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Tytuł:
Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation
Autorzy:
Abdullaeva, Barno S.
Powiązania:
https://bibliotekanauki.pl/articles/28411647.pdf
Data publikacji:
2023
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
deep learning
gradient boosting
hybrid model
multi-step ahead forecasting
rainfall-runoff simulation
Opis:
Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
Źródło:
Journal of Water and Land Development; 2023, 59; 216--223
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An assessment of machine learning and data balancing techniques for evaluating downgrade truck crash severity prediction in Wyoming
Autorzy:
Ampadu, Vincent-Michael Kwesi
Haq, Muhammad Tahmidul
Ksaibati, Khaled
Powiązania:
https://bibliotekanauki.pl/articles/2176018.pdf
Data publikacji:
2022
Wydawca:
Fundacja Centrum Badań Socjologicznych
Tematy:
crash severity
performance
extreme gradient boosting tree
adaptive boosting tree
random forest
gradient boost decision tree
adaptive synthetic algorithm
Opis:
This study involved the investigation of various machine learning methods, including four classification tree-based ML models, namely the Adaptive Boosting tree, Random Forest, Gradient Boost Decision Tree, Extreme Gradient Boosting tree, and three non-tree-based ML models, namely Support Vector Machines, Multi-layer Perceptron and k-Nearest Neighbors for predicting the level of severity of large truck crashes on Wyoming road networks. The accuracy of these seven methods was then compared. The Final ROC AUC score for the optimized random forest model is 95.296 %. The next highest performing model was the k-NN with 92.780 %, M.L.P. with 87.817 %, XGBoost with 86.542 %, Gradboost with 74.824 %, SVM with 72.648 % and AdaBoost with 67.232 %. Based on the analysis, the top 10 predictors of severity were obtained from the feature importance plot. These may be classified into whether safety equipment was used, whether airbags were deployed, the gender of the driver and whether alcohol was involved.
Źródło:
Journal of Sustainable Development of Transport and Logistics; 2022, 7, 2; 6--24
2520-2979
Pojawia się w:
Journal of Sustainable Development of Transport and Logistics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Evolving ensembles of linear classifiers by means of clonal selection algorithm
Autorzy:
Bereta, M.
Burczyński, T.
Powiązania:
https://bibliotekanauki.pl/articles/969829.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
artificial immune systems
clonal selection
linear classifiers
bagging
boosting
Opis:
Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work well with the proposed algorithm as the base classifier.
Źródło:
Control and Cybernetics; 2010, 39, 2; 325-342
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
BOOSTING UNDER QUANTILE REGRESSION – CAN WE USE IT FOR MARKET RISK EVALUATION?
Autorzy:
Bień-Barkowska, Katarzyna
Powiązania:
https://bibliotekanauki.pl/articles/453152.pdf
Data publikacji:
2014
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
Boosting
quantile regression
GARCH models
value-at-risk
Opis:
We consider boosting, i.e. one of popular statistical machine-learning meta-algorithms, as a possible tool for combining individual volatility estimates under a quantile regression (QR) framework. Short empirical exercise is carried out for the S&P500 daily return series in the period of 2004-2009. Our initial findings show that this novel approach is very promising and the in-sample goodness-of-fit of the QR model is very good. However much further research should be conducted as far as the out-of-sample quality of conditional quantile predictions is concerned.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 1; 7-17
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers
Autorzy:
Biju, V. G.
Prashanth, CM
Powiązania:
https://bibliotekanauki.pl/articles/108646.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi
Tematy:
bagging
boosting
SVM
KNN
decision tree
Opis:
This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.
Źródło:
Journal of Applied Computer Science Methods; 2017, 9 No. 1; 23-47
1689-9636
Pojawia się w:
Journal of Applied Computer Science Methods
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gas exchange in valved two-stroke SI engine
Autorzy:
Buczek, K.
Mitianiec, W.
