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Wyświetlanie 1-10 z 150
Tytuł:
A Neural Network Model of an Ising Spin Glass
Autorzy:
Wilson, K. F.
Goossens, D. J.
Powiązania:
https://bibliotekanauki.pl/articles/2013998.pdf
Data publikacji:
2000-05
Wydawca:
Polska Akademia Nauk. Instytut Fizyki PAN
Tematy:
75.10.-b
75.10.Nr
84.35.+i
Opis:
The behaviour of an Ising spin glass (S=1/2) with infinite range interactions is modelled using a numerical simulation based on a neural network. Thermodynamic variables are defined on the network, and are found to obey the Thouless-Anderson-Palmer theory when the applied magnetic field is zero. When a magnetic field is applied along the spin direction, complex field-dependent behaviour appears, including a state in which the Edwards-Anderson order parameter is independent of temperature below the critical temperature.
Źródło:
Acta Physica Polonica A; 2000, 97, 5; 983-986
0587-4246
1898-794X
Pojawia się w:
Acta Physica Polonica A
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:
Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:
https://bibliotekanauki.pl/articles/1838288.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
air pollutant
ANN
MATLAB
forecasting
Opis:
Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:
Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:
Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:
https://bibliotekanauki.pl/articles/1838392.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
air pollutant
ANN
MATLAB
forecasting
Opis:
Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:
Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Neural Network Model for Object Mask Detection in Medical Images
Autorzy:
Tereikovskyi, Igor
Korchenko, Oleksander
Bushuyev, Sergey
Tereikovskyi, Oleh
Ziubina, Ruslan
Veselska, Olga
Powiązania:
https://bibliotekanauki.pl/articles/2200721.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
model
neural network
object mask
medical images
Opis:
In modern conditions in the field of medicine, raster image analysis systems are becoming more widespread, which allow automating the process of establishing a diagnosis based on the results of instrumental monitoring of a patient. One of the most important stages of such an analysis is the detection of the mask of the object to be recognized on the image. It is shown that under the conditions of a multivariate and multifactorial task of analyzing medical images, the most promising are neural network tools for extracting masks. It has also been determined that the known detection tools are highly specialized and not sufficiently adapted to the variability of the conditions of use, which necessitates the construction of an effective neural network model adapted to the definition of a mask on medical images. An approach is proposed to determine the most effective type of neural network model, which provides for expert evaluation of the effectiveness of acceptable types of models and conducting computer experiments to make a final decision. It is shown that to evaluate the effectiveness of a neural network model, it is possible to use the Intersection over Union and Dice Loss metrics. The proposed solutions were verified by isolating the brachial plexus of nerve fibers on grayscale images presented in the public Ultrasound Nerve Segmentation database. The expediency of using neural network models U-Net, YOLOv4 and PSPNet was determined by expert evaluation, and with the help of computer experiments, it was proved that U-Net is the most effective in terms of Intersection over Union and Dice Loss, which provides a detection accuracy of about 0.89. Also, the analysis of the results of the experiments showed the need to improve the mathematical apparatus, which is used to calculate the mask detection indicators.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 1; 41--46
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Convolutional Neural Network Model Based Human Wearable Smart Ring System : Agent Approach
Autorzy:
Bhajantri, Lokesh B.
Kagalkar, Ramesh M.
Ranjolekar, Pundalik
Powiązania:
https://bibliotekanauki.pl/articles/2055210.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
internet of things
agents
convolutional neural networks
smart ring system
agent knowledge base
Opis:
The Internet of Things has a set of smart objects with smart connectivity that assists in monitoring real world environment during emergency situations. It could monitor the various applications of emergency situations such as road accidents, criminal acts including physical assaults, kidnap cases, and other threats to people’s way of life. In this work, the proposed work is to afford real time services to users in emergency situations through Convolutional Neural Networks in terms of efficiency and reliable services. Finally, the proposed work has simulated with respect to the performance parameters of the proposed scheme like the probability of accuracy and processing time.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 4; 673--678
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Grid Search of Convolutional Neural Network model in the case of load forecasting
Autorzy:
Tran, Thanh Ngoc
Powiązania:
https://bibliotekanauki.pl/articles/1841362.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
load forecasting
grid search
convolutional neural network
Opis:
The Convolutional Neural Network (CNN) model is one of the most effective models for load forecasting with hyperparameters which can be used not only to determine the CNN structure and but also to train the CNN model. This paper proposes a frame work for Grid Search hyperparameters of the CNN model. In a training process, the optimalmodels will specify conditions that satisfy requirement for minimum of accuracy scoresof Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). In the testing process, these optimal models will be used to evaluate the results along with all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. Load demand data of Queensland (Australia) and Ho Chi Minh City (Vietnam) were utilized to verify the accuracy and reliability of the Grid Search framework.
