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Tytuł:
Applying Hunger Game Search (HGS) for selecting significant blood indicators for early prediction of ICU COVID-19 severity
Autorzy:
Sayed, Safynaz AbdEl-Fattah
ElKorany, Abeer
Sayed, Sabah
Powiązania:
https://bibliotekanauki.pl/articles/27312915.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ICU severity prediction
COVID-19
clinical blood tests
Hunger Game search
HGS
optimization algorithm
support vector machine
SVM
feature selection
Opis:
This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms.
Źródło:
Computer Science; 2023, 24 (1); 113--136
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Efficient Classification of Hyperspectral Remotely Sensed Data Using Support Vector Machine
Autorzy:
Mahendra, H. N.
Mallikarjunaswamy, S.
Powiązania:
https://bibliotekanauki.pl/articles/2134051.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
support vector machine
SVM
central processing unit
CPU
digital signal processor
DSP
field programmable gate array
FPGA
high level synthesis
HLS
hardware description language
HDL
Opis:
This work present an efficient hardware architecture of Support Vector Machine (SVM) for the classification of Hyperspectral remotely sensed data using High Level Synthesis (HLS) method. The high classification time and power consumption in traditional classification of remotely sensed data is the main motivation for this work. Therefore presented work helps to classify the remotely sensed data in real-time and to take immediate action during the natural disaster. An embedded based SVM is designed and implemented on Zynq SoC for classification of hyperspectral images. The data set of remotely sensed data are tested on different platforms and the performance is compared with existing works. Novelty in our proposed work is extend the HLS based FPGA implantation to the onboard classification system in remote sensing. The experimental results for selected data set from different class shows that our architecture on Zynq 7000 implementation generates a delay of 11.26 μs and power consumption of 1.7 Watts, which is extremely better as compared to other Field Programmable Gate Array (FPGA) implementation using Hardware description Language (HDL) and Central Processing Unit (CPU) implementation.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 609--617
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Lung cancer detection using an integration of fuzzy K-Means clustering and deep learning techniques for CT lung images
Autorzy:
Prasad, J. Maruthi Nagendra
Chakravarty, S.
Krishna, M. Vamsi
Powiązania:
https://bibliotekanauki.pl/articles/2173683.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fuzzy K-means
artificial neural networks
SVM
support vector machine
crow search optimization algorithm
algorytm rozmytych k-średnich
sztuczne sieci neuronowe
maszyna wektorów wspierających
algorytm optymalizacji wyszukiwania kruków
Opis:
Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 3; art. no. e139006
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq
Autorzy:
Alwan, Imzahim A.
Aziz, Nadia A.
Powiązania:
https://bibliotekanauki.pl/articles/1838006.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
land cover mapping
Sentinel 2
supervised classification
maximum likelihood
Support Vector Machine (SVM)
confusion matrix
Opis:
Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 1; 5-21
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection and classification of short-circuit faults on a transmission line using current signal
Autorzy:
Coban, Melih
Tezcan, Suleyman S.
Powiązania:
https://bibliotekanauki.pl/articles/2086833.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
transmission line
fault detection
fault classification
support vector machine
SVM
linia przesyłowa
wykrywanie uszkodzeń
klasyfikacja błędów
maszyna wektorów nośnych
Opis:
This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 4; e137630, 1--9
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach
Autorzy:
Birame, M’hamed
Bessedik, Sid Ahmed
Benkhoris, Mohamed Fouad
Powiązania:
https://bibliotekanauki.pl/articles/1840890.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
fault diagnosis
partial rupture rotor bar
spectral analysis
least square support vector machine
LS-SVM
diagnostyka uszkodzeń
silnik indukcyjny
wirnik
analiza widmowa
maszyna wektorów nośnych
Opis:
Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.
