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Wyświetlanie 1-15 z 47
Tytuł:
Porównanie liniowych metod PCA (Principal Component Analysis) i LDA (Linear Discriminant Analysis) zastosowanych do klasyfikacji matryc wzbudzeniowo-emisyjnych wybranych grup substancji biologicznych
Comparison of Principal Component Analysis and Linear Discriminant Analysis applied to classification of excitation-emission matrices of the selected biological material
Autorzy:
Leśkiewicz, M.
Kaliszewski, M.
Mierczyk, Z.
Włodarski, M.
Powiązania:
https://bibliotekanauki.pl/articles/211300.pdf
Data publikacji:
2016
Wydawca:
Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
Tematy:
analiza cech
spektroskopia fluorescencyjna
klasyfikacja substancji biologicznych
Feature Analysis
Fluorescence Spectroscopy
Biological Material Classification
Opis:
W pracy porównano właściwości dwóch liniowych metod (PCA i LDA) pozwalających na redukcję wymiarów w trakcie analizy cech oraz zbadano wydajność tych dwóch algorytmów w procesie klasyfikacji wybranego materiału biologicznego na podstawie jego wzbudzeniowo-emisyjnych matryc fluorescencyjnych. Stwierdzono, że metoda LDA redukuje liczbę wymiarów (znaczących zmiennych) bardziej efektywnie niż metoda PCA. Za pomocą algorytmu LDA udało się uzyskać względnie dobre rozróżnienie badanego materiału biologicznego.
Quality of two linear methods (PCA and LDA) applied to reduce dimensionality of feature analysis is compared and efficiency of their algorithms in classification of the selected biological materials according to their excitation-emission fluorescence matrices is examined. It has been found that LDA method reduces the dimensions (or a number of significant variables) more effectively than PCA method. Arelatively good discrimination within the examined biological material has been obtained with the use of LDA algorithm.
Źródło:
Biuletyn Wojskowej Akademii Technicznej; 2016, 65, 1; 15-31
1234-5865
Pojawia się w:
Biuletyn Wojskowej Akademii Technicznej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
It’s Raining Immigrants! HELLelujah!: The Metaphors of Immigration in Early American Magazines (1828–1959)
Autorzy:
Anna, Rogos-Hebda,
Powiązania:
https://bibliotekanauki.pl/articles/888683.pdf
Data publikacji:
2020-09-14
Wydawca:
Uniwersytet Warszawski. Wydawnictwa Uniwersytetu Warszawskiego
Tematy:
Critical Metaphor Analysis
Cognitive Linguistics
conceptual metaphor
immigrants
R
multifactorial usage-feature analysis
COHA
historical linguistics
discur- sive construction
Opis:
Stemming from a conviction that the same phenomenon can be construed differently by different cognisers, metaphors used “reflect[ing] and effect[ing] underlying construal operations which are ideological in nature” (Hart 2011, 2), the present paper investigates how the conceptualisation and linguistic construction of IMMIGRANTS changed over time, forwarding a convenient representation of reality. To that end, the study marries the Cognitive Linguistic approach to Critical Discourse Analysis (Charteris-Black 2004; Hart 2010; 2011; 2015) with the multifactorial usage-feature analysis (Glynn 2010). The results have shown that in the times of increased migration IMMIGRANTS were objectified, their otherness foregrounded through appropriate discursive strategies and topoi. Curbing immigration in later periods contributed to an observable shift in the linguistic representation of the immigrant out-group.
Źródło:
Anglica. An International Journal of English Studies; 2020, 29/2; 115-
0860-5734
Pojawia się w:
Anglica. An International Journal of English Studies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Order estimation of japanese paragraphs by supervised machine learning and various textual features
Autorzy:
Murata, M.
Ito, S.
Tokuhisa, M.
Ma, Q.
