Search results

1

Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction

100%

Duda J., Gurgul H., Syrek R.

Statistics in Transition new series

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2020

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vol. 21

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issue 5

99-118

EN

While we would like to predict exact values, the information available, being incomplete, is rarely sufficient - usually allowing only conditional probability distributions to be predicted. This article discusses hierarchical correlation reconstruction (HCR) methodology for such a prediction using the example of bid-ask spreads (usually unavailable), but here predicted from more accessible data like closing price, volume, high/low price and returns. Using HCR methodology, as in copula theory, we first normalized marginal distributions so that they were nearly uniform. Then we modelled joint densities as linear combinations of orthonormal polynomials, obtaining their decomposition into mixed moments. Then we modelled each moment of the predicted variable separately as a linear combination of mixed moments of known variables using least squares linear regression. By combining these predicted moments, we obtained the predicted density as a polynomial, for which we can e.g. calculate the expected value, but also the variance to determine the uncertainty of the prediction, or we can use the entire distribution for, e.g. more accurate further calculations or generating random values. 10-fold cross-validation log-likelihood tests were conducted for 22 DAX companies, leading to very accurate predictions, especially when individual models were used for each company, as significant differences were found between their behaviours. An additional advantage of using this methodology is that it is computationally inexpensive; estimating and evaluating a model with hundreds of parameters and thousands of data points by means of this methodology takes only a second on a computer.

2

Supervisory optimal control using machine learning for building thermal comfort

100%

Abdufattokhov S., Mahamatov N., Ibragimova K., Gulyamova D., Yuldashev D.

Operations Research and Decisions

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2022

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vol. 32

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issue 4

EN

For the past few decades, control and building engineering communities have been focusing on thermal comfort as a key factor in designing sustainable building evaluation methods and tools. However, estimating the indoor air temperature of buildings is a complicated task due to the nonlinear and complex building dynamics characterised by the time-varying environment with disturbances. The primary focus of this paper is designing a predictive and probabilistic room temperature model of buildings using Gaussian processes (GPs) and incorporating it into model predictive control (MPC) to minimise energy consumption and provide thermal comfort satisfaction. The full probabilistic capabilities of GPs are exploited from two perspectives: the mean prediction is used for the room temperature model, while the uncertainty is involved in the MPC objective not to lose the desired performance and design a robust controller. We illustrated the potential of the proposed method in a numerical example with simulation results.

3

Clustering Methods Applied to Reduce the Training Sample Size in Support Vector Machines

100%

Trzęsiok M., The Karol Adamiecki University of Economics in Katowice

Acta Universitatis Lodziensis. Folia Oeconomica

|

2009

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vol. 225

EN

Support vector machines belong to the group of methods of supervised learning. They generate non-linear models with good generalization abilities. The core of SVMs algorithm is the quadratic program which is solved for obtaining the optimal separating hyperplane. Because finding the solution of this quadratic program is computationally expensive, SVMs are not feasible for very large data sets. As a solution Wang, Wu and Zhang (2005) suggested to combine the AT-means clustering technique with SVMs to reduce the number of support vectors. The paper presents a common approach using K-medoids and compares it with the original SVMs.

PL

Metoda wektorów nośnych jest metodą dyskryminacji generującą nieliniowe modele o dużym stopniu uogólnienia (małych błędach klasyfikacji na zbiorach testowych). Jednak ze względu na dużą złożoność obliczeniową, związaną z koniecznością rozwiązania zadania optymalizacji wypukłej, które jest podstawowym elementem algorytmu metody, stosowanie metody, szczególnie w przypadku zbiorów uczących o dużej liczebności, nie zawsze jest możliwe. Złożoność obliczeniowa algorytmu metody wektorów nośnych zależy przede wszystkim od liczby obserwacji w zbiorze uczącym. Jako rozwiązanie tego problemu Wang, Wu i Zhang zaproponowali pogrupowanie danych ze zbioru uczącego za pomocą taksonomicznej metody AT-średnich i zastosowanie metody wektorów nośnych na dużo mniej licznym zbiorze środków ciężkości tak otrzymanych klas. W artykule przedstawiona została ocena analogicznego podejścia, wykorzystującego do grupowania metodę K-medoidów oraz porównanie z oryginalną metodą wektorów nośnych.

4

Extreme gradient boosting method in the prediction of company bankruptcy

100%

Pawełek B.

Statistics in Transition new series

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2019

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vol. 20

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issue 2

155-171

EN

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.

5

Sztuczna inteligencja w problematyce modeli oceny ryzyka w instytucjach finansowych z perspektywy prawno-regulacyjnej

100%

Nowakowski M., Waliszewski K.

