Temat: prediction accuracy

Skocz do pozycji: 1.

Tytuł:: Studies on additive properties of some processing operations
Studium addytywności procesów przeróbczych
Autorzy:: Malewski, J.
Tematy:: mineral processing
operation modeling
simulation
prediction accuracy; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Wydział Geoinżynierii, Górnictwa i Geologii. Instytut Górnictwa
Powiązania:: https://bibliotekanauki.pl/articles/89216.pdf Link otwiera się w nowym oknie
Opis:: The accuracy of prediction of mineral processing operation by partial effects summation is analyzed. Methodological and practical aspects of the problem are discussed using laboratory studies of selected comminution and classifying operations as examples. Laboratory experiments show that the effects of examined mineral processing operation depend on simultaneously running processes of classification and comminution. The influence rate of interaction between both processes on the final results is significant, but in some cases it may be neglected. The obtained results have preliminary character and needed a further verification.
W artykule przedstawiono zagadnienie nieaddytywności procesów przeróbczych, jakie występuje w realnych procesach i które powinno być brane pod uwagę w przypadku konstruowania matematycznych modeli efektywności operacji uwzględniających wpływ składu ziarnowego. W warunkach laboratoryjnych wykonano testy i pomiary wydajności i składu ziarnowego na sześciu różnych maszynach: kruszarkach, młynach, przesiewaczu wibracyjnym i klasyfikatorze powietrznym. Badania pokazały, że w przypadku wydajności maszyn rozdrabiających i sprawności przesiewania modele tych operacji powinny uwzględniać nieaddytywność procesu.. Natomiast różnice (dP) w składzie ziarnowym produktów rozdrabiania były niewielkie i nie przekraczały trzech procent.
Dostawca treści:: Biblioteka Nauki

Artykuł

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Skocz do pozycji: 2.

Tytuł:: Bankruptcy models : verifying their validity as a predictor of corporate failure
Modele upadłości : weryfikowanie ich ważności jako czynnik prognostyczny niepowodzenia korporacyjnego
Autorzy:: Kovacova, M.
Kliestik, T.
Kubala, P.
Valaskova, K.
Radišić, M.
Borocki, J.
Tematy:: bankruptcy
company
prediction accuracy
upadłość
przedsiębiorstwo
dokładność przewidywania; Pokaż więcej
Wydawca:: Politechnika Częstochowska
Powiązania:: https://bibliotekanauki.pl/articles/406179.pdf Link otwiera się w nowym oknie
Opis:: Although the issue of corporate failure analysis is a hot topic for business research since the last century, even nowadays there are numerous researches focusing on assessing the financial health of companies. Within increasing internationalization and globalization the demand for bankruptcy prediction is important not only for owners of the companies, but also for other interested groups. We aim to test the validity of prediction models developed as partial results of our research project. Bankruptcy prediction models were constructed on the data set of Slovak companies covering the year 2015 and based on the various statistical methodologies. We provided the validity of these models and their prediction accuracy on the data set of Slovak companies covering the following year 2016.
Chociaż kwestia analizy niepowodzenia korporacyjnego jest gorącym tematem badań biznesowych od zeszłego wieku, nawet obecnie prowadzone są liczne badania skupiające się na ocenie kondycji finansowej firm. W warunkach rosnącej internacjonalizacji i globalizacji zapotrzebowanie na prognozy bankructwa jest ważne nie tylko dla właścicieli firm, ale także dla innych zainteresowanych grup. Celem artykułu jest sprawdzenie ważności modeli prognostycznych opracowanych jako częściowe wyniki projektu badawczego przez autorów. Modele przewidywania bankructwa zostały zbudowane na zbiorze danych słowackich firm w roku 2015 na podstawie różnych metodologii statystycznych. Zapewniona została poprawność tych modeli i dokładność ich prognozowania na zbiorze danych słowackich firm obejmująca rok 2016.
Dostawca treści:: Biblioteka Nauki

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na półce

Skocz do pozycji: 3.

Tytuł:: Investigating the impact of age-depended hair colour darkening during childhood on DNA-based hair colour prediction with the HIrisPlex system
Autorzy:: Rosset, Iwona
Karłowska-Pik, Joanna
Pośpiech, Ewelina
Sitek, Aneta
Spólnicka, Magdalena
Branicki, Wojciech
Kukla-Bartoszek, Magdalena
Kayser, Manfred
Walsh, Susan
Strapagiel, Dominik
Żądzińska, Elżbieta
Sobalska-Kwapis, Marta
Słomka, Marcin
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 4.

