Wyniki wyszukiwania - AGRO

1

Pedotransfer function for determining saturated hydraulic conductivity using artificial neural network (ANN)

100%

Kruk E., Malec M., Klatka S., Brodzinska-Cygan A., Kolodziej J.

Acta Scientiarum Polonorum. Formatio Circumiectus

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2017

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tom 16

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nr 4

2

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Model development of the external friction of granular vegetable materials on the basis of artificial neural networks

100%

Francik S., Fraczek J.

International Agrophysics

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2001

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tom 15

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nr 4

3

An artificial neural network is capable of predicting odour intensity

100%

Linder R., Zamelczyk-Pajewska M., Poppl S. J., Kosmider J.

Polish Journal of Environmental Studies

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2005

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tom 14

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nr 4

For the first time, an artificial neural network (ANN) has been employed for predicting the intensity of gas mixtures comprising different odour components. Sensory assessments are necessary but they are time-consuming, harmful, and expensive. Therefore, an instrumental quantification of subjective sensory assessments is highly desired. Because of nonlinearities arising in sensory-instrumental relationships, we decided for an ANN that was trained by gas chromatographic signals of gas mixtures. The ANN could be demonstrated to classify odour intensity fairly well.

4

Novelty based feedback regulation in artificial neural networks

88%

Rychwalska A., Jablonski P., Zochowski M., Nowak A.

Acta Neurobiologiae Experimentalis

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2005

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tom 65

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nr 4

5

Application of neural networks and support vector machine for significant wave height prediction

88%

Berbic J., Ocvirk E., Carevic D., Loncar G.

Oceanologia

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2017

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tom 59

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nr 3

For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN) and support vector machine (SVM). The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

6

Studies on the ANN implementation in the macro BIM cost analyzes

88%

Juszczyk M.

Przegląd Naukowy. Inżynieria i Kształtowanie Środowiska

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2017

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tom 26

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nr 2[76]

Studies on the ANN implementation in the macro BIM cost analyzes. The paper presents an approach which combines the concept of macro-level BIM-based cost analyzes analyzes and application of artificial intelligence tools – namely artificial neural networks. Discussion and foundations of the proposed approach are introduced in the paper to clarify the problem’s core. An exemplary case study reports the results of initial studies on the application of neural networks for the purposes of BIM-based cost analysis of a buildings’ fl oor structural frame. The results obtained justify the proposal of application of neural networks as a supportive mathematical tool in the problem presented in the paper.

7

Neural network simulation in running of acetic acid synthesis unit while start-up

88%

Porkuian O., Samojlova Z.

Teka Komisji Motoryzacji i Energetyki Rolnictwa

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2013

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tom 13

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nr 3

8

Weekly urban water demand forecasting using a hybrid waveletbootstrap-artificial neural network approach

88%

Adamowski K., Adamowski J. F., Seidou O., Ozga-Zielinski B.

Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation

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2014

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tom 46

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nr 3

This study developed a hybrid wavelet–bootstrapartifi cial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis signifi cantly improved the model’s performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confi dence bands; this can be helpful in operational water demand forecasting.

9

Avtomatizacija tekhnologicheskikh processov sakharnogo proizvodstva na osnove intelektual'nogo podkhoda

75%

ljashenko S., Ljashenko A.

Motrol. Motoryzacja i Energetyka Rolnictwa

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2013

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tom 15

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nr 7

10

The adaptive system on the basis of artificial neuron networks

75%

Ulshin V., Yurkov D.

Teka Komisji Motoryzacji i Energetyki Rolnictwa

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2010

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tom 10D

11

Investigating the role of atmospheric variables on sea level variations in the Eastern Central Red Sea using an artificial neural network approach

75%

Zubier K. M., Eyouni L. S.

Oceanologia

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2020

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tom 62

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nr 3

12

Modelling values of river macrophyte metrics using artificial neural networks

75%

Gebler D., Kayzer D., Budka A., Szoszkiewicz K.

Infrastruktura i Ekologia Terenów Wiejskich

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2012

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nr 1/IV

The results of field research at 230 river sections located throughout Poland were used to examine the possibility of predicting values of macrophyte metrics of ecological status. Artificial intelligence methods such as artificial neural networks were used in the modelling. The physicochemical parameters of water (alkalinity, conductivity, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand) were used as the explanatory (modelling) variables. The explained (modelled) parameters were the Polish MIR (Macrophyte Index for Rivers), the British MTR (Mean Trophic Rank) and the French IBMR (River Macrophytes Biological Index). The quality of the constructed models was assessed using the normalized root mean square error (NRMSE) and the r–Pearson’s linear correlation coefficient between variables modelled by the networks and calculated on the basis of the botanical research. These analyses demonstrated that the network modelling MIR values had the highest accuracy. The lowest prediction accuracy was obtained for MTR and IBMR indices. The differences between particular models are likely to result from better adjustment of the Polish method to local rivers (particularly in terms of indicator species used).