Finanse i Prawo Finansowe

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2022

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vol. 1

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issue 33

119-141

EN

Purpose of the article / hypothesis: This article aims to verify the need to introduce additional legal and regulatory requirements in relation to the models used in banks, including, in particular, risk assessment models. At the same time, the article analyzes the need for possible introduction of sector-specific guidelines, or the need to include the above-mentioned models in the classification of high-risk artificial intelligence systems, referred to in the draft EU regulation on artificial intelligence. Methodology: The article is based on an analysis of the available literature on the subject, legal acts as well as regulations and standards developed both at the local and international level. Research results / results: The issue of the application of models in the financial sector, mainly banking, is of significant importance from the perspective of the regulator and supervisor. Quality, compliance with the regulations, but also efficiency and effective supervision may constitute the (instability) of a given financial institution, the instability of which may be a component – at least potentially – of systemic risk. Banks commonly use internal models that generally allow the calculation of capital requirements to cover specific risks in a bank’s business, such as credit risk or market risk. Internal models have been evolving for years and are undoubtedly becoming more and more accurate (they predict with a greater probability the occurrence of certain events), although they are still only certain assumptions that reality can verify, as evidenced by financial crises that have already occurred in the past as well as failures of banks considered to be stable. At the same time, the development of new technologies, in particular the so-called artificial intelligence makes institutions more and more willing to use various models, e.g. machine learning, to support these models and obtain theoretically better results. The European Union, but also other jurisdictions are considering or already introducing specific legal and regulatory solutions that are to introduce clear rules related to the use of certain artificial intelligence systems, including those used by financial institutions. As a result, institutions – already burdened with significant regulatory requirements, may soon be obliged to go through another "health path" of a legal and regulatory compliance nature.

6

A comparative study of corporate credit ratings prediction with machine learning

100%

DOĞAN S., BÜYÜKKÖR Y., ATAN M.

Operations Research and Decisions

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2022

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vol. 32

|

issue 1

25-47

EN

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.

7

Assessment of Young People’s Attitudes Towards Sustainable Entrepreneurship with Machine Learning Techniques Use

100%

Mroczek T., Kaszuba-Perz A., Czyżewska M.

Horyzonty Polityki

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2022

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vol. 13

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issue 42

187-202

EN

RESEARCH OBJECTIVE: The main purpose of the research is to assess whether the young generation in Poland has the potential to build sustainable companies by evaluating the attitudes of university students towards sustainable entrepreneurship.. THE RESEARCH PROBLEM AND METHODS: The readiness to create a sustainable enterprise was assessed on the basis of self-assessment of pro-social attitudes, business intentions and knowledge of social and environmental aspects in business with the use of multidimensional analysis based on machine learning methods. THE PROCESS OF ARGUMENTATION: A significant challenge for policymakers, scientists and entrepreneurs is solving important social and environmental problems through the development of sustainable entrepreneurship. The implementation of this concept requires efforts to educate and shape pro-social attitudes, especially among the young generation. Therefore, the research focuses on identifying and assessing the attitudes and awareness level of pro-social aspects in business and recognition of entrepreneurial intentions among representatives of this generation. RESEARCH RESULTS: The research results show that young people have an intuitive sense of what sustainable business is all about, but the formal knowledge in this area is low. Financial aspects, i.e. the possibility of obtaining significant income from own business, as well as independence and the possibility of being a manager, turned out to be much more motivating for entrepreneurship than the possibility of changing the world for the better, helping local communities or protecting the environment. CONCLUSIONS, INNOVATIONS AND RECOMMENDATIONS: The innovative approach to multidimensional data analysis highlighted the lack of knowledge and insufficient level of pro-social attitudes among the young generation, which is a particularly worrying phenomenon in the context of formulated challenges and social and environmental needs.

8

Francesco Marconi (2020). Newsmakers: Artificial Intelligence And The Future Of Journalism

100%

Baranowski P.

Central European Journal of Communication

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2021

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vol. 14

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issue 2(29)

357-360

EN

This is the review of the book by Francesco Marconi "Newsmakers: Artificial Intelligence and the Future of Journalism."

9

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

88%

Chlebus M., Dyczko M., Woźniak M.

Central European Economic Journal

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2021

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vol. 8

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issue 55

EN

Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.

10

Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland

88%

Dzik-Walczak A., Odziemczyk M.

