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Wyświetlanie 1-15 z 361
Tytuł:
Material identification during turning by neural network
Autorzy:
Denkena, Berend
Bergmann, Benjamin
Handrup, Miriam
Witt, Matthias
Powiązania:
https://bibliotekanauki.pl/articles/99572.pdf
Data publikacji:
2020
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
machine learning
turning
monitoring
Opis:
A design concept for high-performance components involves the combination of different materials in hybrid workpieces. Different material properties and chemical compositions influence the machining quality of hybrid workpieces. To achieve a constant workpiece and process quality, it is necessary to adjust the process parameters to the individual material. Thus, it is mandatory to classify the material during machining for the relevant range of process parameters. This paper examines teaching strategies for neural networks to determine the machined material in process by a small amount of cross points. For this purpose, different training sets are compared. Process parameters with different cutting speeds, feeds and with constant and varying depth of cut are examined. In addition, the signal sources necessary for robust material classification are compared and investigated. The investigation is performed for the cylindrical turning of friction welded EN AW-6082/20MnCr5 shafts. The study shows that an F1 score of 0.99 is achieved at a constant cutting depth, provided that only the corner points of the process window and the machine control signals are used for training. With an additional variation of the cutting depth, the classification rate is significantly improved by the use of external sensors such as the acceleration sensor.
Źródło:
Journal of Machine Engineering; 2020, 20, 2; 65-76
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Preface to special issue on recent advances in machine learning and its applications
Autorzy:
Kowalski, Piotr A.
Łukasik, Szymon
Kulczycki, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/384244.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
advance
machine learning
applications
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 2; 30-31
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Computer System for Automated Ontology Building Basic Crocus
Autorzy:
Oborska, O.
Maherovskyj, M
Vovnjanka, R.
Powiązania:
https://bibliotekanauki.pl/articles/118041.pdf
Data publikacji:
2015
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
ontology
machine learning
logic predicate
Opis:
The article exposes the approach developing a computer system of automated ontology building based on creation of architecture system ontology synthesis CROCUS (Cognition Relations or Concepts Using Semantics) software model. The basic modules of the system and its operations are described. The choice of software tools for implementation was described. Example of SDK decision for system realization was substantiated. The using of this system allows filling the domain ontology in automatic mode.
Źródło:
Applied Computer Science; 2015, 11, 4; 70-82
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On different ways to classify Internet traffic : a short review of selected publications
O wielu sposobach klasyfikacji ruchu internetowego: krótki przegląd wybranych publikacji
Autorzy:
Foremski, P.
Powiązania:
https://bibliotekanauki.pl/articles/375768.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
internet
traffic classification
machine learning
Opis:
Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversify in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.
Artykuł prezentuje przegląd 13 wybranych prac z dziedziny klasyfikacji ruchu internetowego pod kątem różnorodności w zastosowanych metodach. Prace zostały wybrane z najciekawszych naszym zdaniem publikacji z ostatnich kilku lat (2009-2012). W porównaniu do istniejących przeglądów literaturowych - np. [13], [14], czy [3] - niniejszy artykuł dotyczy nowszych badań, oraz wykazuje, że łączenie wielu metod klasyfikacji w jeden system może być ciekawym kierunkiem dla przyszłych badań w tej dziedzinie. Klasyfikacja ruchu internetowego polega na odgadnięciu nazwy protokołu komunikacyjnego lub aplikacji, która wygenerowała dany ciąg pakietów IR Informacja ta jest przydatna np. w zarządzaniu ruchem w sieciach internetowych, gdy potrzeba kształtować ruch w zależności od jego rodzaju. Klasyfikacja ruchu znajduje zastosowanie także w zagadnieniach sieciowych związanych z wdrażaniem zasad bezpieczeństwa (np. zakaz stosowania aplikacji Skype), monitorowaniem natężenia ruchu (np. wykrywanie ataków DoS), oraz wielu innych. Przegląd literatury został podzielony na 4 kategorie: klasyfikacja ruchu (rozdział 3.1., prace nr 16), detekcja pojedynczych aplikacji (rozdział 3.2., prace nr 7-8), metody pozyskiwania „wiedzy bazowej" (ang. ground truth, rozdział 3.3., prace nr 9-11), oraz inne (rozdział 3.4., prace nr 12 i 13). Wszystkie prace zostały podsumowane w Tabeli 3. W ostatnim rozdziale (str. 10) prezentujemy wyniki przeglądu. Pokazujemy na przykład, że istnieje wiele metod klasyfikacji, które mogą być połączone w jeden system i wzajemnie się uzupełniać - przez multiklasyfikację (ang. multi-classification] lub obsługę różnych części ruchu (np. [31] dla TCP i [15] dla UDP). Podajemy także nasze rekomendacje dotyczące walidacji metod klasyfikacji i zbierania śladów ruchu internetowego.