Powiązania:
https://bibliotekanauki.pl/articles/245914.pdf
Data publikacji:
2010
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
transport
engine development
two-stroke engine
boosting
Opis:
The paper describes the work of high speed charged spark ignition overhead poppet valve two-stroke engine, which enables to achieve higher total efficiency and exhaust gas emission comparable to four-stroke engines. The work of such engines is possible by proper choice ofvalve timings, geometrical parameters of inlet, outlet ducts and charge pressure. The engine has to be equipped with direct fuel injection system enabling lower emission of pollutants. The work is based on theoretical considerations performed in GT-Power in previous authors' research and carried out in CFD code (KIVA 3 V) for different engine configurations. The initial results included in the paper show influence of inlet port geometry and charge pressure on engine scavenging process. Additionally, optimum fuel spray injector position was considered in order to obtain proper fuel vaporization and avoid significant wall-wetting. The simulation results show that the nitrogen oxides arę considerably reduced in comparison to four-stroke engines because ofhigher internal exhaust gas recirculation. The innovation of this proposal is applying of poppet intake and exhaust valves with turbocharging in the two-stroke engine and obtaining a significant downsizing effect. The conclusion shows the possibilities of proper gas exchange process in this type of two-stroke engine and thus, the feasibility of its application as a power unii for transportation means with higher total efficiency than traditional engines with possible change of engine work in two modes: two- and four-stroke cycles.
Źródło:
Journal of KONES; 2010, 17, 1; 73-80
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of ensemble gradient boosting decision trees to forecast stock price on WSE
Autorzy:
Dadej, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/518035.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Gdański. Wydział Ekonomiczny
Tematy:
equity investments
artificial intelligence
machine learning
algorithmic trading strategy
gradient boosting
Opis:
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.
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.
Źródło:
Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”; 2019, 9; 265-275
1731-6707
Pojawia się w:
Zeszyty Studenckie Wydziału Ekonomicznego „Nasze Studia”
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, gradient boosting and GWLM-NARX models
Autorzy:
Fayaz, Sheikh Amir
Zaman, Majid
Butt, Muheet Ahmed
Kaul, Sameer
Powiązania:
https://bibliotekanauki.pl/articles/38433812.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
meteorological data
M5 model tree
linear model functions
gradient boosting
logistic model tree
Opis:
Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.
Źródło:
Applied Computer Science; 2022, 18, 4; 16-27
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Gradient Boosting in Regression
Gradientowa odmiana metody boosting w analizie r e g r e s ji
Autorzy:
Gatnar, Eugeniusz
Powiązania:
https://bibliotekanauki.pl/articles/904716.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
tree-based models
regression
boosting
Opis:
Szeroko stosowane w praktyce metody nieparametryczne wykorzystujące tzw. drzewa regresyjne mają jedną istotną wadę. Otóż wykazują one niestabilność, która oznacza, że niewielka zmiana wartości cech obiektów w zbiorze uczącym może prowadzić do powstania zupełnie innego modelu. Oczywiście wpływa to negatywnie na ich trafność prognostyczną. Tę wadę można jednak wyeliminować, dokonując agregacji kilku indywidualnych modeli w jeden. Znane są trzy metody agregacji modeli i wszystkie opierają się na losowaniu ze zwracaniem obiektów ze zbioru uczącego do kolejnych prób uczących: agregacja bootstrapowa (boosting), losowanie adaptacyjne (bagging) oraz metoda hybrydowa, łącząca elementy obu poprzednich. W analizie regresji szczególnie warto zastosować gradientową, sekwencyjną, odmianę metody boosting. W istocie polega ona wykorzystaniu drzew regrcsyjnych w kolejnych krokach do modelowania reszt dla modelu uzyskanego w poprzednim kroku.
The successful tree-based methodology has one serious disadvantage: lack of stability. That is, regression tree model depends on the training set and even small change in a predictor value could lead to a quite different model. In order to solve this problem single trees are combined into one model. There are three aggregation methods used in classification: bootstrap aggregation (bagging), adaptive resample and combine (boosting) and adaptive bagging (hybrid bagging-boosting procedure). In the field of regression a variant of boosting, i.e. gradient boosting, can be used. Friedman (1999) proved that boosting is equivalent to a stepwise function approximation in which in each step a regression tree models residuals from last step model.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 194
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of selected supervised classification methods to bank marketing campaign
Autorzy:
Grzonka, D.
