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Tytuł:
Assessing the efficiency of a random forest regression model for estimating water quality indicators
Autorzy:
Zavareh, Maryam
Maggioni, Viviana
Zhang, Xinxuan
Powiązania:
https://bibliotekanauki.pl/articles/27810498.pdf
Data publikacji:
2023
Wydawca:
Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:
Random Forest
water quality
hydrometeorological information
Opis:
This work evaluates the efficiency of Random Forest (RF) regression for predicting water quality indicators and investigates factors affecting water quality in 11 watersheds in Virginia, District of Columbia, and Maryland. Ten years of daily water quality data along with hydro-meteorological information (such as precipitation) and watershed physiology and characteristics (e.g., size, soil type, land use) are used to predict dissolved oxygen (DO), specific conductivity (K), and turbidity (Tu) across the selected watersheds. The RF regression model is developed for six scenarios, with an increasing number of predictors introduced in each scenario. The first scenario contains the smallest amount of information (water quality indicators DO, K and Tu), while scenario 6 contains all the available variables. The RF model is evaluated based on three statistical metrics: the relative root mean square error, the correlation coefficient, and the percentage of variance explained. In addition, the degree of importance for each predictor is used to rank their importance within each scenario. The model shows excellent performance for DO as the predicted variable. The model predicting K slightly outperforms the one predicting Tu. Scenario 4 (built based on water quality indicators, hydro-meteorological data, watershed physiology and land cover information) provided the best tradeoff between performance and efficiency (quantified in terms of the amount of information needed to develop the model). In conclusion, based on the RF model, land cover plays a significant role in predicting water quality indicators. In addition, the developed RF regression model is adaptable to watersheds in this region over a range of climates.
Źródło:
Meteorology Hydrology and Water Management. Research and Operational Applications; 2023, 11, 2; 1--18
2299-3835
2353-5652
Pojawia się w:
Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Impacts of forest spatial structure on variation of the multipath phenomenon of navigation satellite signals
Autorzy:
Brach, Michał
Stereńczak, Krzysztof
Bolibok, Leszek
Kwaśny, Łukasz
Krok, Grzegorz
Laszkowski, Michał
Powiązania:
https://bibliotekanauki.pl/articles/2044153.pdf
Data publikacji:
2019
Wydawca:
Instytut Badawczy Leśnictwa
Tematy:
GNSS
multipath
random forest
Borut
forest structure
LiDAR
Opis:
The GNSS (Global Navigation Satellite System) receivers are commonly used in forest management in order to determine objects coordinates, area or length assessment and many other tasks which need accurate positioning. Unfortunately, the forest structure strongly limits access to satellite signals, which makes the positioning accuracy much weak comparing to the open areas. The main reason for this issue is the multipath phenomenon of satellite signal. It causes radio waves reflections from surrounding obstacles so the signal do not reach directly to the GNSS receiver’s antenna. Around 50% of error in GNSS positioning in the forest is because of multipath effect. In this research study, an attempt was made to quantify the forest stand features that may influence the multipath variability. The ground truth data was collected in six Forest Districts located in different part of Poland. The total amount of data was processed for over 2,700 study inventory plots with performed GNSS measurements. On every plot over 25 forest metrics were calculated and over 25 minutes of raw GNSS observations (1500 epochs) were captured. The main goal of this study was to find the way of multipath quantification and search the relationship between multipath variability and forest structure. It was reported that forest stand merchantable volume is the most important factor which influence the multipath phenomenon. Even though the similar geodetic class GNSS receivers were used it was observed significant difference of multipath values in similar conditions.
Źródło:
Folia Forestalia Polonica. Series A . Forestry; 2019, 61, 1; 3-21
0071-6677
Pojawia się w:
Folia Forestalia Polonica. Series A . Forestry
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting immunogenicity in murine hosts with use of Random Forest classifier
Przewidywanie immunogenności u myszy przy użyciu klasyfikatora Random Forest
Autorzy:
Marciniak, Anna
Tarczewska, Martyna
Kloska, Sylwester
Powiązania:
https://bibliotekanauki.pl/articles/2016293.pdf
Data publikacji:
2020
Wydawca:
Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB
Tematy:
Random Forest Classifier
immunogenicity
machine learning
entropy
Gini index
klasyfikator Random Forest
immunogenność
uczenie maszynowe
entropia
Opis:
Biomedical data are difficult to interpret due to their large amount. One of the solutions to cope with this problem is to use machine learning. Machine learning can be used to capture previously unnoticed dependencies. The authors performed random forest classifier with entropy and Gini index criteria on immunogenicity data. Input data consisted of 3 columns: epitope (8-11 amino acids long peptide), major histocompatibility complex (MHC) and immune response. Presented model can predict the immune response based on epitope-MHC complex. Achieved results had accuracy of 84% for entropy and 83% for Gini index. The results are not fully satisfying but are a fair start for more complexed experiments and could be used as an indicator for further research.
Dane biomedyczne są trudne do interpretacji ze względu na ich dużą ilość. Jednym z rozwiązań radzenia sobie z tym problemem jest wykorzystanie uczenia maszynowego. Techniki te umożliwiają wychwycenie wcześniej niezauważonych zależności. W artykule przedstawiono wykorzystanie klasyfikatora Random Forest z kryterium entropii i indeksem Gini na danych dotyczących immunogenności. Dane wejściowe składają się z 3 kolumn: epitop (peptyd o długości 8-11 aminokwasów), główny kompleks zgodności tkankowej (MHC) i odpowiedź immunologiczna. Zaprezentowany model przewiduje odpowiedź immunologiczną na podstawie kompleksu epitop-MHC. Uzyskane wyniki osiągnęły dokładność na poziomie 84% (entropia) i 83% (indeks Gini). Wyniki nie są w pełni satysfakcjonujące, ale stanowią dobry początek dla bardziej złożonych eksperymentów i wyznacznik do dalszych badań.
Źródło:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2020, 24; 31-43
1899-0088
Pojawia się w:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification of Seizure Types Using Random Forest Classifier
Autorzy:
Basri, Ashjan
Arif, Muhammad
Powiązania:
https://bibliotekanauki.pl/articles/2123290.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
EEG
fast fourier transform
seizure
random forest
Opis:
Epilepsy is one of the most common mental disorders in the world, affecting 65 million people. The prevalence in Arab countries of Epilepsy is estimated at 174 per 100,000 individuals, and in Saudi Arabia is 6.54 per 1,000 individuals. Epilepsy seizures have different types, and each patient needs to have a treatment plan according to the seizure type. Hence, accurate classification of seizure type is an essential part of diagnosing and treating epileptic patients. In this paper, features based on fast Fourier transform from EEG montages are used to classify different types of seizures. Since the distribution of classes is not uniform and the dataset suffers from severe imbalance. Various algorithms are used to under-sample the majority class and over-sample the minority classes. Random forest classifier produced classification accuracy of 96% to differentiate three types of seizures from the healthy EEG reading.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 3; 167--178
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel drift detection algorithm based on features’ importance analysis in a data streams environment
Autorzy:
Duda, Piotr
Przybyszewski, Krzysztof
Wang, Lipo
Powiązania:
https://bibliotekanauki.pl/articles/1837417.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
data stream mining
random forest
features importance
Opis:
The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 287-298
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of the Random Forest Model to Predict the Plasticity State of Vertisols
Autorzy:
Al Masmoudi, Yassine
Bouslihim, Yassine
Doumali, Kaoutar
El Aissaoui, Abdellah
Namr, Khalid Ibno
Powiązania:
https://bibliotekanauki.pl/articles/1839081.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
soil plasticity
random forest
moroccan vertisol
soil degradation
Opis:
Vertisol plasticity is related to moisture content, and it requires an in-depth physicochemical characterization. This information allows us to use the land under the most adequate conditions and avoid soil physical degradation, especially its compaction. The objective of this study was to characterize the Vertisol in the Moroccan region of Doukkala-Abda and to predict soil plasticity based on the physicochemical parameters of soil, such as texture, electrical conductivity, Soil Organic Matter (SOM) and other chemical parameters for 120 samples. Determination of soil plasticity using Atterberg limits is a challenging and time-consuming method. Thus, this study aimed to develop a new model that can predict soil plasticity using the Random Forest algorithm. The soils presented homogeneity in the majority of physicochemical parameters, except a significant difference observed in the SOM and the electrical conductivity, which in turn influenced the soil plasticity state. The results showed significant and positive correlations between SOM, Soil Clay Content (SCC), Electrical Conductivity (EC), and plasticity in the Vertisol fields of the region. For the training phase, the model gave excellent results with a coefficient of determination of 0.995 and an RMSE of 0.164. Almost the same results were observed in the validation phase with a coefficient of determination of 0.974 and an RMSE of 0.361, which shows that the model succeeded in predicting plasticity in both phases. On the basis of these results, this model can be used for the plasticity prediction using other physicochemical parameters and the Random Forest Model. The prediction of soil plasticity is an important parameter to respect the timing of introducing machines/tools in the fields and avoid Vertisol degradation.
Źródło:
Journal of Ecological Engineering; 2021, 22, 2; 36-46
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
EQUITY ISSUANCE AND CORPORATE DIVIDEND POLICY IN EMERGING ECONOMY CONTEXT
Autorzy:
Rohov, Heorhiy
Solesvik, Marina Z.
Powiązania:
https://bibliotekanauki.pl/articles/453403.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
dividend policy
emission policy
random forest algorithm
Ukraine
Opis:
This article explores links between the size of a company, industrial sector in which a company operates, concentration of capital, size of business and emission and dividend policy in the Ukrainian corporate sector. Guided by insights from the bird-in-hand theory, clientele theory, signaling theory, and agency theory, we justify factors that determine the choice of shares’ placement by Ukrainian public joint stock companies and forming of their dividend policy related to the current operating conditions of the Ukrainian corporate sector. Using mathematical approach of tree classification construction in the form of random forest algorithm, we found out that maximization of the share capital value, that is involved in shares issuance of Ukrainian PJSCs, is not a priority for owners of corporate rights. 86.1 per cent of companies have selected private placements of shares. In the non-financial sector, 87.5 per cent of companies opted private placements. The study revealed also only a small share (3.5%) of Ukrainian joint stock companies paid dividends to shareholders. However, the dividend policy of Ukrainian joint stock companies changed when they listed their shares on foreign stock markets. In this case two thirds of explored firms paid dividends.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2016, 17, 4; 114-137
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predictive Business Process Monitoring with Tree-based Classification Algorithms
Autorzy:
Owczarek, Tomasz
Janke, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/503954.pdf
Data publikacji:
2018
Wydawca:
Międzynarodowa Wyższa Szkoła Logistyki i Transportu
Tematy:
business process
prediction
classification
random forest
gradient boosting
Opis:
Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show that.
Źródło:
Logistics and Transport; 2018, 40, 4; 73-82
1734-2015
Pojawia się w:
Logistics and Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Interpretative machine learning as a key in recognizing the variability of lakes trophy patterns
Autorzy:
Jasiewicz, Jarosław
Zawiska, Izabela
Rzodkiewicz, Monika
Woszczyk, Michał
Powiązania:
https://bibliotekanauki.pl/articles/2054583.pdf
Data publikacji:
2022-03-31
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
total phosphorus
interpretative machine learning
random forest
Masurian lakes
Opis:
The paper presents an application of interpretative machine learning to identify groups of lakes not with similar features but with similar potential factors influencing the content of total phosphorus – Ptot. The method was developed on a sample of 60 lakes from North-Eastern Poland and used 25 external explanatory variables. Selected variables are stable over a long time, first group includes morphometric parameters of lakes and the second group en- compass watershed geometry geology and land use. Our method involves building a regression model, creating an ex- plainer, finding a set of mapping functions describing how each variable influences the outcome, and finally clustering objects by ’the influence’. The influence is a non-linear and non-parametric transformation of the explanatory variables into a form describing a given variable impact on the modeled feature. Such a transformation makes group data on the functional relations between the explanatory variables and the explained variable possible. The study reveals that there are five clusters where the concentration of Ptot is shaped similarly. We compared our method with other numerical analyses and showed that it provides new information on the catchment area and lake trophy relationship.
Źródło:
Quaestiones Geographicae; 2022, 41, 1; 127-146
0137-477X
2081-6383
Pojawia się w:
Quaestiones Geographicae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Estimating parameters of empirical infiltration models from the global dataset using machine learning
Autorzy:
Kim, S.
Karahan, G.
Sharma, M.
Pachepsky, Y.
Powiązania:
https://bibliotekanauki.pl/articles/2083049.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Agrofizyki PAN
Tematy:
infiltration modelling
random forest
Soil Water
Infiltration Global database
Opis:
It is beneficial to develop pedotransfer relationships to estimate infiltration equation coefficients in site-specific conditions from readily available data. No systematic studies have been published concerning the relationships between the accuracy of the infiltration equation and the accuracy of the predicted coefficients in this equation. The objective of this work was to test the hypothesis that, for the same infiltration data, the accuracy of pedotransfer predictions for coefficients in an infiltration equation is greater for the infiltration equation that performs better. The hypothesis was tested using the commonly employed Horton and Mezencev (modified Kostiakov) infiltration equations with data from the Soil Water Infiltration Global database. The random forest machine learning algorithm was used to develop the pedotransfer model. The Horton and the Mezencev models performed better with 928 and 758 datasets, respectively. The accuracy of the estimates of the infiltration equation coefficients did not differ substantially between the estimates obtained from all data and from the data where the infiltration equation had lower root-mean-squared error values. The root-mean-squared error values of the pedotransfer estimates decreased by 2 to 25% when only datasets with the same infiltration measurement method were considered. The development of predictive pedotransfer equations with the data obtained from the same infiltration measurement method is recommended.
Źródło:
International Agrophysics; 2021, 35, 1; 73-81
0236-8722
Pojawia się w:
International Agrophysics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark
Autorzy:
Gumaste, Shweta
G., Narayan D.
Shinde, Sumedha
K., Amit
Powiązania:
https://bibliotekanauki.pl/articles/1839316.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
cloud
DDoS
distributed processing
OpenStack
Apache Spark
random forest
Opis:
Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning classifiers on a distributed processing platform. We evaluate the DDoS detection mechanism in an OpenStack-based cloud testbed using the Apache Spark framework. We compare the classification performance using benchmark and real-time cloud datasets. Results of the experiments reveal that the random forest method offers better classifier accuracy. Furthermore, we demonstrate the effectiveness of the proposed distributed approach in terms of training and detection time.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 4; 62-71
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia
Autorzy:
Ismanto, Rido Dwi
Fitriana, Hana Listi
Manalu, Johanes
Purboyo, Alvian Aji
Prasasti, Indah
Powiązania:
https://bibliotekanauki.pl/articles/27314279.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
flood-susceptibility assessment
random forest
frequency ratio
Sumedang
remote sensing
Opis:
Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 6; 129--157
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognozowanie przedziału czasowego z maksymalnym w ciągu doby z użyciem gazu przez kotłownię
Forecasting the time interval of the day with the maximum boilers gas consumption
Autorzy:
Nowak, Bogdan
Bartnicki, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/394678.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
Tematy:
zużycie gazu
model prognostyczny
random forest
gas consumption
prognostic model
Opis:
Działania mające na celu poprawę efektywności energetycznej systemów zaopatrzenia w ciepło wymagają korzystania z coraz bardziej złożonych metod. Podstawowe sposoby zmniejszenia zużycia ciepła poprzez stosowanie lepszej izolacji cieplnej mają coraz bardziej ograniczone możliwości iwymagają stosunkowo dużych nakładów finansowych. Dobre efekty mogą być osiągane przez coraz lepsze dopasowanie rozwiązań technicznych, sposobów regulacji czy zasad eksploatacji źródła ciepła do warunków konkretnego obiektu zasilanego wciepło. Wymaga to jednak zarówno badań identyfikujących skuteczność takich metod, jak inarzędzi służących do opisu wybranych elementów systemu czy jego całości. Artykuł przedstawia wyniki badań przeprowadzonych dla kotłowni gazowej zasilającej w ciepło grupę budynków mieszkalnych. Celem było zbudowanie modelu, który prognozowałby dla konkretnego dnia przedział czasowy, w którym występuje maksymalne zużycie gazu. Dysponując pomiarami zużycia gazu wkolejnych godzinach doby, zdecydowano się zbudować model prognostyczny wyznaczający tę część doby, w której takie maksimum wystąpi. W opracowanym modelu zdecydowano się zastosować procedurę lasów losowych (random forest). Do utworzenia modelu zastosowano pakiet mlr (Kassambara), w którym przeprowadzono również strojenie hiperparametrów modelu na bazie danych historycznych. W oparciu o odrębne dane dla innego okresu działania kotłowni przedstawiono wyniki oceny jego jakości. Uzyskano skuteczność niemal 44%. Strojenie modelu wpłynęło na poprawę jego zdolności predykcyjnych.
The heat supply systems energy efficiency improvement requires the use of increasingly complex methods. The basic ways to reduce heat consumption is by using better thermal insulation, although they have more and more limited possibilities and need relatively large financial outlays. Good effects can be achieved by the better heat source adaptation to the conditions of aspecific facility supplied with heat. However, this requires research that identifies the effectiveness of such solutions as well as the tools used to describe selected elements of the system or its entirety. The article presents the results of tests carried out for agas boiler room supplying heat to agroup of residential buildings. The goal was to build amodel that would forecast the day range in which the maximum gas consumption occurs for agiven day. Having measurements of gas consumption in subsequent hours of the day, it was decided to build aforecasting model determining the part of the day in which such amaximum would occur. To create the model the random forest procedure was used along with the mlr (Kassambara) package. The model’s hyperparameters were tuned based on historical data. Based on data for another period of boilerroom operation, the results of the model’s quality assessment were presented. Close to 44% efficiency was achieved. Tuning the model improved its predictive ability.
Źródło:
Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN; 2019, 109; 93-109
2080-0819
Pojawia się w:
Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Vibroacoustic Real Time Fuel Classification in Diesel Engine
Autorzy:
Bąkowski, A.
Kekez, M.
Radziszewski, L.
Sapietova, A.
Powiązania:
https://bibliotekanauki.pl/articles/177686.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fuel recognition
classification trees
particle swarm optimization (PSO)
random forest
Opis:
Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
Źródło:
Archives of Acoustics; 2018, 43, 3; 385-395
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A System for Filling Store Displays: Pitting a Single Model against a Set of Demand Forecasting Models
System zapełnienia ekspozycji sklepowych: pojedynczy model a zespół modeli prognozowania popytu
Autorzy:
Myna, Artur
Myna, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2206342.pdf
Data publikacji:
2023
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
Extreme Gradient Boosting
logistic regression
random forest
regresja logistyczna
las losowy
Opis:
The aim of the paper was to develop the concept of retail display space allocation as a system and to assess the quality of very slow-moving products demand forecasting models (that have not yet been used by retail companies in Poland) as its key subsystem. Forecasts were made using the example of a clothing company. The quality of these models was assessed using the Weighted Mean Absolute Percentage Error. The first step was to build the individual models. Later, the authors built separate models for brick-and-mortar and online stores as well as brands, creating a set of six models. The findings show that the classification approach for very slow movers provides as precise results as the regression approach. No single model or set of models (built with a particular machine learning method) could be identified that made the best demand forecasts for brick-and-mortar stores, as statistical tests generally did not confirm the significance of the differences between the median forecasts.
Celem artykułu jest opracowanie koncepcji zapełnienia ekspozycji sklepowych jako sys- temu oraz ocena jakości modeli prognozowania popytu (które w Polsce nie są jeszcze wykorzystywane przez sieci handlowe) bardzo wolno rotujących produktów jako jego kluczowego podsystemu. Jakość modeli oceniono za pomocą miary Weighted Mean Absolute Percentage Error na różnych poziomach szczegółowości: dla całej sieci sprzedaży i określonego miesiąca oraz na „na przecięciu” sklepu, produk- tu i rozmiaru produktu. Najpierw zbudowano pojedyncze modele, następnie zaś odrębne modele dla sklepów stacjonarnych i internetowych, jak również marek, tworząc zespół sześciu modeli. Poprawę dopasowania modeli osiągnięto tylko dla sklepów internetowych. Wyniki pracy wskazują, że podejście klasyfikacyjne dla bardzo wolno rotujących produktów charakteryzują równie precyzyjne wyniki pro- gnoz jak podejście regresyjne. Nie można wskazać jednego modelu lub zespołu modeli (zbudowanego określoną metodą uczenia maszynowego), który wykonał najlepsze prognozy popytu dla sklepów sta- cjonarnych, gdyż istotności różnic median prognoz na ogół nie potwierdzono testami statystycznymi.