Powiązania:
https://bibliotekanauki.pl/articles/91894.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
supervised machine learning
estimate
paragraph
vector machine
SVM
feature analysis
nadzorowane uczenie maszynowe
oszacowanie
paragraf
maszyna wektorów nośnych
analiza funkcji
Opis:
In this paper, we propose a method to estimate the order of paragraphs by supervised machine learning. We use a support vector machine (SVM) for supervised machine learning. The estimation of paragraph order is useful for sentence generation and sentence correction. The proposed method obtained a high accuracy (0.84) in the order estimation experiments of the first two paragraphs of an article. In addition, it obtained a higher accuracy than the baseline method in the experiments using two paragraphs of an article. We performed feature analysis and we found that adnominals, conjunctions, and dates were effective for the order estimation of the first two paragraphs, and the ratio of new words and the similarity between the preceding paragraphs and an estimated paragraph were effective for the order estimation of all pairs of paragraphs.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 247-255
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An SFA-HMM performance evaluation method using state difference optimization for running gear systems in high-speed trains
Autorzy:
Cheng, Chao
Wang, Meng
Wang, Jiuhe
Shao, Junjie
Chen, Hongtian
Powiązania:
https://bibliotekanauki.pl/articles/2172116.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
slow feature analysis
SFA
performance evaluation
hidden Markov model
HMM
running gear system
analiza cech
ocena efektywności
ukryty model Markowa
układ biegowy
Opis:
The evaluation of system performance plays an increasingly important role in the reliability analysis of cyber-physical systems. Factors of external instability affect the evaluation results in complex systems. Taking the running gear in high-speed trains as an example, its complex operating environment is the most critical factor affecting the performance evaluation design. In order to optimize the evaluation while improving accuracy, this paper develops a performance evaluation method based on slow feature analysis and a hidden Markov model (SFA-HMM). The utilization of SFA can screen out the slowest features as HMM inputs, based on which a new HMM is established for performance evaluation of running gear systems. In addition to directly classical performance evaluation for running gear systems of high-speed trains, the slow feature statistic is proposed to detect the difference in the system state through test data, and then eliminate the error evaluation of the HMM in the stable state. In addition, indicator planning and status classification of the data are performed through historical information and expert knowledge. Finally, a case study of the running gear system in high-speed trains is discussed. After comparison, the result shows that the proposed method can enhance evaluation performance.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 389--402
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of sparse linear discriminant analysis for prediction of protein-protein interactions
Autorzy:
Stąpor, K.
Fabian, P.
Powiązania:
https://bibliotekanauki.pl/articles/95137.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
sparse discriminant analysis
feature selection
protein-protein interaction
Opis:
To understand the complex cellular mechanisms involved in a biological system, it is necessary to study protein-protein interactions (PPIs) at the molecular level, in which prediction of PPIs plays a significant role. In this paper we propose a new classification approach based on the sparse discriminant analysis [10] to predict obligate (permanent) and non-obligate (transient) protein-protein interactions. The sparse discriminant analysis [10] circumvents the limitations of the classical discriminant analysis [4, 9] in the high dimensional low sample size settings by incorporating inherently the feature selection into the optimization procedure. To characterize properties of protein interaction, we proposed to use the binding free energies. The performance of our proposed classifier is 75% ± 5%.
Źródło:
Information Systems in Management; 2016, 5, 1; 109-118
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Geomorphic Control on Soil Erosion – a Case Study in the Subarnarekha Basin, India
Autorzy:
Kathwas, Amar Kumar
Patel, Nilanchal
Powiązania:
https://bibliotekanauki.pl/articles/2088182.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:
geomorphological feature
soil erosion
USLE
principal component analysis
Opis:
Geomorphology depicts the qualitative and quantitative characteristics of both terrain and landscape features combined with the processes responsible for its evolution. Soil erosion by water involves processes, which removes soil particles and organic matter from the upper sheet of the soil surface, and then transports the eroded material to distant location under the action of water. Very few studies have been conducted on the nature and dynamics of soil erosion in the different geomorphologic features. In the present investigation, an attempt has been made to assess the control of geomorphologic features on the soil loss. Universal Soil Loss Equation (USLE) was used to determine soil loss from the various geomorphological landforms. Principal component analysis (PCA) was implemented on the USLE parameters to determine the degree of association between the individual principal components and the USLE-derived soil loss. Results obtained from the investigation signify the influence of the various landforms on soil erosion. PC5 is found to be significantly correlated with the USLE-derived soil loss. The results ascertained significant association between the soil loss and geomorphological landforms, and therefore, suitable strategies can be implemented to alleviate soil loss in the individual landforms.