Borowik, B.
Suchacka, G.
Powiązania:
https://bibliotekanauki.pl/articles/94739.pdf
Data publikacji:
2016
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
Tematy:
classification
supervised learning
data mining
decision trees
bagging
boosting
random forests
bank marketing
R project
Opis:
Supervised classification covers a number of data mining methods based on training data. These methods have been successfully applied to solve multi-criteria complex classification problems in many domains, including economical issues. In this paper we discuss features of some supervised classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained results are used for evaluating the effectiveness of the classification rules.
Źródło:
Information Systems in Management; 2016, 5, 1; 36-48
2084-5537
2544-1728
Pojawia się w:
Information Systems in Management
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
APPLICATION OF MIXED MODELS AND FAMILIES OF CLASSIFIERS TO ESTIMATION OF FINANCIAL RISK PARAMETERS
Autorzy:
Grzybowska, Urszula
Karwański, Marek
Powiązania:
https://bibliotekanauki.pl/articles/452746.pdf
Data publikacji:
2015
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
LGD
mixed models
random forests
gradient boosting
Opis:
The essential role in credit risk modeling is Loss Given Default (LGD) estimation. LGD is treated as a random variable with bimodal distribution. For LGD estimation advanced statistical models such as beta regression can be applied. Unfortunately, the parametric methods require amendments of the “inflation” type that lead to mixed modeling approach. Contrary to classical statistical methods based on probability distribution, the families of classifiers such as gradient boosting or random forests operate with information and allow for more flexible model adjustment. The problem encountered is comparison of obtained results. The aim of the paper is to present and compare results of LGD modeling using statistical methods and data mining approach. Calculations were done on real life data sourced from one of Polish large banks.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2015, 16, 1; 108-115
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
FAMILIES OF CLASSIFIERS – APPLICATION IN DATA
Autorzy:
Grzybowska, Urszula
Karwański, Marek
Powiązania:
https://bibliotekanauki.pl/articles/453604.pdf
Data publikacji:
2014
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Katedra Ekonometrii i Statystyki
Tematy:
random forests
gradient boosting
DEA
rating classes
variable selection
ranking
high rated portfolio
Opis:
Economic description of firms and companies is based on a number of indicators. The indicators are related to each other and can be considered only in a specific context. Regression models allow for such approach. Unfortunately, the problems we deal with are usually nonlinear and the choice of relevant information is very difficult. The aim of the paper is to present a method of variable selection based on random forest and gradient boosting approach and its application to companies ranking in DEA method. The results will be compared with the ordering obtained using expert supported approach for variable selection in DEA.
Źródło:
Metody Ilościowe w Badaniach Ekonomicznych; 2014, 15, 2; 94-101
2082-792X
Pojawia się w:
Metody Ilościowe w Badaniach Ekonomicznych
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A 1.67 pJ/Conversion-step 8-bit SAR-Flash ADC Architecture in 90-nm CMOS Technology
Autorzy:
Khatak, Anil
Kumar, Manoj
Dhull, Sanjeev
Powiązania:
https://bibliotekanauki.pl/articles/1844527.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
analog to digital converter
ADC
successive approximation register (SAR)
common mode current feedback gain boosting
CMFD-GB
residue amplifier
RA
spurious free dynamic range
SFDR
integral nonlinearity
INL
differential nonlinearity
DNL
Opis:
A novice advanced architecture of 8-bit analog to digital converter is introduced and analyzed in this paper. The structure of proposed ADC is based on the sub-ranging ADC architecture in which a 4-bit resolution flash-ADC is utilized. The proposed ADC architecture is designed by employing a comparator which is equipped with common mode current feedback and gain boosting technique (CMFD-GB) and a residue amplifier. The proposed 8 bits ADC structure can achieve the speed of 140 mega-samples per second. The proposed ADC architecture is designed at a resolution of 8 bits at 10 MHz sampling frequency. DNL and INL values of the proposed design are -0.94/1.22 and -1.19/1.19 respectively. The ADC design dissipates a power of 1.24 mW with the conversion speed of 0.98 ns. The magnitude of SFDR and SNR from the simulations at Nyquist input is 39.77 and 35.62 decibel respectively. Simulations are performed on a SPICE based tool in 90 nm CMOS technology. The comparison shows better performance for this proposed ADC design in comparison to other ADC architectures regarding speed, resolution and power consumption.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 3; 347-354