Źródło:
Theoretical and Applied Informatics; 2013, 25, 2; 119-136
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Weather and a part of day recognition in the photos using a KNN methodology
Autorzy:
Krzywicki, T.
Powiązania:
https://bibliotekanauki.pl/articles/298048.pdf
Data publikacji:
2018
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
image analysis
machine learning
classification
Opis:
This article presents a proposal for recognizing the weather and part of a day in digital photos encoded in the bitmap format, based on auctorial edge detection algorithm of horizon to demarcate the sky and k-nearest neighbours algorithm, to classify the daytime in the picture as “day” or “night” and to classify the weather as “sunny” or “cloudy”. To verify the effectiveness of the classification the Internal Bagging-5 model was applied. The data for surveys in the form of pictures was prepared on self-provision. To test the method in a different location, data from the Internet was used.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2018, 21(4); 291-302
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Word prediction in computational historical linguistics
Autorzy:
Dekker, Peter
Zuidema, Willem
Powiązania:
https://bibliotekanauki.pl/articles/1818886.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:
computational historical linguistics
machine learning
deep learning
Opis:
In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.
Źródło:
Journal of Language Modelling; 2020, 8, 2; 295--336
2299-856X
2299-8470
Pojawia się w:
Journal of Language Modelling
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Word prediction in computational historical linguistics
Autorzy:
Dekker, Peter
Zuidema, Willem
Powiązania:
https://bibliotekanauki.pl/articles/1818890.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:
computational historical linguistics
machine learning
deep learning
Opis:
In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.
Źródło:
Journal of Language Modelling; 2020, 8, 2; 295--336
2299-856X
2299-8470
Pojawia się w:
Journal of Language Modelling
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Long-term Traffic Forecasting in Optical Networks Using Machine Learning
Autorzy:
Walkowiak, Krzysztof
Szostak, Daniel
Włodarczyk, Adam
Kasprzak, Andrzej
Powiązania:
https://bibliotekanauki.pl/articles/27311948.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
traffic forecasting
machine learning
classification
Regression
Opis:
Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-terT traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 751--762
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Twitter Opinion Mining Using Sentiment Analysis
Autorzy:
Maheshwari, Saumil
Shukla, Shubham
Kumari, Dazy
Powiązania:
https://bibliotekanauki.pl/articles/1075568.pdf
Data publikacji:
2019
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Naive Bayes
Opinion Mining
machine learning
Opis:
This is very important to extract verdict or opinion of others about any product, topic or about some person. The rich sources of this opinion rich data are blogs, online review sites and social networking sites. Among the social networking sites, twitter one of such largest source of microblog has gained popularity with more than 500 million tweets per day. Because of this Twitter has become a primary source for opinion mining. Twitter messages called tweets, are much focused because of the restricted characters size of 140 characters. Social network data is one of the most effective and accurate indicators of public sentiment. In this paper, twitter data is analyzed to determine the opinion of public. Twitter data about iPhone 6 is collected for analysis using the Twitter public API which allows developers to extract tweets from twitter programmatically. In this paper, Naïve Bayes classifier is used to calculate the sentiments of tweets and compared with baseline algorithms.
Źródło:
World Scientific News; 2019, 121; 73-82
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Extreme gradient boosting method in the prediction of company bankruptcy
Autorzy:
Pawełek, Barbara
Powiązania:
https://bibliotekanauki.pl/articles/1194455.pdf
Data publikacji:
2019-07-02
Wydawca:
Główny Urząd Statystyczny
Tematy:
XGBoost
company bankruptcy
machine learning
outlier
Opis:
Machine learning methods are increasingly being used to predict company bankruptcy. Comparative studies carried out on selected methods to determine their suitability for predicting company bankruptcy have demonstrated high levels of prediction accuracy for the extreme gradient boosting method in this area. This method is resistant to outliers and relieves the researcher from the burden of having to provide missing data. The aim of this study is to assess how the elimination of outliers from data sets affects the accuracy of the extreme gradient boosting method in predicting company bankruptcy. The added value of this study is demonstrated by the application of the extreme gradient boosting method in bankruptcy prediction based on data free from the outliers reported for companies which continue to operate as a going concern. The research was conducted using 64 financial ratios for the companies operating in the industrial processing sector in Poland. The research results indicate that it is possible to increase the detection rate for bankrupt companies by eliminating the outliers reported for companies which continue to operate as a going concern from data sets.
Źródło:
Statistics in Transition new series; 2019, 20, 2; 155-171
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning and traditional econometric models : a systematic mapping study
Autorzy:
Pérez-Pons, María E.
Parra-Dominguez, Javier
Omatu, Sigeru
Herrera-Viedma, Enrique
Corchado, Juan Manuel
Powiązania:
https://bibliotekanauki.pl/articles/2147125.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
machine learning
econometric models
regression
prediction
Opis:
Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 79--100
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Extensive feature set approach in facial expression recognition in static images
Autorzy:
Żarkowski, M.
Powiązania:
https://bibliotekanauki.pl/articles/950766.pdf
Data publikacji:
2013
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
machine learning
image processing
social robotics
Opis:
The article presents the preliminary concept of facial emotion recognition system. The approach focuses on the feature extraction and selection process which simplifies the stage of defining and adding new elements to the feature set. The evaluation of the system was performed with two discriminant analysis classifiers, decision tree classifier and four variants of k-nearest neighbors classifier. The system recognizes seven emotions. The verification step utilizes two databases of face images representing laboratory and natural conditions. Personal and interpersonal emotion recognitin was evaluated. The best quality of classification for personal emotion recognition was achieved by 1NN classifier, the recognition rate was 99.9% for the laboratory conditions and 97.3 for natural conditions. For interpersonal emotion recognition the rate was 82.5%.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2013, 7, 4; 52-58
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gearbox faults feature selection and severity classification using machine learning
Autorzy:
Zuber, Ninoslav
Bajrić, Rusmir
Powiązania:
https://bibliotekanauki.pl/articles/1841932.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
gearbox fault
vibration analysis
machine learning
Opis:
The most widely used technique for gearbox fault diagnosis is still vibration analysis. The need for gearbox condition monitoring in an automated process is essential and there is still a problem with the selection of features that best describe a fault or its severity level. For this purpose, multiple-domain vibration signals statistic features are extracted through time and frequency domain by postprocessing of raw time signal, time-synchronous average signal, frequency spectra and cepstrum. Five different datasets are considered with different levels of fault analyzing gear chipped and a missing tooth, gear root crack, and gear tooth wear under stable running speed and load. A preliminary experimental study of a single stage test bench gearbox was performed in order to test feature sensitivity to type and level of fault in the process of clustering and classification. Selected features were finally processed using an artificial neural network classifier.
Źródło:
Eksploatacja i Niezawodność; 2020, 22, 4; 748-756
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques
Autorzy:
Thomas, Angel
Palekar, Sangeeta
Kalambe, Jayu
Powiązania:
https://bibliotekanauki.pl/articles/2077647.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
glucose
image processing
machine learning
colorimetry
Opis:
Glucose concentration measurement is essential for diagnosis, monitoring and treatment of various medical conditions like diabetes mellitus, hypoglycemia, etc. This paper presents a novel image-processing and machine learning based approach for glucose concentration measurement. Experimentation based on Glucose oxidase - peroxidase (GOD/POD) method has been performed to create the database. Glucose in the sample reacts with the reagent wherein the concentration of glucose is detected using colorimetric principle. Colour intensity thus produced, is proportional to the glucose concentration and varies at different levels. Existing clinical chemistry analyzers use spectrophotometry to estimate the glucose level of the sample. Instead, this developed system uses simplified hardware arrangement and estimates glucose concentration by capturing the image of the sample. After further processing, its Saturation (S) and Luminance (Y) values are extracted from the captured image. Linear regression based machine learning algorithm is used for training the dataset consists of saturation and luminance values of images at different concentration levels. Integration of machine learning provides the benefit of improved accuracy and predictability in determining glucose level. The detection of glucose concentrations in the range of 10–400 mg/dl has been evaluated. The results of the developed system were verified with the currently used spectrophotometry based Trace40 clinical chemistry analyzer. The deviation of the estimated values from the actual values was found to be around 2-3%.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 323--328
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative study of corporate credit ratings prediction with machine learning
Autorzy:
Doğan, Seyyide
Büyükkör, Yasin
Atan, Murat
Powiązania:
https://bibliotekanauki.pl/articles/2175830.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
credit rating
credit risk
machine learning
Opis:
Credit scores are critical for financial sector investors and government officials, so it is important to develop reliable, transparent and appropriate tools for obtaining ratings. This study aims to predict company credit scores with machine learning and modern statistical methods, both in sectoral and aggregated data. Analyses are made on 1881 companies operating in three different sectors that applied for loans from Turkey’s largest public bank. The results of the experiment are compared in terms of classification accuracy, sensitivity, specificity, precision and Mathews correlation coefficient. When the credit ratings are estimated on a sectoral basis, it is observed that the classification rate considerably changes. Considering the analysis results, it is seen that logistic regression analysis, support vector machines, random forest and XGBoost have better performance than decision tree and k-nearest neighbour for all data sets.
Źródło:
Operations Research and Decisions; 2022, 32, 1; 25--47
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 361

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