Źródło:
Polish Journal of Soil Science; 2021, 54, 1; 1-24
0079-2985
Pojawia się w:
Polish Journal of Soil Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of data pre-processing methods for sentiment analysis of reviews
Autorzy:
Parlar, Tuba
Ozel, Selma
Song, Fei
Powiązania:
https://bibliotekanauki.pl/articles/305513.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
data pre-processing
feature selection
sentiment analysis
text classification
Opis:
The goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.
Źródło:
Computer Science; 2019, 20 (1); 123-141
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring
Autorzy:
Wang, Can
Peng, Jianxin
Zhang, Xiaowen
Powiązania:
https://bibliotekanauki.pl/articles/176601.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
acoustical analysis
feature extraction
support vector machine
snoring sound
Opis:
Acoustical analysis of snoring provides a new approach for the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS). A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. The acoustical features of snoring were extracted from a full night’s recording of 6 OSAHS patients, and regular snoring sounds and snoring sounds related to respiratory disorder events were classified using a support vector machine (SVM) method. The mean recognition rate for simple snoring sounds and snoring sounds related to respiratory disorder events is more than 91.14% by using the grid search, a genetic algorithm and particle swarm optimization methods. The predicted AHI from the present study has a high correlation with the AHI from polysomnography and the correlation coefficient is 0.976. These results demonstrate that the proposed method can classify the snoring sounds of OSAHS patients and can be used to provide guidance for diagnosis of OSAHS.
Źródło:
Archives of Acoustics; 2020, 45, 1; 141-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of geometrical features of a developing lateral root by means of biophysical tools
Autorzy:
Szymanowska-Pulka, J.
Lipowczan, M.
Karczewski, J.
Powiązania:
https://bibliotekanauki.pl/articles/80971.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
conference
root formation
lateral root
Arabidopsis
geometrical feature
analysis
Źródło:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology; 2013, 94, 3
0860-7796
Pojawia się w:
BioTechnologia. Journal of Biotechnology Computational Biology and Bionanotechnology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
In search of phonotactic preferences
Autorzy:
Orzechowska, Paula
Powiązania:
https://bibliotekanauki.pl/articles/1152516.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
phonotactic preferences
feature weighing and ranking
cluster analysis
PCA
Opis:
The objective of this contribution is to provide an analysis of consonant clusters based on the assumption that phonotactic preferences are encoded in phonological features of individual segments forming a cluster. This encoding is expressed by a set of parameters established for the following features: complexity, place of articulation, manner of articulation and voicing. On the basis of empirically observed tendencies of feature distribution and co-occurrence, novel phonotactic preferences for English word-initial consonant clusters are proposed. Statistical methods allow us to weigh the preferences and determine a ranking of phonological features in cluster formation.
Źródło:
Yearbook of the Poznań Linguistic Meeting; 2016, 2, 1
2449-7525
Pojawia się w:
Yearbook of the Poznań Linguistic Meeting
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Supervised probabilistic failure prediction of tuned mass damper-equipped high steel frames using machine learning methods
Autorzy:
Farrokhi, Farshid
Rahimi, Sepideh
Powiązania:
https://bibliotekanauki.pl/articles/1845128.pdf
Data publikacji:
2020
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
failure analysis
supervised machine learning
feature selection
tuned mass damper
Opis:
In this study, firstly, the behavior of a high steel frame equipped with tuned mass damper (TMD) due to several seismic records is investigated considering the structural and seismic uncertainties. Then, machine learning methods including artificial neural networks (ANN), decision tree (DT), Naïve Bayes (NB) and support vector machines (SVM) are used to predict the behavior of the structure. Results showed that among the machine learning models, SVM with Gaussian kernel has better performance since it is capable of predicting the drift of stories and the failure probability with R2 value equal to 0.99. Furthermore, results of feature selection algorithms revealed that when using TMD in high steel structures, seismic uncertainties have greater influences on drift of stories in comparison with structural uncertainties. Findings of this study can be used in design and probabilistic analysis of high steel frames equipped with TMDs.
Źródło:
Studia Geotechnica et Mechanica; 2020, 42, 3; 179-190
0137-6365
2083-831X
Pojawia się w:
Studia Geotechnica et Mechanica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative analysis of morphometric features of the eggs of selected alimentary tract parasites and of the plant pollens
Autorzy:
Szwabe, K.