Źródło:
Diagnostyka; 2021, 22, 1; 57-63
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Water Quality Classification by Integration of Attribute-Realization and Support Vector Machine for the Chao Phraya River
Autorzy:
Sillberg, Chalisa Veesommai
Kullavanijaya, Pratin
Chavalparit, Orathai
Powiązania:
https://bibliotekanauki.pl/articles/1955579.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
environmental data analysis
machine learning
SVM
support vector machine
water quality index
WQI
Opis:
The water quality index (WQI) is an essential indicator to manage water usage properly. This study aimed at applying a machine learning-based approach integrating attribute-realization (AR) and support vector machine (SVM) algorithm to classify the Chao Phraya River’s water quality. The historical monitoring dataset during 2008-2019 including biological oxygen demand (BOD), conductivity (Cond), dissolved oxygen (DO), faecal coliform bacteria (FCB), total coliform bacteria (TCB), ammonia (NH3-N), nitrate (NO3-N), salinity (Sal), suspended solids (SS), total nitrogen (TN), total dissolved solids (TDS), and turbidity (Turb), were processed via four studied steps: data pre-processing by means substituting method, contributing parameter evaluation by recognition pattern study, examination of the mathematic functions for quality classification, and validation of obtained approach. The results showed that NH3-N, TCB, FCB, BOD, DO, and Sal were the main attributes contributing orderly to water quality classification with confidence values of 0.80, 0.79, 0.78, 0.76, 0.69, and 0.64, respectively. Linear regression was the most suitable function to river water data classification than Sigmoid, Radial basis and Polynomial. The different number of attributes and mathematic functions promoted the different classification performance and accuracy. The validation confirmed that AR-SVM was a potent approach application to classify river water’s quality with 0.86-0.95 accuracy when applied three to six attributes.
Źródło:
Journal of Ecological Engineering; 2021, 22, 9; 70-86
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor
Autorzy:
Birame, M’hamed
Taibi, Djamel
Bessedik, Sid Ahmed
Benkhoris, Mohamed Fouad
Powiązania:
https://bibliotekanauki.pl/articles/327458.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
induction motor
inter-turn short circuit
fault diagnosis
least square support vector machine
LS-SVM
silnik indukcyjny
zwarcie międzyzwojowe
diagnostyka uszkodzeń
Opis:
Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.
Źródło:
Diagnostyka; 2020, 21, 4; 35-41
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Comparative Study between CS-LBP/SVM and CS-LBP/PCA in Facial Expression Recognition
Autorzy:
Gaur, Sheena
Powiązania:
https://bibliotekanauki.pl/articles/1075570.pdf
Data publikacji:
2019
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Center symmetric local binary patterns (CS-LBP)
Facial Expression Analysis
Principal Component Analysis 9PCA)
Support Vector Machine (SVM)
Opis:
Face plays significant role in social communication. This is a 'window' to human personality, emotions and thoughts. Due to this, face is a subject of study in many areas of science such as psychology, behavioral science, medicine and computer science etc. In this paper, a comparative study is suggested between CS-LBP/SVM and CS-LBP/PCA. These algorithms are used in emotive facial expression recognition. Finally, a comparison is shown between PCA & SVM in terms of Dimension Reduction. The proposed system uses grayscale frontal face images of a Japanese female to classify six basic emotions namely happiness, sadness, disgust, fear, surprise and anger.
Źródło:
World Scientific News; 2019, 121; 83-89
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm
Autorzy:
Xia, Xin
Liu, Xiaofeng
Lou, Jichao
Powiązania:
https://bibliotekanauki.pl/articles/227220.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
smart substation
network fault classification
improved separation interval method (ISIM)
support vector
machine (SVM)
Anti-noise processing (ANP)
Opis:
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 657-663
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine-Learning Methods for Assessing Dynamic Resistance of Existing Bridge Structures Subjected to Mining Tremors
Metody uczenia maszynowego w ocenie odporności dynamicznej istniejących obiektów mostowych poddanych wstrząsom górniczym
Autorzy:
Rusek, J.
