scholarly journals Explaining Cultural Capital through Combining Different Dimensions of Social Capital: A Fussy Analysis Based on Artificial Neural Network Approach (ANNFIS) (A Case Study of Citizens Aged 18 and Above in Tehran)

2016 ◽  
Vol 3 (2) ◽  
pp. 46
Author(s):  
Ghorbanali Ebrahimi ◽  
Hadi Razeghimaleh
Keyword(s):  
Neural Network ◽  
Social Capital ◽  
Artificial Neural Network ◽  
Cultural Capital ◽  
Multivariate Linear Regression Analysis ◽  
The South ◽  
The North ◽  
The Mean ◽  
Artificial Neural ◽  
North And South

<p>The main objective of this study was to find out whether social capital and its dimensions affect the cultural capital of citizens in Tehran, and whether there is any difference in the social capital and cultural capital in the north and south urban neighborhoods. To answer these questions, a fuzzy questionnaire for collecting the data was designed. The research method in this study was based on Artificial Neural Network -Fuzzy Inference System (ANNFIS). Statistical population included individuals aged 18 and above residing in Tehran, and sample size consisted of 2538 people.</p><p>The findings of this study indicated that there is a significant difference in the cultural capital between north and south neighborhoods in Tehran. The mean of cultural capital in the south neighborhoods (2.49 out of 10) was higher than that of north neighborhoods (6.77 out of 10). Furthermore, the degree of neighborhood social capital was different between the north and south neighborhoods of Tehran, and this difference was statistically significant, so that the mean of social capital in the south neighborhoods (6.75 out of 10) was greater than that of north neighborhoods (2.88 out of 10).</p>Multivariate linear regression analysis to explain cultural capital has revealed that social trust (- 0.502) and relation networks (- 0.087) exerted the highest and lowest impact on the dependent variable, respectively. It should be noted that, of the three variables entered into the regression equation, all variables have remained in the equation. It should be noted that the effects of all variables on the dependent variable of cultural capital was negative.

scholarly journals Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Best Architecture of an Artificial Neural Network to Model Prefilming Airblast Atomization: Not So Deep Learning

2021 ◽  
Author(s):  
Geoffroy Chaussonnet ◽  
Sebastian Gepperth ◽  
Simon Holz ◽  
Rainer Koch ◽  
Hans-Jörg Bauer
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Space ◽  
Middle Layer ◽  
Ann Model ◽  
Deformation Velocity ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Domain Of Parameters

Abstract A fully connected Artificial Neural Network (ANN) is used to predict the mean spray characteristics of prefilming airblast atomization. The model is trained from the planar prefilmer experiment from the PhD thesis of Gepperth (2020). The output of the ANN model are the Sauter Mean Diameter, the mean droplet axial velocity, the mean ligament length and the mean ligament deformation velocity. The training database contains 322 different operating points. Two types of model input quantities are investigated and compared. First, nine dimensional parameters are used as inputs for the model. Second, nine non-dimensional groups commonly used for liquid atomization are derived from the first set of inputs. The best architecture is determined after testing over 10000 randomly drawn ANN architectures, with up to 10 layers and up to 128 neurons per layer. The striking results is that for both types of model, the best architectures consist of only 3 hidden layer in the shape of a diabolo. This shape recalls the shape of an autoencoder, where the middle layer would be the feature space of reduced dimensionality. It was found that the model with dimensional input quantities always shows a lower test and validation errors than the one with non-dimensional input quantities. In general, the two types of models provide comparable accuracy, better than typical correlations of SMD and droplet velocity. Finally the extrapolation capability of the models was assessed by a training them on a confined domain of parameters and testing them outside this domain.

scholarly journals Methods for Determining Nitrogen, Phosphorus, and Potassium (NPK) Nutrient Content Using Features from Accelerated Segment Test (FAST)

2020 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Raden Sumiharto ◽  
Ristya Ginanjar Putra ◽  
Samuel Demetouw
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nutrient Content ◽  
Scale Invariant ◽  
Rice Field Soil ◽  
Long Time ◽  
The Mean ◽  
Artificial Neural ◽  
Phosphorus And Potassium ◽  
Scale Invariant Feature

Nutrient Content NPK is macro nutrient content that important for the growth of a plant. The measurement of NPK conducted periodically, but the measurement using laboratories test need relatively long time. This Research is conducted to determine the nutrient content of the soil, consisted of nitrogen, phosphor, and calcium (NPK) using digital image processing based on Features from Accelerated Segment Test (FAST) and backpropagation artificial neural network. The data sample in this research taken from the rice field soil in Daerah Istimewa Yogyakarta province where the soil taken at the length of 30 cm to 110 cm with l20 cm interval, and -30° to 30° degree with interval 10°. The model from this measurement system based on texture’s characteristic that extracted using Scale Invariant Feature Transform from soil’s image that already passed pre-processing process. The characteristic result will be the input from the artificial neural network with a variation on parameter’s model. The model tested for the purpose of knowing the influence the distance and degree where the image taken and the influence of parameter’s artificial neural network. The result from the research, is a accurate value of the measurement for each nutrient in the soil, nitrogen (94.86%), phosphor (58.93%) and calcium (63.57%), with the mean 72,46%. The corresponding result obtained from image taken with optimal height of 70 cm and degree 0o

scholarly journals Rapid Modeling of the Sound Speed Field in the South China Sea Based on a Comprehensive Optimal LM-BP Artificial Neural Network

2021 ◽  
Vol 9 (5) ◽  
pp. 488
Author(s):  
Jin Huang ◽  
Yu Luo ◽  
Jian Shi ◽  
Xin Ma ◽  
Qian-Qian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
South China Sea ◽  
Network Model ◽  
South China ◽  
Sound Speed ◽  
The South China Sea ◽  
The South ◽  
China Sea ◽  
Artificial Neural

Ocean sound speed is an essential foundation for marine scientific research and marine engineering applications. In this article, a model based on a comprehensive optimal back propagation artificial neural network model is developed. The Levenberg–Marquardt algorithm is used to optimize the model, and the momentum term, normalization, and early termination method were used to predict the high precision marine sound speed profile. The sound speed profile was described by five indicators: date, time, latitude, longitude, and depth. The model used data from the CTD observation dataset of scientific investigation over the South China Sea (2009–2012) (108°–120°E, 6°–8°N), which includes comprehensive scientific investigation data from four voyages. The feasibility of modeling the sound speed field in the South China Sea is investigated. The proposed model uses the momentum term, normalization, and early termination in a traditional BP artificial neural network structure and mitigates issues with overtraining and difficulty when determining the BP neural network parameters. With the LM algorithm, a fast-modeling method for the sound field effectively achieves the precision requirement for sound speed prediction. Through the prediction and verification of the data from 2009 to 2012, the newly proposed optimized BP network model is shown to dramatically reduce the training time and improve precision compared to the traditional network model. Results showed that the root mean squared error decreased from 1.7903 m/s to 0.95732 m/s, and the training time decreased from 612.43 s to 4.231 s. Finally, the sound ray tracing simulations confirm that the model meets the accuracy requirements of acoustic sounding and verify the model’s feasibility for the real-time prediction of the vertical sound speed in saltwater bodies.

Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

10.17158/320 ◽  
2014 ◽  
Vol 18 (2) ◽  
Author(s):  
Eric John G. Emberda ◽  
Den Ryan L. Dumas ◽  
Timothy Pierce M. Rentillo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Feed Forward ◽  
Feed Forward Back Propagation ◽  
Forecast Data ◽  
The Mean ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.</p>

scholarly journals Multilayered artificial neural network for performance prediction of an adiabatic solar liquid desiccant dehumidifier

2019 ◽  
Vol 14 (3) ◽  
pp. 351-363
Author(s):  
Andrew Y A Oyieke ◽  
Freddie L Inambao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vapour Pressure ◽  
Air Conditioning ◽  
Ann Model ◽  
Liquid Desiccant ◽  
Humidity Ratio ◽  
Packed Tower ◽  
The Mean ◽  
Artificial Neural

Abstract In this study, a multi-layered artificial neural network (ANN) algorithm was developed and trained to predict the performance of a solar powered liquid desiccant air conditioning (LDAC) system particularly the adiabatic packed tower dehumidifier using Lithium Bromide (LiBr) desiccant. A reinforced technique of supervised learning based on error correction principle rule coupled with perceptron convergence theorem was applied to create the algorithm. The parameters such as temperature, flow rates and humidity ratio of both air and desiccant fluid were fed as inputs to the ANN algorithm and their respective outputs used to determine dehumidifier effectiveness and moisture removal rate (MRR). The ANN model when subjected to validity tests using vapour pressure of LiBr desiccant solution at specific random temperatures and concentrations, gave astounding outcomes with precise estimation to R2 values of 0.9999 for all desiccant concentration levels. Due to the variation in solar radiation, the MRR and effectiveness fluctuated with the change in desiccant and air temperatures, giving maximum differences of 0.2 g/s and 1.8% respectively between the predicted and measured values depicting a perfect match. With respect to humidity ratio, MRR was accurately predicted by ANN algorithm with maximum difference of 3.4969% while the mean variation was −0.5957%. With respect to air temperature, the dehumidifier effectiveness was perfectly predicted by the ANN algorithm to an average accuracy of 0.53% and extreme positive deviation of 4.14%. The MRR was replicated to a mean variation of 0.013% and highest point difference of 0.08%. In all the above cases, the mean and maximum differences between the ANN model and experimental values were far below the allowable limit of ± 5%, hence the algorithm was deemed to be successful and could find use in air conditioning scenarios. The ANN algorithm’s capability and flexibility test of processing unforeseen inputs was accurate with negligible deviations and prospects of predicting the desiccant’s vapour pressure, dehumidifier effectiveness and MRR within all ranges of temperature and concentration which then eliminates the need for use of charts.

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