Źródło:
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu; 2023, 67, 2; 96-106
1899-3192
Pojawia się w:
Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan
Autorzy:
Yaseen, Muhammad Waseem
Awais, Muhammad
Riaz, Khuram
Rasheed, Muhammad Babar
Waqar, Muhammad
Rasheed, Sajid
Powiązania:
https://bibliotekanauki.pl/articles/31340346.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:
hydrometeorology
random forest
support vector
multilayer perceptron
machine learning
flood forecasting
Opis:
Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
Źródło:
Archives of Hydro-Engineering and Environmental Mechanics; 2022, 69, 1; 59-77
1231-3726
Pojawia się w:
Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Data-mining Technique for Seismic Vulnerability Assessment
Autorzy:
Wojcik, Waldemar
Karmenova, Markhaba
Smailova, Saule
Tlebaldinova, Aizhan
Belbeubaev, Alisher
Powiązania:
https://bibliotekanauki.pl/articles/1844631.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
data analysis
seismic assessment
clustering
h-means
k-means
random forest
Opis:
Assessment of seismic vulnerability of urban infrastructure is an actual problem, since the damage caused by earthquakes is quite significant. Despite the complexity of such tasks, today’s machine learning methods allow the use of “fast” methods for assessing seismic vulnerability. The article proposes a methodology for assessing the characteristics of typical urban objects that affect their seismic resistance; using classification and clustering methods. For the analysis, we use kmeans and hkmeans clustering methods, where the Euclidean distance is used as a measure of proximity. The optimal number of clusters is determined using the Elbow method. A decision-making model on the seismic resistance of an urban object is presented, also the most important variables that have the greatest impact on the seismic resistance of an urban object are identified. The study shows that the results of clustering coincide with expert estimates, and the characteristic of typical urban objects can be determined as a result of data modeling using clustering algorithms.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 2; 261-266
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers
Autorzy:
Saini, Rashmi
Powiązania:
https://bibliotekanauki.pl/articles/2174656.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
Machine Learning
Random Forest
AdaBoost
vegetation mapping
vegetation indices
Opis:
Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 1; 57--74
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A random forest model for the prediction of spudcan penetration resistance in stiff-over-soft clays
Autorzy:
Gao, Pan
Liu, Zhihui
Zeng, Ji
Zhan, Yiting
Wang, Fei
Powiązania:
https://bibliotekanauki.pl/articles/1573798.pdf
Data publikacji:
2020
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
machine learning
random forest
jack-up
penetration resistance
stiff-over-soft clays
Opis:
Punch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan penetration resistance in stiff-over-soft clays, based on the random forest (RF) method. The RF model was trained and tested with numerical simulation results obtained through the Finite Element model, implemented with the Coupled Eulerian Lagrangian (CEL) approach. With the proposed CEL model, the effects of the stiff layer thickness, undrained shear strength ratio, and the undrained shear strength of the soft layer on the bearing characteristics, as well as the soil failure mechanism, were numerically studied. A simplified resistance profile model of penetration in stiff-over-soft clays is proposed, divided into three sections by the peak point and the transition point. The importance of soil parameters to the penetration resistance was analysed. Then, the trained RF model was tested against the test set, showing a good prediction of the numerical cases. Finally, the trained RF was validated against centrifuge tests. The RF model successfully captured the punch-through potential, and was verified using data recorded in the field, showing advantages over the SNAME guideline. It is supposed that the trained RF model should give a good prediction of the spudcan penetration resistance profile, especially if trained with more field data.
Źródło:
Polish Maritime Research; 2020, 4; 130-138
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Monitoring Vegetation Cover Changes by Sentinel-1 Radar Images Using Random Forest Classification Method
Autorzy:
Tran, Van Anh
Le, Thi Le
Nguyen, Nhu Hung
Le, Thanh Nghi
Tran, Hong Hanh
Powiązania:
https://bibliotekanauki.pl/articles/2020227.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
vegetation cover change,
Sentinel-1
Random Forest
Binh Duong
Vietnam
Wietnam
wegetacja
Opis:
Vietnam is an Asian country with hot and humid tropical climate throughout the year. Forests account for more than 40% of the total land area and have a very rich and diverse vegetation. Monitoring the changes in the vegetation cover is obviously important yet challenging, considering such large varying areas and climatic conditions. A traditional remote sensing technique to monitor the vegetation cover involves the use of optical satellite images. However, in presence of the cloud cover, the analyses done using optical satellite image are not reliable. In such a scenario, radar images are a useful alternative due to the ability of radar pulses in penetrating through the clouds, regardless of day or night. In this study, we have used multi temporal C band satellite images to monitor vegetation cover changes for an area in Dau Tieng and Ben Cat districts of Binh Duong province, Mekong Delta, Vietnam. With a collection of 46 images between March 2015 and February 2017, the changes of five land cover types including vegetation loss and replanting in 2017 were analyzed by selecting two cases, using 9 images in the dry season of 3 years 2015, 2016 and 2017 and using all of 46 images to conduct Random Forest classifier with 100, 200, 300 and 500 trees respectively. The result in which the model with nine images and 300 trees gave the best accuracy with an overall accuracy of 98.4% and a Kappa of 0.97. The results demonstrated that using VH polarization, Sentinel-1 gives quite a good accuracy for vegetation cover change. Therefore, Sentinel-1 can also be used to generate reliable land cover maps suitable for different applications.
Źródło:
Inżynieria Mineralna; 2021, 2; 441--451
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of machine learning algorithms to predict permeability in tight sandstone formations
Zastosowanie metod uczenia maszynowego do przewidywania przepuszczalności w formacjach zwięzłych piaskowców typu tight gas
Autorzy:
Topór, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2143653.pdf
Data publikacji:
2021
Wydawca:
Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:
machine learning
random forest
permeability
prediction
uczenie maszynowe
lasy losowe
predykcja
przepuszczalność
Opis:
The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.
Zastosowanie algorytmów uczenia maszynowego w geologii naftowej otworzyło nowy rozdział w poszukiwaniu złóż ropy i gazu. Algorytmy uczenia maszynowego zostały z powodzeniem wykorzystane do przewidywania kluczowych właściwości petrofizycznych charakteryzujących złoże. W pracy zastosowano metody uczenia maszynowego do przewidywania przepuszczalności w warunkach ustalonego ciśnienia złożowego dla formacji zwięzłych piaskowców typu tight gas. Modele zostały skonstruowane przy użyciu algorytmów o różnym stopniu komplikacji (wielowymiarowa regresja liniowa – MLR i lasy losowe – RF), a następnie poddano je procesowi uczenia na danych zawierających podstawowe informacje o otworze, podstawowe parametry petrofizyczne oraz typ skał pochodzący z makroskopowego i mikroskopowego opisu próbek rdzeni. Typ skał został rozkodowany i poddany procesowi inżynierii cech, aby wydobyć dodatkowe zmienne do modelu. Proces uczenia na zbiorze treningowym został przeprowadzony z wykorzystaniem 10-krotnej kroswalidacji. Uzyskane wyniki pokazują, że oba algorytmy mogą przewidywać przepuszczalność z dużą dokładnością (R2 = 0,800 dla MLR vs R2 = 0,834 dla RF). Dokładność modelu RF jest około 3% lepsza niż MLR i około 6% lepsza w porównaniu do modelu referencyjnego (model regresji liniowej z jedną zmienną – porowatością). W przypadku obu modeli porowatość była najistotniejszym parametrem przy przewidywaniu przepuszczalności. Dodatkowo w modelu wykorzystującym lasy losowe istotną cechą okazała się głębokość próbki, co może świadczyć o dodatkowych interakcjach pomiędzy zmiennymi. Cechą wspólną próbek w zbiorze treningowym i testowym, dla których modele zadziałały ze słabą skutecznością, były porowatość od 3% do 10% i brak spękań. Dodatkowo w zbiorze testowym niska dokładność przewidywań przepuszczalności była związana z obecnością cementacji kalcytem i kwarcem. Workflow wykorzystujący stan wiedzy dotyczącej modelowania, którego trzon stanowi pakiet tidymodels, będzie dalej stosowany do prognozowania przestrzennych właściwości petrofizycznych na podstawie atrybutów sejsmicznych.
Źródło:
Nafta-Gaz; 2021, 77, 5; 283-292
0867-8871
Pojawia się w:
Nafta-Gaz
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Approach to License Plate Recognition in Real Time Using Multi-stage Computational Intelligence Classifier
Autorzy:
Kekez, Michał
Powiązania:
https://bibliotekanauki.pl/articles/27311914.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
car license plates
LPR
ANPR
OCR
image processing
neural network
Random Forest
Opis:
Automatic car license plate recognition (LPR) is widely used nowadays. It involves plate localization in the image, character segmentation and optical character recognition. In this paper, a set of descriptors of image segments (characters) was proposed as well as a technique of multi-stage classification of letters and digits using cascade of neural network and several parallel Random Forest or classification tree or rule list classifiers. The proposed solution was applied to automated recognition of number plates which are composed of capital Latin letters and Arabic numerals. The paper presents an analysis of the accuracy of the obtained classifiers. The time needed to build the classifier and the time needed to classify characters using it are also presented.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 2; 275--280
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate
Autorzy:
Pural, Yusuf Enes
Powiązania:
https://bibliotekanauki.pl/articles/24148677.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
soft sensor
machine learning
random forest
multi-layer perceptron
flotation
grade estimation
Opis:
Soft sensors are mathematical models that estimate the value of a process variable that is difficult or expensive to measure directly. They can be based on first principle models, data-based models, or a combination of both. These models are increasingly used in mineral processing to estimate and optimize important performance parameters such as mill load, mineral grades, and particle size. This study investigates the development of a data-driven soft sensor to predict the silicate content in iron ore reverse flotation concentrate, a crucial indicator of plant performance. The proposed soft sensor model employs a dataset obtained from Kaggle, which includes measurements of iron and silicate content in the feed to the plant, reagent dosages, weight and pH of pulp, as well as the amount of air and froth levels in the flotation units. To reduce the dimensionality of the dataset, Principal Component Analysis, an unsupervised machine learning method, was applied. The soft sensor model was developed using three machine learning algorithms, namely, Ridge Regression, Multi-Layer Perceptron, and Random Forest. The Random Forest model, created with non-reduced data, demonstrated superior performance, with an R-squared value of 96.5% and a mean absolute error of 0.089. The results suggest that the proposed soft sensor model can accurately predict the silicate content in the iron ore flotation concentrate using machine learning algorithms. Moreover, the study highlights the importance of selecting appropriate algorithms for soft sensor developments in mineral processing plants.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 169823
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The use of data mining models in solving the problem of imbalanced classes based on the example of an online marketing campaign
Wykorzystanie modeli data mining w rozwiązywaniu problemu niezrównoważonych klas na przykładzie kampanii marketingowych w Internecie
Autorzy:
Łapczyński, Mariusz
Surma, Jerzy
Powiązania:
https://bibliotekanauki.pl/articles/424980.pdf
Data publikacji:
2015
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
C&RT
Random Forest
imbalanced class problem
online social network
banner ad campaign
Opis:
While building predictive models in analytical CRM, researchers often encounter the problem of imbalanced classes (skewed distributions of dependent variables), which consists in the fact that the number of observations belonging to one category of the dependent variable is much lower than the number of observations belonging to the second category of that variable. This is related to such areas as churn analysis, customer acquisition models and cross and up-selling models. The purpose of the paper is to present a predictive model that was built to predict the response of Internet users to banner advertising. The dataset used in the study came from an online social network which offers advertisers banner campaigns targeting its users. The advertising campaign of a cosmetics company was carried out in the autumn of 2010 and was mainly targeted at young women. A user of this service was described by 115 independent variables – 3 out of which were demographic variables (sex, age, education), and the remaining 112 referred to the user’s online activity. While building the model there appeared the problem of imbalanced classes due to the low number of users who clicked on the banner ad. The number of cases amounted to 81,000, while the number of positive reactions to the banner was 207, which constitutes approximately 0.25% of the dependent variable. During the study, two popular data mining tools were utilized – the decision trees C&RT and Random Forest. The second goal of this paper is to compare the performance of the predictive models based on both these analytical tools.
Źródło:
Econometrics. Ekonometria. Advances in Applied Data Analytics; 2015, 3 (49); 9-19
1507-3866
Pojawia się w:
Econometrics. Ekonometria. Advances in Applied Data Analytics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method
Autorzy:
Choi, Young-Sin
Kwon, Do-Hun
Lee, Min_Woo
Cha, Eun-Ji
Jeon, Junhyub
Lee, Seok-Jae
Kim, Jongryoul
Kim, Hwi-Jun
Powiązania:
https://bibliotekanauki.pl/articles/2174571.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Fe-based amorphous
soft magnetic properties
artificial intelligence
machine learning
random forest regression
Opis:
The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe80.5Si3.63B13.54C2.33 at.% and the highest saturation magnetization was obtained Fe81.83Si3.63B12.63C1.91at.% with R2 values of 0.74 and 0.878, respectively.
Źródło:
Archives of Metallurgy and Materials; 2022, 67, 4; 1459--1463
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
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