Powiązania:
https://bibliotekanauki.pl/articles/385657.pdf
Data publikacji:
2018
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
dynamika budowli
uczenie maszynowe
sztuczne sieci neuronowe
SVM
wstrząsy górnicze
odporność dynamiczna
mosty
dynamics of structures
machine learning
Artificial Neural Networks
SVM Support Vector Machine
mining tremors
dynamic resistance
bridges
Opis:
W pracy przedstawiono wyniki badań, których celem było utworzenie modelu pozwalającego na określenie odporności istniejących obiektów mostowych na wpływy wstrząsów górniczych. Podstawą do analiz była utworzona przez autora baza danych o odporności dynamicznej żelbetowych obiektów mostowych poddanych wymuszeniu sejsmicznemu charakterystycznemu dla terenu Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Odporność dynamiczna każdego obiektu w bazie danych została wyrażona w postaci granicznych wartości przyspieszeń drgań gruntu, jakie dana konstrukcja może przejąć bez zagrożenia bezpieczeństwa. Badania przeprowadzono, wykorzystując metodę Support Vector Machine (SVM) w ujęciu regresyjnym (SVR – Support Vector Regression) oraz sztuczne sieci neuronowe (ANN – Artificial Neural Network). Utworzone w ten sposób modele porównano w aspekcie jakości predykcji oraz uogólniania nabytej wiedzy. Pozwoliło to na wytypowanie metody najbardziej efektywnej pod względem oceny odporności dynamicznej istniejących obiektów mostów.
This paper demonstrates the results of research studies aimed at creating a model that allows to determine the resistance of existing bridge structures to the impact of mining tremors. A database (created by the author of this article) of the dynamic resistance of reinforced concrete bridge structures subjected to seismic excitations commonly occurring in the Legnica-Głogów Copper District (LGOM) formed the basis for the analysis. The dynamic resistance of each structure contained in the database was expressed as the limit values of the acceleration of ground vibrations that may be carried by a given structure without compromising its safety. The study was carried out using the Support Vector Machine (SVM) method in a Support Vector Regression (SVR) approach as well as an Artificial Neural Network (ANN). The models were compared in terms of the quality of the predictions and generalization of the acquired knowledge. This allows to select the most-effective method in evaluating the dynamic resistance of existing bridge structures.
Źródło:
Geomatics and Environmental Engineering; 2018, 12, 1; 109-120
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of the influence of mining impacts on the intensity of damage to masonry building structures
Analiza wpływu oddziaływań górniczych na intensywność uszkodzeń budynków murowanych
Autorzy:
Firek, K.
Powiązania:
https://bibliotekanauki.pl/articles/105166.pdf
Data publikacji:
2017
Wydawca:
Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
Tematy:
technical condition
buildings
masonry structure
mining impacts
Partial Least Squares Regression
multiple regression analysis
Support Vector Machine
stan techniczny
budynek
konstrukcja murowana
wpływy górnicze
metoda cząstkowych najmniejszych kwadratów
PLSR
analiza regresji wielorakiej
metoda wektorów podpierających
SVM
Opis:
The paper presents the results of the analysis of the extent of damage to building structures subjected to mining impacts in the form of tremors and continuous surface deformation. The two methods which were used included the multiple regression analysis and the Support Vector Machine – SVM, which belongs to the socalled Machine Learning. The study used the database of the design, technical condition and potential causes of damage to 199 non-renovated buildings, up to the age of 20 years, of a traditional brick construction, located in the mining area of Legnica-Głogów Copper District (LGOM). The conducted analysis allowed for the qualitative assessment of the influence of mining impacts on the extent of damage to the studied buildings.
W referacie przedstawiono wyniki analizy zakresu uszkodzeń budynków poddanych oddziaływaniom górniczym w postaci wstrząsów oraz ciągłych deformacji terenu. Posłużono się statystyczną metodą regresji wielorakiej oraz metodą wektorów podpierających (Support Vector Machine – SVM) zaliczaną do tzw. uczenia maszynowego (Machine Learning). W badaniach wykorzystano bazę danych o konstrukcji, stanie technicznym i potencjalnych przyczynach uszkodzeń 199 nieremontowanych budynków w wieku do 20 lat, o tradycyjnej konstrukcji murowanej, usytuowanych na terenie górniczym Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Przeprowadzona analiza pozwoliła na jakościową ocenę wpływu oddziaływań górniczych na zakres uszkodzeń badanych budynków.
Źródło:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 1; 69-79
2300-5130
2300-8903
Pojawia się w:
Czasopismo Inżynierii Lądowej, Środowiska i Architektury
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction
Ocena zdolności ekstremalnych maszyn uczących (ELM) do przewidywania poziomu wód gruntowych
Autorzy:
Yadav, B.
Ch, S.
Mathur, S.
Adamowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/293096.pdf
Data publikacji:
2017
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
extreme learning machine (ELM)
forecasting
groundwater level
support vector machine (SVM)
water resource management
maszyna uczenia ekstremalnego (ELM)
maszyna wektorów nośnych SVM
poziom wód gruntowych
prognozowanie
zarządzanie zasobami wodnymi
Opis:
Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.
