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    Wyświetlanie 1-11 z 11
    Tytuł:
    From conventional to machine learning methods for maritime riskassessment
    Autorzy:
    Rawson, A.
    Brito, M.
    Sabeur, Z.
    Tran-Thanh, L.
    Powiązania:
    https://bibliotekanauki.pl/articles/2063954.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    risk assessment
    machine learning method
    bayesian networks
    machine learning algorithms
    multicriteria approach
    maritime risk
    Opis:
    Within the last thirty years, the range and complexity of methodologies proposed to assess maritime risk have increased significantly. Techniques such as expert judgement, incident analysis, geometric models, domain analysis and Bayesian Networks amongst many others have become dominant within both the literature and industry. On top of this, advances in machine learning algorithms and big data have opened opportunities for new methods which might overcome some limitations of conventional approaches. Yet, determining the suitability or validity of one technique over another is challenging as it requires a systematic multicriteria approach to compare the inputs, assumptions, methodologies and results of each method. Within this paper, such an approach is proposed and tested within an isolated waterway in order to justify the proposed advantages of a machine learning approach to maritime risk assessment and should serve as inspiration for future work.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2021, 15, 3; 757--764
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving the credibility of the extracted position from a vast collection of job offers with machine learning ensemble methods
    Autorzy:
    Drozda, Paweł
    Ropiak, Krzysztof
    Nowak, Bartosz A.
    Talun, Arkadiusz
    Osowski, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/22615539.pdf
    Data publikacji:
    2023
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    machine learning
    web scraping
    granularity method
    classification
    Opis:
    The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2023, 26(1); 125--140
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of artificial neural networks in predicting voter turnout based on the analysis of demographic data
    Autorzy:
    Michalak, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/92572.pdf
    Data publikacji:
    2019
    Wydawca:
    Oddział Kartograficzny Polskiego Towarzystwa Geograficznego
    Tematy:
    artificial neural networks
    voter turnout
    machine learning
    cartographic research method
    Opis:
    The author presents the results of research on the use of artificial neural networks in predicting voter turnout. He describes the principles of operation of artificial neural networks, as well as detailed results of two machine learning methods used to predict voter turnout. The research resulted in creation of a functional model that allows for prediction of voter turnout results with a considerable degree of accuracy. The entire research process was carried out using the cartographic research method.
    Źródło:
    Polish Cartographical Review; 2019, 51, 3; 109-116
    2450-6974
    Pojawia się w:
    Polish Cartographical Review
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An advanced ensemble modeling approach for predicting carbonate reservoir porosity from seismic attributes
    Autorzy:
    Topór, Tomasz
    Sowiżdżał, Krzysztof
    Powiązania:
    https://bibliotekanauki.pl/articles/27310145.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    machine learning
    model stacking
    ensemble method
    carbonates
    seismic attributes
    porosity prediction
    Opis:
    This study uses a machine learning (ML) ensemble modeling approach to predict porosity from multiple seismic attributes in one of the most promising Main Dolomite hydrocarbon reservoirs in NW Poland. The presented workflow tests five different model types of varying complexity: K-nearest neighbors (KNN), random forests (RF), extreme gradient boosting (XGB), support vector machine (SVM), single layer neural network with multilayer perceptron (MLP). The selected models are additionally run with different configurations originating from the pre-processing stage, including Yeo–Johnson transformation (YJ) and principal component analysis (PCA). The race ANOVA method across resample data is used to tune the best hyperparameters for each model. The model candidates and the role of different pre-processors are evaluated based on standard ML metrics – coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The model stacking is performed on five model candidates: two KNN, two XGB, and one SVM PCA with a marginal role. The results of the ensemble model showed superior accuracy over single learners, with all metrics (R2 0.890, RMSE 0.0252, MAE 0.168). It also turned out to be almost three times better than the neural net (NN) results obtained from commercial software on the same testing set (R2 0.318, RMSE 0.0628, MAE 0.0487). The spatial distribution of porosity from the ensemble model indicated areas of good reservoir properties that overlap with hydrocarbon production fields. This observation completes the evaluation of the ensemble technique results from model metrics. Overall, the proposed solution is a promising tool for better porosity prediction and understanding of heterogeneous carbonate reservoirs from multiple seismic attributes.
    Źródło:
    Geology, Geophysics and Environment; 2023, 49, 3; 245--260
    2299-8004
    2353-0790
    Pojawia się w:
    Geology, Geophysics and Environment
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Machine Learning Methods in Algorithmic Trading Strategy Optimization – Design and Time Efficiency
    Autorzy:
    Ryś, Przemysław
    Ślepaczuk, Robert
    Powiązania:
    https://bibliotekanauki.pl/articles/1356900.pdf
    Data publikacji:
    2019-08-09
    Wydawca:
    Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
    Tematy:
    Algorithmic trading
    investment strategy
    machine learning
    optimization
    differential evolutionary method
    cross-validation
    overfitting
    Opis:
    The main aim of this paper was to formulate and analyse the machine learning methods, fitted to the strategy parameters optimization specificity. The most important problems are the sensitivity of a strategy performance to little parameter changes and numerous local extrema distributed over the solution space in an irregular way. The methods were designed for the purpose of significant shortening of the computation time, without a substantial loss of strategy quality. The efficiency of methods was compared for three different pairs of assets in case of moving averages crossover system. The problem was presented for three sets of two assets’ portfolios. In the first case, a strategy was trading on the SPX and DAX index futures; in the second, on the AAPL and MSFT stocks; and finally, in the third case, on the HGF and CBF commodities futures. The methods operated on the in-sample data, containing 16 years of daily prices between 1998 and 2013 and was validated on the out-of-sample period between 2014 and 2017. The major hypothesis verified in this paper is that machine learning methods select strategies with evaluation criterion near the highest one, but in significantly lower execution time than the brute force method (Exhaustive Search).
    Źródło:
    Central European Economic Journal; 2018, 5, 52; 206 - 229
    2543-6821
    Pojawia się w:
    Central European Economic Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Cutting force prediction of Ti6Al4V using a machine learning model of SPH orthogonal cutting process simulations
    Autorzy:
    Klippel, Hagen
    Sanchez, Eduardo Gonzalez
    Isabel, Margolis
    Röthlin, Matthias
    Afrasiabi, Mohamadreza
    Michal, Kuffa
    Wegener, Konrad
    Powiązania:
    https://bibliotekanauki.pl/articles/2052187.pdf
    Data publikacji:
    2022
    Wydawca:
    Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
    Tematy:
    machining
    Ti6Al4V
    machine learning
    SPH
    smoothed particle hydrodynamics
    meshfree method
    Opis:
    The prediction of machining processes is a challenging task and usually requires a large experimental basis. These experiments are time-consuming and require manufacturing and testing of different tool geometries at various process conditions to find optimum machining settings. In this paper, a machine learning model of the orthogonal cutting process of Ti6Al4V is proposed to predict the cutting and feed forces for a wide range of process conditions with regards to rake angle, clearance angle, cutting edge radius, feed and cutting speed. The model uses training data generated by virtual experiments, which are conducted using physical based simulations of the orthogonal cutting process with the smoothed particle hydrodynamics (SPH). The ML training set is composed of input parameters, and output process forces from 2500 instances of GPU accelerated SPH simulations. The resulting model provides fast process force predictions and can consider the cutter geometry in comparison to classical analytical approaches.
    Źródło:
    Journal of Machine Engineering; 2022, 22, 1; 111-123
    1895-7595
    2391-8071
    Pojawia się w:
    Journal of Machine Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of prototype selection algorithms used in construction of neural networks learned by SVD
    Autorzy:
    Jankowski, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/330020.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    radial basis function network
    extreme learning machine
    kernel method
    prototype selection
    machine learning
    k nearest neighbours
    radialna funkcja bazowa
    metoda jądrowa
    uczenie maszynowe
    metoda k najbliższych sąsiadów
    Opis:
    Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions. What is more, prototype selection algorithms automatically choose not only the placement of prototypes, but also their number. Thanks to this advantage, it is no longer necessary to estimate the number of kernels with time-consuming multiple train-test procedures. The best results of learning can be obtained by pseudo-inverse learning with a singular value decomposition (SVD) algorithm. The article presents a comparison of several prototype selection algorithms co-working with singular value decomposition-based learning. The presented comparison clearly shows that the combination of prototype selection and SVD learning of a neural network is significantly better than a random selection of kernels for the RBFN or the ELM, the support vector machine or the kNN. Moreover, the presented learning scheme requires no parameters except for the width of the Gaussian kernel.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 719-733
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
    Autorzy:
    Sumi, S. M.
    Zaman, M. F.
    Hirose, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/331290.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    maszyna ucząca się
    metoda wielomodelowa
    przetwarzanie wstępne
    rainfall forecasting
    machine learning
    multi model method
    preprocessing
    model ranking
    Opis:
    In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A fast neural network learning algorithm with approximate singular value decomposition
    Autorzy:
    Jankowski, Norbert
    Linowiecki, Rafał
    Powiązania:
    https://bibliotekanauki.pl/articles/330870.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Moore–Penrose pseudoinverse
    radial basis function network
    extreme learning machine
    kernel method
    machine learning
    singular value decomposition
    deep extreme learning
    principal component analysis
    pseudoodwrotność Moore–Penrose
    radialna funkcja bazowa
    maszyna uczenia ekstremalnego
    uczenie maszynowe
    analiza składników głównych
    Opis:
    The learning of neural networks is becoming more and more important. Researchers have constructed dozens of learning algorithms, but it is still necessary to develop faster, more flexible, or more accurate learning algorithms. With fast learning we can examine more learning scenarios for a given problem, especially in the case of meta-learning. In this article we focus on the construction of a much faster learning algorithm and its modifications, especially for nonlinear versions of neural networks. The main idea of this algorithm lies in the usage of fast approximation of the Moore–Penrose pseudo-inverse matrix. The complexity of the original singular value decomposition algorithm is O(mn2). We consider algorithms with a complexity of O(mnl), where l < n and l is often significantly smaller than n. Such learning algorithms can be applied to the learning of radial basis function networks, extreme learning machines or deep ELMs, principal component analysis or even missing data imputation.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 3; 581-594
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of data quoted on the Day-Ahead Market of TGE S.A. using Statistics and Machine Learning Toolbox
    Autorzy:
    Tchórzewski, Jerzy
    Longota, Bartłomiej
    Powiązania:
    https://bibliotekanauki.pl/articles/2201615.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    artificial neural network
    cluster analysis
    Day-Ahead Market
    k-means method
    Matlab and Simulink environment
    Statistics and Machine Learning Toolbox
    Ward’s method
    Opis:
    The publication contains the results of research in the field of cluster analysis carried out using data quoted on the Day-Ahead Market of TGE S.A. Two methods were used in the analysis, one hierarchical known as the Ward’s method, and the other non-hierarchical - the k-means method. Many interesting research results have been obtained, which are illustrated, among others, in in the form of dendrograms, silhouette graphs and graphs in the form of clusters. Data on the volume and the volumeweighted average price of electricity were examined for various types of quotations: fixing 1, fixing 2 and continuous quotations. The research was carried out in the MATLAB and Simulink environments using a library called Machine and Statistics Learning Toolbox. Selected test results were interpreted.