Central European Economic Journal

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2021

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vol. 8

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issue 55

352-377

EN

The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for convolutional networks were logit models, random forests, XGBoost, and dense neural networks. Hyperparameters and model architecture were selected based on a random search and analysis of learning curves and experiments in folded, stratified cross-validation. In addition, the sensitivity of the results to data preprocessing was investigated. It was found that convolutional neural networks can be used to analyze cross-sectional tabular data, especially for the problem of modelling the probability of corporate bankruptcy. In order to achieve good results with models based on parameters updated by a gradient (neural networks and logit), it is necessary to use appropriate preprocessing techniques. Models based on decision trees have been shown to be insensitive to the data transformations used.

11

The prediction of new Covid-19 cases in Poland with machine learning models

88%

Chwila A.

Statistics in Transition new series

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2023

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vol. 24

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issue 2

59-83

EN

The COVID-19 pandemic has had a huge impact both on the global economy and on everyday life in all countries all over the world. In this paper, we propose several possible machine learning approaches to forecasting new confirmed COVID-19 cases, including the LASSO regression, Gradient Boosted (GB) regression trees, Support Vector Regression (SVR), and Long-Short Term Memory (LSTM) neural network. The above methods are applied in two variants: to the data prepared for the whole Poland and to the data prepared separately for each of the 16 voivodeships (NUTS 2 regions). The learning of all the models has been performed in two variants: with the 5-fold time-series cross-validation as well as with the split into the single train and test subsets. The computations in the study used official statistics from government reports from the period of April 2020 to March 2022. We propose a setup of 16 scenarios of the model selection to detect the model characterized by the best ex-post prediction accuracy. The scenarios differ from each other by the following features: the machine learning model, the method for the hyperparameters selection and the data setup. The most accurate scenario for the LASSO and SVR machine learning approaches is the single train/test dataset split with data for the whole Poland, while in case of the LSTM and GB trees it is the cross validation with data for whole Poland. Among the best scenarios for each model, the most accurate ex-post RMSE is obtained for the SVR. For the model performing best in terms of the ex-post RMSE, the interpretation of the outcome is conducted with the Shapley values. The Shapley values make it possible to present the impact of auxiliary variables in the machine learning model on the actual predicted value. The knowledge regarding factors that have the strongest impact on the number of new infections can help companies to plan their economic activity during turbulent times of pandemics. We propose to identify and compare the most important variables that affect both the train and test datasets of the model.

12

Application of ensemble gradient boosting decision trees to forecast stock price on WSE

88%

Dadej M.

Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”

|

2019

|

issue 9

265-275

EN

The main purpose of this article is to apply machine learning model based on ensemble of gradient boosted decision trees to forecast direction of share prices of Bank Handlowy S.A listed on WSE. In the introduction, the author presented the context of machine learning and its application in forecasting stock prices. Afterwards, the author describes the process of building classification model which uses XGboost framework from data preprocessing to model evaluation. The input features of the model were technical analysis indicators, like stochastic oscillators or moving averages. Output of the model was a direction of stock price after one week. The accuracy of the model based on testing dataset is 72%. The author also performed a simulation, based on the model. The simulation was made with the Monte Carlo method which stochastic process had a Laplace distribution. During interpretation, at the end, the author pointed limitations of model and algorithmic trading strategy evaluation techniques based on backtest.

PL

Celem niniejszego artykułu jest wykorzystanie modelu z dziedziny uczenia maszynowego opartego na algorytmie zespołu wzmocnionych gradientowo drzew decyzyjnych do prognozowania kierunku zmian kursu akcji Banku Handlowego S.A. notowanego na GPW. We wstępie został przedstawiony kontekst uczenia maszynowego oraz wykorzystania go do prognozowania cen akcji. Następnie, przedstawiono proces tworzenia modelu klasyfikacyjnego wykorzystujący strukturę XGboost od etapu przetwarzania danych do jego ewaluacji. Danymi wejściowymi modelu były wskaźniki wykorzystywane w analizie technicznej, m.in. oscylatory stochastyczne oraz średnie ruchome, natomiast danymi wyjściowymi były kierunki zmian kursu na przestrzeni następnego tygodnia. Skuteczność modelu na danych testowych wyniosła 72%. Na końcu przeprowadzono symulacje portfela inwestycyjnego, podejmującego decyzje o transakcjach na podstawie wcześniej stworzonego modelu, wykorzystując metodę Monte Carlo w której dynamika procesów stochastycznych miała rozkład Laplace’a. Przy interpretacji wyników portfela inwestycyjnego wskazano ograniczenia ewaluacji modelu i strategii inwestycyjnej opartej o backtest.

13

How to fly to safety without overpaying for the ticket

88%

Kaczmarek T., Grobelny P.