Kurnatowski, P.
Powiązania:
https://bibliotekanauki.pl/articles/6166.pdf
Data publikacji:
2012
Wydawca:
Polskie Towarzystwo Parazytologiczne
Tematy:
comparative analysis
morphometric feature
egg
gastrointestinal tract
parasite
plant pollen
Źródło:
Annals of Parasitology; 2012, 58, 2
0043-5163
Pojawia się w:
Annals of Parasitology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on ship trajectory extraction based on multiattribute DBSCAN optimisation algorithm
Autorzy:
Xu, Xiaofeng
Cui, Deqaing
Li, Yun
Xiao, Yingjie
Powiązania:
https://bibliotekanauki.pl/articles/1551877.pdf
Data publikacji:
2021
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
clustering algorithm
abnormal route
DBSCAN
feature trajectory extraction
fitting analysis
Opis:
With the vigorous development of maritime traffic, the importance of maritime navigation safety is increasing day by day. Ship trajectory extraction and analysis play an important role in ensuring navigation safety. At present, the DBSCAN (density-based spatial clustering of applications with noise) algorithm is the most common method in the research of ship trajectory extraction, but it has shortcomings such as missing ship trajectories in the process of trajectory division. The improved multi-attribute DBSCAN algorithm avoids trajectory division and greatly reduces the probability of missing sub-trajectories. By introducing the position, speed and heading of the ship track point, dividing the complex water area and vectorising the ship track, the function of guaranteeing the track integrity can be achieved and the ship clustering effect can be better realised. The result shows that the cluster fitting effect reaches up to 99.83%, which proves that the multi-attribute DBSCAN algorithm and cluster analysis algorithm have higher reliability and provide better theoretical guidance for the analysis of ship abnormal behaviour.
Źródło:
Polish Maritime Research; 2021, 1; 136-148
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Multistage Procedure of Mobile Vehicle Acoustic Identification for Single-Sensor Embedded Device
Autorzy:
Astapov, S.
Riid, A.
Powiązania:
https://bibliotekanauki.pl/articles/227146.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
vehicle identification
acoustic signal analysis
feature extraction
classification
fuzzy logic
Opis:
Mobile vehicle identification has a wide application field for both civilian and military uses. Vehicle identification may be achieved by incorporating single or multiple sensor solutions and through data fusion. This paper considers a single-sensor multistage hierarchical algorithm of acoustic signal analysis and pattern recognition for the identification of mobile vehicles in an open environment. The algorithm applies several standalone techniques to enable complex decision-making during event identification. Computationally inexpensive procedures are specifically chosen in order to provide real-time operation capability. The algorithm is tested on pre-recorded audio signals of civilian vehicles passing the measurement point and shows promising classification accuracy. Implementation on a specific embedded device is also presented and the capability of real-time operation on this device is demonstrated.
Źródło:
International Journal of Electronics and Telecommunications; 2013, 59, 2; 151-160
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An analysis of a multidimensional dataset of an epidemic study using soft computing tools - a pilot study
Autorzy:
Handri, S.
Nomura, S.
Irfan, A. C.M.
Fukuda, S.
Yamano, E.
Watanabe, Y.
Powiązania:
https://bibliotekanauki.pl/articles/333077.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
epidemiologia
analiza regresji logistycznej
epidemiology
logistic regression analysis
feature set selection
K-NN analysis
Opis:
Two contrasting approaches toward an epidemic study were illustrated as a pilot study; the regression analysis which is rather conventional methodology used in the past/present epidemic studies, and the other is the classifier analysis which is in the soft computing toolbox. The dataset we used for this study is obtained from a part of a cohort study which principally focused on a fatigue syndrome of the elementary and junior high school educates. In the classifier analysis we employed a major supervised machine-learning algorithm, K-Nearest Neighbour (K-NN), coupled with Principal Component Analysis (PCA). As a result, the performance that was found by cross validation method in the classifier analysis provides better results than that of the regression analysis. Finally we discussed the availability of both analyses with referring the technical and conceptual limitation of both approaches.
Źródło:
Journal of Medical Informatics & Technologies; 2009, 13; 107-110
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 47

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