Źródło:
Archives of Electrical Engineering; 2021, 70, 1; 25-36
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Neural Network Model for Control of Operating Modes of Crushing and Grinding Complex
Autorzy:
Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Borychenko, Olena
Kalinchyk, Vitalii
Powiązania:
https://bibliotekanauki.pl/articles/2174915.pdf
Data publikacji:
2022
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
classification
modelling
neural network
radial basis function network
RBF
multilayer perceptron
MLP
Opis:
This article investigates the application of neural network models to create automated control systems for industrial processes. We reviewed and analysed works on dispatch control and evaluation of equipment operating modes and the use of artificial neural networks to solve problems of this type. It is shown that the main requirements for identification models are the accuracy of estimation and ease of algorithm implementation. It is shown that artificial neural networks meet the requirements for accuracy of classification problems, ease of execution and speed. We considered the structures of neural networks that can be used to recognise the modes of operation of technological equipment. Application of the model and structure of networks with radial basis functions and multilayer perceptrons for identifying the mode of operation of equipment under given conditions is substantiated. The input conditions for constructing neural network models of two types with a given three-layer structure are offered. The results of training neural models on the model of a multilayer perceptron and a network with radial basis functions are presented. The estimation and comparative analysis of models depending on model parameters are made. It is shown that networks with radial basis functions offer greater accuracy in solving identification problems. The structural scheme of the automated process control system with mode identification based on artificial neural networks is offered.
Źródło:
Rocznik Ochrona Środowiska; 2022, 24; 26--40
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting the Energy Consumption of an Industrial Enterprise Based on the Neural Network Model
Autorzy:
Kalinchyk, Vasyl
Meita, Olexandr
Pobigaylo, Vitalii
Kalinchyk, Vitalii
Filyanin, Danylo
Powiązania:
https://bibliotekanauki.pl/articles/2069887.pdf
Data publikacji:
2021
Wydawca:
Politechnika Koszalińska. Wydawnictwo Uczelniane
Tematy:
electrical load
daily schedule
modelling
neural network
multilayer perceptron
MLP
Opis:
This research paper investigates the application of neural network models for forecasting in energy. The results of forecasting the weekly energy consumption of the enterprise according to the model of a multilayer perceptron at different values of neurons and training algorithms are given. The estimation and comparative analysis of models depending on model parameters is made.
Źródło:
Rocznik Ochrona Środowiska; 2021, 23; 484--492
1506-218X
Pojawia się w:
Rocznik Ochrona Środowiska
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection
Autorzy:
Brunner, Csaba
Kő, Andrea
Fodor, Szabina
Powiązania:
https://bibliotekanauki.pl/articles/2147134.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
intrusion detection
neural network
ensemble classifiers
hyperparameter optimization
sparse autoencoder
NSL-KDD
machine learning
Opis:
Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial neural network model for predicting air pollution. Case study of the Moravica district, Serbia
Autorzy:
Blagojević, M.
Papić, M.
Vujičić, M.
Šućurović, M.
Powiązania:
https://bibliotekanauki.pl/articles/207320.pdf
Data publikacji:
2018
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
air pollution
neural network
Serbia
data processing
zanieczyszczenie powietrza
sieć neutronowa
przetwarzanie danych
Opis:
An example of artificial neural network model for predicting air pollution has been presented. The research was conducted in Serbia, the Moravica District, on the territory of two municipalities (LuCani and Ivanjica) and the town Čačak. The level of air pollution was classified by a neural network model according to the input data: municipality, site, year, levels of soot, sulfur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter. The model was evaluated using a lift chart and a root mean square error (RMSE) has been determined, whose value was 0.0635. A multilayer perceptron has also been created and trained with a back propagation algorithm. The neural network was tested with the data mining extensions (DMX) queries. The results have been obtained for air pollution based on new input data that can be used to predict the level of pollution in future if new measurements are carried out. A web-based application was designed for displaying the results.
Źródło:
Environment Protection Engineering; 2018, 44, 1; 129-139
0324-8828
Pojawia się w:
Environment Protection Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
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