13

A generalized artificial neural network model for deep-bed drying of paddy

75%

Bakhshipour A., Zare D.

Electronic Journal of Polish Agricultural Universities. Series Agricultural Engineering

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2017

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tom 20

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nr 1

14

Identification of Lactobacillus strains at the species level using FTIR spectroscopy and artificial neural networks

75%

Dziuba B.

Polish Journal of Food and Nutrition Sciences

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2007

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tom 57

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nr 3

301-306

Fourier-transform infrared (FTIR) spectroscopy and artificial neural networks were used to identify bacteria of the genus Lactobacillus at the species level. A previously developed method for measuring FTIR spectra, and a strategy for their analysis provided the basis for selecting the FTIR spectra of the tested bacteria, and for creating a spectral library, as described elsewhere [Dziuba et al., 2007b]. In our previous study [Dziuba et al., 2007b] we demonstrated that the FTIR spectral characteristics of Lactobacillus strains based exclusively on the differentiation index D, calculated from the Pearson’s correlation coefficient, and cluster analysis are not sufficient to describe the relationships between FTIR spectra and bacteria as molecular systems in a way that would permit their proper identification. Thus, research was launched in which the spectra collected in the above library were used for developing artificial neural networks. The practical value of these networks was verified based on the results of identification of 17 bacterial strains of known taxonomy as well as 7 strains isolated from dairy products and identified on the basis of their taxonomy and biochemical tests. The application of artificial neural networks, i.e. the most advanced chemometric method, to analysis of FTIR spectra enabled correct identification of 93% of bacterial strains of the genus Lactobacillus.

15

Pomegranate fruit quality assessment using machine intelligence and wavelet features

75%

Kumar A., Rajpurohit V. S., Jirage B. J.

Journal of Horticultural Research

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2018

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tom 26

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nr 1

16

Concept of soil temperature coefficient for determining spatial distribution of soil temperature, using physiographic parameters of the basin and artificial neutral network (ANN)

75%

Kruk E., Malec M., Klatka S., Brodzinska-Cygan A.

Acta Scientiarum Polonorum. Formatio Circumiectus

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2018

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tom 17

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nr 2

The paper presents the concept of soil temperature coefficient, as a ratio of soil temperature in the given point on the area of a basin and soil temperature in the basal point located within the watershed. For modelling the distribution of the soil temperature coefficient depending on selected soil and physiographic parameters, artificial neural networks (ANN) were used. ANN were taught based on empirical data, which covered measurements of soil temperature in 126 points, in the layer of soil at the depth of 0–10 cm, within the area of the Mątny stream basin located in the Gorce mountain range of West Carpathians. The area size of the basin amounts to 1.47 km2. Temperature was measured by means of a TDR device. The soil and physiographic parameters included: slopes, flow direction, clay content, height above sea level, exposition, slope shape, placement on the slope, land-use, and hydrologic group. Parameters were generated using DEM of 5m spatial resolution and soil maps, using the ArcGIS program. The MLP 10-8-1 model proved to be the best fitted neural network, with 8 neurons in the hidden layer. The quality parameters were satisfactory. For the learning set, the quality parameter amounted to 0.805; for the testing set, 0.894; and for the validating set, 0.820. Global sensitivity analysis facilitated the assessment of percentage shares, contributing to the soil temperature ratio. Land use (25.0%) and exposition (20.5%) had the highest impact on of the aforementioned ratio, while the placement on the slope and flow direction had the lowest impact.

17

Machine learning methods applied to sea level predictions in the upper part of a tidal estuary

75%

Guillou N., Chapalain G.

Oceanologia

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2021

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tom 63

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nr 4

18

Assessment of product configuration management with usage of artificial neural networks

75%

Swiderski A.

Towaroznawcze Problemy Jakości. Polskie Towarzystwo Towaroznawcze

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2009

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nr 3

35-43

19

Prediction of CEC using fractal parameters by artificial neural networks

75%

Bayat H., Davatgar N., Jalali M.

International Agrophysics

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2014

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tom 28

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nr 2

The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.

20

Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks

75%

Adamopoulos S., Karageorgos A., