Telecommunications; 2010, 56, 1; 49-54
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model klasyfikacyjny prognozowania wystąpienia mikoryzy u jednorocznych siewek sosny zwyczajnej (Pinus sylvestris L.) na zrębie zupełnym
Classification model for the prediction of mycorrhiza development of one-year-old Scots pine (Pinus sylvestris L.) seedlings on clear-cut
Autorzy:
Aleksandrowicz-Trzcińska, M.
Drozdowski, S.
Powiązania:
https://bibliotekanauki.pl/articles/994751.pdf
Data publikacji:
2012
Wydawca:
Polskie Towarzystwo Leśne
Tematy:
lesnictwo
zrab zupelny
odnowienia lasu
odnowienia naturalne
sosna zwyczajna
Pinus sylvestris
mikrosiedliska
siewki jednoroczne
cechy biometryczne
mikoryza
wystepowanie
modele predykcyjne
analiza logistyczna
logistic regression
natural regeneration
microhabitat
ectomycorrhiza
biometric parameters
Opis:
The aim of this study was to build the models describing the relationships between mycorrhiza development in one−year−old Scots pine seedlings and site preparation method, place of growth of seedlings on the clear−cut area and their biometric parameters. A logistic regression was used to study.
Źródło:
Sylwan; 2012, 156, 12; 883-894
0039-7660
Pojawia się w:
Sylwan
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Crash data reporting systems in Fourteen Arab countries: challenges and improvement
Autorzy:
Abounoas, Zahira
Raphael, Wassim
Badr, Yarob
Faddoul, Rafic
Guillaume, Anne
Powiązania:
https://bibliotekanauki.pl/articles/1833641.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
road accidents
road safety
information system
reporting system
variables selection
classification model
wypadek drogowy
bezpieczeństwo na drogach
system informacyjny
systemy raportowania
dobór zmiennych
model klasyfikacji
Opis:
Traffic crash fatalities and serious injuries still represent a big burden for most Arab countries because the actual policies, strategies, and interventions are based on poorly collected data. Through this paper, we assessed the crash data reporting systems in Fourteen Arab countries via a survey conducted to identify the fundamental dysfunctions at the management and data collection levels. Then, to address some of the dataset problems, we had applied data mining technics to select a minimum of variables (crash, vehicle, and road user) that should be collected for a better understanding of crash circumstances. For this raison, three methods of selection (correlation, information gain, and gain ratio) and seven classifiers (naive Bayes, nearest neighbour, random forest, random tree, J48, reduced error pruning tree, and bagging) were tested and compared to identify the variables that affect significantly the crashes severity. Decision trees family of classifiers showed the best performance based on the analysis of the area under the curve. The explanatory variables obtained from the data mining process were combined with other descriptive variables to maintain traceability. As a result, we produced hybrid lists of variables for the crash, vehicle, and road user, each contains 25 variables. Finally, in order to propose a cost-effective solution to switch from manual to electronic data collection, we got inspired by a tool used to track animals to create and customize a unified e-form for handheld devices, in order to ensure easy entering of the harmonized data for the entire region based on our selected lists of variables. The tool verified the countries requirements especially by enabling data collection and transfer with and without the internet, and by allowing data analysis thought its built-in Geographic Information System (GIS) capabilities.
Źródło:
Archives of Transport; 2020, 56, 4; 73-88
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predykcyjny model dobowej emisji energii sejsmicznej indukowanej eksploatacją górniczą
Predictive model of the daily release of seismic energy induced by mining
Autorzy:
Jakubowski, J.
Lenart, Ł.
Ożóg, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/166220.pdf
Data publikacji:
2014
Wydawca:
Stowarzyszenie Inżynierów i Techników Górnictwa
Tematy:
sejsmiczność indukowana
wstrząsy górnicze
hazard sejsmiczny
zagrożenie tąpaniami
drzewa wzmacniane
sieci neuronowe
regresja logistyczna
modele prognostyczne
modele klasyfikacyjne
induced seismicity
mining tremors
seismic hazard
rockburst hazard
data mining
boosted trees
neural networks
logistic regression
predictive model
classification model
Opis:
W artykule przedstawiono budowę i ocenę predykcyjnego modelu klasyfikacyjnego dobowej emisji energii sejsmicznej indukowanej eksploatacją ścianową węgla. Model jest oparty na danych z katalogu wstrząsów i podstawowych danych o wydobyciu i ścianach eksploatowanych w partii XVI kopalni Piast w okresie od lipca 1987 do marca 2011. Zmienną prognozowaną jest dwustanowa zmienna określająca wystąpienie dobowej sumy energii sejsmicznej wstrząsów w rejonie ściany większej lub równej wartości progowej 10/5 J. Zastosowano trzy metody analityczne w schemacie data mining: regresję logistyczną, sieci neuronowe i drzewa wzmacniane. Jako najlepszy do celów prognozy wybrano model drzew wzmacnianych. Wyniki na zbiorze walidacyjnym pokazały jego dobrą zdolność predykcyjną, co zachęca do dalszych badań.
This paper presents the design and evaluation of the classification predictive model of daily seismic activity induced by longwall mining. The model combines seismic catalog data, output volume and basic characteristics of the longwall faces in sector XVI of the Piast coal mine over the period of July 1987 to March 2011. The predicted variable defines the occurrence of a daily sum of seismic energy released nearby the longwall, that is greater than or equal to the threshold value of 10/5 J. Machine learning and statistical methods were applied, namely neural networks, stochastic gradient boosted trees and logistic regression. The design and evaluation of the classification predictive models were presented. The boosted tree model appeared to meet the prediction quality criteria best. The results of the model evaluation show its promising predictive capability.
Źródło:
Przegląd Górniczy; 2014, 70, 3; 18-25
0033-216X
Pojawia się w:
Przegląd Górniczy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of happiness in EU countries using the multi-model classification based on models of symbolic data
Autorzy:
Pełka, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/425036.pdf
Data publikacji:
2019
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
happiness
the European Union
symbolic data analysis
ensemble clustering
Opis:
The results of happiness analysis are presented in the form of a World Happiness Report that covers 156 countries and 17 different indicators. In the article model-based clustering ensemble is built to determine what selected European countries have similar patterns of happiness. The results are analyzed using multidimensional scaling and a decision tree to find out what factors determine cluster memberships. In the empirical part, three clusters were detected The first contains countries: Austria, Denmark, Finland, Germany, Ireland, Luxembourg, the Netherlands, Norway, Sweden, Switzerland and the United Kingdom. They have the highest values for all the variables, except the negative affect. The second cluster contains seven countries: Bulgaria, Estonia, Hungary, Lithuania, Poland, Romania and Slovakia. This cluster is also the most homogeneous one. The third cluster contains eight countries: Cyprus, the Czech Republic, France, Greece, Italy, Portugal, Slovenia and Spain.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2019, 23, 3; 15-25
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 107

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