Economics and Business Review

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2023

|

vol. 9

|

issue 2

160-183

EN

For most active investors treasury bonds (govs) provide diversification and thus reduce the risk of a portfolio. These features of govs become particularly desirable in times of elevated risk which materialize in the form of the flight-to-safety (FTS) phenomenon. The FTS for govs provides a shelter during market turbulence and is exceptionally beneficial for portfolio drawdown risk reduction. However what if the unsatisfactory expected return from treasuries discourages higher bonds allocations? This research proposes a solution to this problem with Deep Target Volatility Equity-Bond Allocation (DTVEBA) that dynamically allocate portfolios between equity and treasuries.

14

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

88%

Bejger S., Fiszeder P.

Przegląd Statystyczny

|

2021

|

vol. 68

|

issue 3

1-15

EN

We combine machine learning tree-based algorithms with the usage of low and high prices and suggest a new approach to forecasting currency covariances. We apply three algorithms: Random Forest Regression, Gradient Boosting Regression Trees and Extreme Gradient Boosting with a tree learner. We conduct an empirical evaluation of this procedure on the three most heavily traded currency pairs in the Forex market: EUR/USD, USD/JPY and GBP/USD. The forecasts of covariances formulated on the three applied algorithms are predominantly more accurate than the Dynamic Conditional Correlation model based on closing prices. The results of the analyses indicate that the GBRT algorithm is the bestperforming method.

15

Artificial Intelligence in Audit

75%

Karmańska A.

Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu

|

2022

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vol. 66

|

issue 4

87-99

PL

Celem artykułu jest wskazanie korzyści płynących z zastosowania sztucznej inteligencji (AI) w badaniu sprawozdań finansowych. Posłużono się kwestionariuszem ankiety. Próbą badawczą objęto 206 praktyków i studentów audytu i rachunkowości. Zastosowano analizę czynnikową metodą głównych składowych z rotacją Promax. Wyniki wskazują, że w opinii respondentów zastosowanie sztucznej inteligencji zwiększa efektywność audytu. Sztuczna inteligencja usprawnia komunikację i obsługę klienta. Ponadto AI może zautomatyzować czasochłonne i rutynowe zadania. Powyższe trzy czynniki odpowiadają za 62,223% wariancji. Wyniki badania wskazują na korzyści płynące z implementacji sztucznej inteligencji w audycie i mogą wspierać menedżerów we wdrażaniu nowych technologii w ich organizacjach. Ograniczeniem badawczym jest fakt, że badanie koncentruje się na respondentach jedynie z Polski.

EN

The main objective of this paper is to identify the benefits of applying the Artificial Intelligence (AI) in the audit sector. The study employed a questionnaire for a research sample including 206 auditing and accounting practitioners and students. Data were collected via an online survey. A principal axis factor analysis with the Promax rotation was conducted to assess the underlying structure for the points of the questionnaire. The research outcomes indicate that, in the opinion of the respondents, AI adoption increases audit efficiency, and enhances client communication and service. Finally, AI can also automate time-consuming and routine tasks. The three indicated factors account for 62.223% variance. The findings reveal the advantages of AI adoption and could support managers in deploying new technology in their organizations. The research limitation concerns the fact that this study focused only on respondents from Poland.

16

Yet another research on GANs in cybersecurity

75%

Zimoń M., Kasprzyk R.

Cybersecurity and Law

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2023

|

vol. 9

|

issue 1

61-72

EN

Deep learning algorithms have achieved remarkable results in a wide range of tasks, including image classification, language translation, speech recognition, and cybersecurity. These algorithms can learn complex patterns and relationships from large amounts of data, making them highly effective for many applications. However, it is important to recognize that models built using deep learning are not fool proof and can be fooled by carefully crafted input samples. This paper presents the results of a study to explore the use of Generative Adversarial Networks (GANs) in cyber security. The results obtained confirm that GANs enable the generation of synthetic malware samples that can be used to mislead a classification model.

17

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

75%

Zekić-Sušac M., Pfeifer S., Šarlija N.

Business Systems Research Journal

|

2014

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vol. 5

|

issue 3

82-96

EN

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

18

Supporting the Age-Period-Cohort model of default rate prediction with interpretable machine learning

75%

Kwiatkowski M. P.

Przegląd Statystyczny

|

2023

|

vol. 70

|

issue 1

54-78

EN

Regular short-term forecasting of defaults is a basic activity of a retail portfolio risk manager. From a business perspective, not only the quality of the forecast is significant, but also the understanding of the trends and their driving factors. The vintage analysis and a more advanced Age-Period-Cohort approach are popular tools used for the purpose. The aim of this article is to demonstrate that interpretable machine learning can support the Age-PeriodCohort approach, facilitating forecasting beyond the time range of training data, eliminating the model identification problem and attributing cohort quality to the specific characteristics of loans approved in a given month. The study is based on real consumer finance portfolios from the Polish market.

19

Personality modifies the effect of post-traumatic stress disorder (PTSD) and society support on depression-anxiety-stress in the residents undergone catastrophic flooding in Henan, China

75%

Jing X., Lu L., Yao Y.

Medycyna Pracy

|

2022

|

vol. 73

|

issue 4

305-314

EN

Background: To analyze the impact of the flood disasters, social support and personality on the mental health of residents in Henan Province, China, providing fundamental knowledges for making measuring strategies to improve the psychological protection and anti-stress ability of the residents after the disaster. Material and Methods: A cross-section study was conducted via an online survey platform “questionnaire star,” which included 572 residents in Henan Province, which underwent the history of ever flood disaster on July 20. The questionnaires of Impact of Event Scale-Revised Edition (IES-R), Perceived Social Support Scale (PSSS), the Depression Anxiety Stress Scales (DASS-21) and the scales of Eysenck Personality Questionnaire-Revised (EPQ-R) version in Chinese were also administered to each participant. Generalized linear regression model was performed. Results: The residents who live in the flooding areas, are male and married had a significantly higher post-traumatic stress disorder (PTSD) score than their counterparts. The scores of depression-anxiety-stress in the residents with stable emotion were significantly lower than those with unstable emotion (p < 0.001). Machine learning showed that PTSD ranked the top risk factor, followed by neuroticism for Depression-Anxiety-Stress after disaster. The PTSD was negatively correlated with social support (p < 0.01), while it was positively correlated with depression-anxiety-stress and emotional stability (p < 0.01). There was a statistically significant interaction between PTSD, social support and neuroticism on depression-anxiety-stress (p < 0.001), with an independent effect of 1.4% on depression-anxiety-stress. Emotional stability showed the largest association with depression-anxiety-stress. Conclusions: Residents living in the catastrophic flooding areas had significant post-traumatic mental health issues, and the severity of mental problems was differently affected by post-traumatic stress disorder and social support in individuals with different personalities. Introvert and PTSD were the major risk factors for depression-anxiety-stress after the disaster.

20

Temporal Expressions in Polish Corpus KPWr

75%

Kocoń J., Marcińczuk M., Oleksy M., Bernaś T., Wolski M.

Cognitive Studies

|

2015

|

issue 15

EN

Temporal Expressions in Polish Corpus KPWrThis article presents the result of the recent research in the interpretation of Polish expressions that refer to time. These expressions are the source of information when something happens, how often something occurs or how long something lasts. Temporal information, which can be extracted from text automatically, plays significant role in many information extraction systems, such as question answering, discourse analysis, event recognition and many more. We prepared PLIMEX - a broad description of Polish temporal expressions with annotation guidelines, based on the state-of-the-art solutions for English, mainly TimeML specification. We also adapted the solution to capture the local semantics of temporal expressions, called LTIMEX. Temporal description also supports further event identification and extends event description model, focusing at anchoring events in time, ordering events and reasoning about the persistence of events. We prepared the specification, which is designed to address these issues and we annotated all documents in Polish Corpus of Wroclaw University of Technology (KPWr) using our annotation guidelines.

Refine search results

Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction

Supervisory optimal control using machine learning for building thermal comfort

Clustering Methods Applied to Reduce the Training Sample Size in Support Vector Machines

Extreme gradient boosting method in the prediction of company bankruptcy

Sztuczna inteligencja w problematyce modeli oceny ryzyka w instytucjach finansowych z perspektywy prawno-regulacyjnej

A comparative study of corporate credit ratings prediction with machine learning

Assessment of Young People’s Attitudes Towards Sustainable Entrepreneurship with Machine Learning Techniques Use

Francesco Marconi (2020). Newsmakers: Artificial Intelligence And The Future Of Journalism

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland

The prediction of new Covid-19 cases in Poland with machine learning models

Application of ensemble gradient boosting decision trees to forecast stock price on WSE

How to fly to safety without overpaying for the ticket

Forecasting currency covariances using machine learning tree-based algorithms with low and high prices

Artificial Intelligence in Audit

Yet another research on GANs in cybersecurity

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Supporting the Age-Period-Cohort model of default rate prediction with interpretable machine learning

Personality modifies the effect of post-traumatic stress disorder (PTSD) and society support on depression-anxiety-stress in the residents undergone catastrophic flooding in Henan, China

Temporal Expressions in Polish Corpus KPWr