    Źródło:
    Studia Informatica : systems and information technology; 2022, 2(27); 49--74
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Quo Vadis NDT? - A Forecast of the Future
    Quo Vadis NDT? – Prognoza przyszłości
    Autorzy:
    Dobmann, Gerd
    Powiązania:
    https://bibliotekanauki.pl/articles/1402118.pdf
    Data publikacji:
    2020
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    full matrix capture
    Total Focusing Method
    artificial intelligence
    machine learning
    additive manufacturing
    metoda pełnego ogniskowania
    TFM
    sztuczna inteligencja
    uczenie maszynowe
    produkcja przyrostowa
    Opis:
    The here presented contribution will forecast the future of NDT, even if this is difficult, because of many uncertainties. Large NDT conferences offer the opportunity to statistically evaluate the most popular topics which are in the actual interest of the NDT community. The author, engaged as subject editor of the Journal NDT&E International, will discuss his experience with peer-reviewed papers too. Worldwide, especially politicians talk about the next industrial revolution, using «catchwords» like digitization, big data, robotics, artificial intelligence, high powerful computing, cloud computing, intelligent manufacturing, etc. We have a global competition; China alone will invest in R&D of artificial intelligence (AI) in the next five years 150 billion $US. However, all R&D projects to these topics are primarily not NDT developing programs. But, NDT will follow the mainstream and will participate in hardware and software developments, to adapt them for its own needs. The contribution discusses tendencies of developments, for instance, in additive manufacturing where NDT is utilized in real-time to feed-back control, to produce - «on-line closed-loop» - the quality. Two special innovations are discussed; one is to the non-linear phenomenon of Local Defect Resonances in visco-elastic materials, the other to Vertical-Cavity Surface-Emitting Lasers (VCSEL), a new, powerful, and flexible heat source in Thermal Testing.
    Niniejsza praca przedstawia prognozę przyszłości badań nieniszczących (BN), nawet jeśli jest to trudne z powodu wielu niewiadomych. Duże konferencje dotyczące BN dają możliwość przeprowadzenia statystycznej oceny najpopularniejszych tematów, którymi aktualnie interesuje się społeczność BN. Autor, pełniący funkcję redaktora działowego w Journal NDT & E International, omówi również swoje doświadczenia z recenzowanymi artykułami. Na całym świecie, zwłaszcza politycy, mówią o kolejnej rewolucji przemysłowej, używając „haseł”, takich jak cyfryzacja, big data, robotyka, sztuczna inteligencja, obliczenia o dużej mocy, przetwarzanie w chmurze, inteligentna produkcja itp. Mamy globalną konkurencję a same Chiny zainwestują w badania i rozwój sztucznej inteligencji (AI) w ciągu najbliższych pięciu lat 150 miliardów dolarów. Wszystkie te projekty badawczo-rozwojowe z wymienionej tematyki nie dotyczą rozwoju BN. Jednakże, BN będą podążać za głównym nurtem i będą korzystać z rozwoju sprzętu i oprogramowania, adaptując je do własnych potrzeb. Artykuł omawia tendencje rozwojowe, na przykładzie sytuacji występującej w przypadku produkcji przyrostowej, gdzie BN są wykorzystywane w czasie rzeczywistym do sterowania sprzężeniem zwrotnym, zapewniającym jakość. Omówiono zostaną dwie innowacje: jedną z nich jest nieliniowe zjawisko lokalnych rezonansów wokół defektów w materiałach wiskoelastycznych, a drugim są lasery o emisji powierzchniowej z pionową wnęką rezonansową (VCSEL), nowe, mocne i adaptywne źródło wzbudzenia w badaniach termograficznych.
    Źródło:
    Badania Nieniszczące i Diagnostyka; 2020, 1-4; 6-17
    2451-4462
    2543-7755
    Pojawia się w:
    Badania Nieniszczące i Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-11 z 11

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