Tytuł:: Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
Autorzy:: Trostianchyn, A.M.
Izonin, I.V.
Duriagina, Z.A.
Tkachenko, R.O.
Kulyk, V.V.
Havrysh, B.M.
Tematy:: Sm-Co alloy
ensemble learning
gradient boosting
prediction accuracy
Stop Sm-Co
uczenie zespołowe
dokładność przewidywania; Pokaż więcej
Wydawca:: Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Powiązania:: https://bibliotekanauki.pl/articles/24200577.pdf Link otwiera się w nowym oknie
Opis:: Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites
Autorzy:: Fan, Chengkai
Zhang, Na
Jiang, Bei
Liu, Wei Victor
Tematy:: kopalnia
samochód ciężarowy
piasek roponośny
oil sands mining
mine truck productivity
Gaussian mixture model
latent variable
prediction accuracy
relative importance; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2203342.pdf Link otwiera się w nowym oknie
Opis:: The historical datasets at operating mine sites are usually large. Directly applying large datasets to build prediction models may lead to inaccurate results. To overcome the real-world challenges, this study aimed to handle these large datasets using Gaussian mixture modelling (GMM) for developing a novel and accurate prediction model of truck productivity. A large dataset of truck haulage collected at operating mine sites was clustered by GMM into three latent classes before the prediction model was built. The labels of these latent classes generated a latent variable. Two multiple linear regression (MLR) models were then constructed, including the ordinary-MLR (O-MLR) and the hybrid GMM-MLR models. The GMM-MLR model incorporated the observed input variables and a latent variable in the form of interaction terms. The O-MLR model was the baseline model and did not involve the latent variable. The GMM-MLR model performed considerably better than the O-MLR model in predicting truck productivity. The interaction terms quantitatively measured the differences in how the observed input variables affected truck productivity in three classes (high, medium, and low truck productivity). The haul distance was the most crucial input variable in the GMM-MLR model. This study provides new insights into handling massive amounts of data in truck haulage datasets and a more accurate prediction model for truck productivity.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 6.

Tytuł:: Uwagi o rejestracji przebiegów czasowych z jednoczesną prognozą ich zmian
The Remarks on Recording of Time Variable Signals with Simultaneous Prediction of Signal Changes
Autorzy:: Żuchowski, A.
Tematy:: rejestracja przebiegów
prognozy
dokładność prognoz
signal recording
prediction
accuracy of prediction; Pokaż więcej
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Powiązania:: https://bibliotekanauki.pl/articles/154094.pdf Link otwiera się w nowym oknie
Opis:: Rozpatrzono koncepcję rejestracji przebiegów czasowych y(t) z jednoczesnym zapisem prognozy ich zmian y(t+t_p) w pewnym przedziale od 0≤t_p≤t_p _max. Rozpatrzono kilka koncepcji procedur prognozowania, ich błędy i zakres t_p _max.
The idea for recording of time variable signals y(t) with simultaneous recording of signal prediction y(t+t_p) for certain from 0≤t_p≤t_p _max as been considered in the paper. The errors and ranges of t_p _max for several ideas of prediction procedures have been discussed.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 7.

Tytuł:: Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
Autorzy:: Puchala, Dariusz
Stokfiszewski, Kamil
Wieloch, Kamil
Tematy:: graphics processing unit
GPU
execution time prediction model
discrete wavelet transform
DWT
lattice structure
convolution-based approach
orthogonal transform
orthogonal filter banks
time effectiveness
prediction accuracy; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2173530.pdf Link otwiera się w nowym oknie
Opis:: Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 8.

Tytuł:: Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
Autorzy:: Puchala, Dariusz
Stokfiszewski, Kamil
Wieloch, Kamil
Tematy:: graphics processing unit
GPU
execution time prediction model
discrete wavelet transform
DWT
lattice structure
convolution-based approach
orthogonal transform
orthogonal filter banks
time effectiveness
prediction accuracy
procesor graficzny
model przewidywania czasu wykonania
dyskretna transformata falkowa
struktura sieciowa
podejście oparte na splotach
przekształcenia ortogonalne
ortogonalne banki filtrów
efektywność czasowa
dokładność przewidywania; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2173537.pdf Link otwiera się w nowym oknie
Opis:: Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 9.

Tytuł:: Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
Autorzy:: Puchala, Dariusz
Stokfiszewski, Kamil
Wieloch, Kamil
Tematy:: graphics processing unit
GPU
execution time prediction model
discrete wavelet transform
DWT
lattice structure
convolution-based approach
orthogonal transform
orthogonal filter banks
time effectiveness
prediction accuracy
procesor graficzny
model przewidywania czasu wykonania
dyskretna transformata falkowa
struktura sieciowa
podejście oparte na splotach
przekształcenia ortogonalne
ortogonalne banki filtrów
efektywność czasowa
dokładność przewidywania; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2173635.pdf Link otwiera się w nowym oknie
Opis:: Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 10.

Tytuł:: Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
Autorzy:: Puchala, Dariusz
Stokfiszewski, Kamil
Wieloch, Kamil
Tematy:: graphics processing unit
GPU
execution time prediction model
discrete wavelet transform
DWT
lattice structure
convolution-based approach
orthogonal transform
orthogonal filter banks
time effectiveness
prediction accuracy
procesor graficzny
model przewidywania czasu wykonania
dyskretna transformata falkowa
struktura sieciowa
podejście oparte na splotach
przekształcenia ortogonalne
ortogonalne banki filtrów
efektywność czasowa
dokładność przewidywania; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2173636.pdf Link otwiera się w nowym oknie
Opis:: Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Dostawca treści:: Biblioteka Nauki

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