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Great Economic Crisis in Poland (1929–1935) in the Context of the Global Crisis
Autorzy:
Klimiuk, Zbigniew
Powiązania:
https://bibliotekanauki.pl/articles/2146972.pdf
Data publikacji:
2022-05-14
Wydawca:
Uniwersytet Mikołaja Kopernika w Toruniu. Wydawnictwo UMK
Tematy:
Great Crisis
industrial and agricultural crisis
economic downturn
deflation
stagnation
economic recovery
boosting the economic situation
public spending
foreign trade
state interventionism
Opis:
The aim of the article was to analyze the causes and consequences of the great economic crisis in Poland in the context of the world economy and the global crisis. The first symptoms of the crisis became evident in June 1929, when production began to decline, and in August that year price drop began. After these events, the economic situation in Poland was systematically deteriorating. The year 1935 can be considered the end of the crisis in Poland, despite the fact that the industrial crisis began to break through gradually already in 1933. The great economic crisis was a global phenomenon, even though it did not start in all countries at the same time. It depended on the internal economic situation of each of them. It stood out against the background of previous economic crises due to the fact that only the introduction of state interventionism in the form of the “New Deal” program in the USA brought about an improvement in the situation, and therefore, in 1933, some economic recovery began in the world. However, in some parts of the world (including Poland), due to the specific features of their economies, the crisis lasted until 1935. The crisis of 1929–1935 was rightly called “the great crisis”, not only because of economic problems, but also due to political consequences, i.e., the strengthening of totalitarian systems in Western Europe and the growing importance of the communist movement around the world (the USSR was the only country not affected by its consequences). The great economic crisis was characterized by: longevity – in industrial countries it lasted until 1933, and in agricultural countries until 1935; depth of impact – it covered all areas of the economy: industry, agriculture, domestic and foreign trade, transport, monetary and credit system; wide geographic scope – it covered all countries of the world connected with the world capitalist system and capitalist economies; the scale of the decline in economic growth rates – in the history of economic crises to date, the world economy has not experienced such a deep collapse and stagnation.
Źródło:
Historia i Polityka; 2022, 40 (47); 25-42
1899-5160
2391-7652
Pojawia się w:
Historia i Polityka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative analysis of selected online tools for JavaScript code minification. A case study of a map application
Autorzy:
Król, Karol
Powiązania:
https://bibliotekanauki.pl/articles/100224.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Rolniczy im. Hugona Kołłątaja w Krakowie
Tematy:
boosting minification
bloating file
compression
performance
optimisation
ad hoc testing
zwiększanie minifikacji
rozdęty plik
kompresja
wydajność
optymalizacja
testowanie
Opis:
The performance of some map applications can be improved not only through the compression of raster files or appropriate data server configuration, but also by using source file minification. Minification can be more or less effective. The objective of the paper is to perform a comparative analysis of selected online tools for minifying JavaScript code and to measure the impact of such minification on the performance of a map application. Minification and performance tests were conducted on a prototype map application. The application was developed as a ZoomLens component extending the functionality of any website. Various tools yielded similar results of the JavaScript file minification, and it did not affect the values of aggregate performance indices. In most cases, it reduced the JavaScript file size by over a half. It has been demonstrated that minification of JavaScript code alone may not be sufficient to improve the application performance noticeably.
Źródło:
Geomatics, Landmanagement and Landscape; 2020, 2; 119-129
2300-1496
Pojawia się w:
Geomatics, Landmanagement and Landscape
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 26

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