Na całym świecie fluktuacje poziomów wód gruntowych stanowią ważny temat badań hydrologicznych. Rosnące potrzeby wodne, błędne praktyki irygacyjne, niewłaściwa gospodarka glebowa i niekontrolowana eksploatacja poziomów wodonośnych są powodami, dla których poziom wód gruntowych podlega fluktuacjom. Dla skutecznego zarządzania zasobami wód gruntowych istotne jest dysponowanie dokładnymi zapiskami i zdolność prognozowania poziomu tych wód. Rozwój technik komputerowych (modele wykorzystujące dane) w dziedzinie hydrologii ma istotny potencjał z powodu niepewnego i złożonego charakteru systemów wód gruntowych. W prezentowanych badaniach wykorzystano dwie techniki komputerowe: maszynę uczenia ekstremalnego (ELM) i maszynę wektorów nośnych (SVM – ang. support vector machine) do przewidywania poziomów wód gruntowych w dwóch studzienkach obserwacyjnych w Kanadzie. Do porównawczych badań modeli wykorzystano zestaw danych miesięcznych z ośmiu lat (2006–2014), składający się z danych hydrologicznych i meteorologicznych (opady, temperatura, ewapotranspiracja, poziom wody). Wymienione zmienne zastosowano w rozmaitych kombinacjach do jedno- i wieloparametrycznej analizy modeli. Wyniki dowodzą, że model ELM ma lepsze zdolności przewidywania miesięcznych poziomów wód gruntowych w porównaniu z modelem SVM.
Źródło:
Journal of Water and Land Development; 2017, 32; 103-112
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic parametric fault detection in complex analog systems based on a method of minimum node selection
Autorzy:
Bilski, A.
Wojciechowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/330761.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
complex analog system
support vector machine (SVM)
tabu search
genetic algorithm
parametric fault detection
system analogowy
maszyna wektorów wspierających
metoda tabu search
algorytm genetyczny
detekcja uszkodzeń
Opis:
The aim of this paper is to introduce a strategy to find a minimal set of test nodes for diagnostics of complex analog systems with single parametric faults using the support vector machine (SVM) classifier as a fault locator. The results of diagnostics of a video amplifier and a low-pass filter using tabu search along with genetic algorithms (GAs) as node selectors in conjunction with the SVM fault classifier are presented. General principles of the diagnostic procedure are first introduced, and then the proposed approach is discussed in detail. Diagnostic results confirm the usefulness of the method and its computational requirements. Conclusions on its wider applicability are provided as well.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2016, 26, 3; 655-668
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Traffic fatalities prediction based on support vector machine
Autorzy:
Li, T.
Yang, Y.
Wang, Y.
Chen, C.
Yao, J.
Powiązania:
https://bibliotekanauki.pl/articles/223743.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
traffic accident
support vector machine
SVM
particle swarm optimization (PSO)
PSO
prediction model
optimal parameters
wypadek drogowy
Particle Swarm Optimization
model prognostyczny
optymalne parametry
Opis:
To effectively predict traffic fatalities and promote the friendly development of transportation, a prediction model of traffic fatalities is established based on support vector machine (SVM). As the prediction accuracy of SVM largely depends on the selection of parameters, Particle Swarm Optimization (PSO) is introduced to find the optimal parameters. In this paper, small sample and nonlinear data are used to predict fatalities of traffic accident. Traffic accident statistics data of China from 1981 to 2012 are chosen as experimental data. The input variables for predicting accident are highway mileage, vehicle number and population size while the output variables are traffic fatality. To verify the validity of the proposed prediction method, the back-propagation neural network (BPNN) prediction model and SVM prediction model are also used to predict the traffic fatalities. The results show that compared with BPNN prediction model and SVM model, the prediction model of traffic fatalities based on PSO-SVM has higher prediction precision and smaller errors. The model can be more effective to forecast the traffic fatalities. And the method using particle swarm optimization algorithm for parameter optimization of SVM is feasible and effective. In addition, this method avoids overcomes the problem of “over learning” in neural network training progress.
Źródło:
Archives of Transport; 2016, 39, 3; 21-30
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
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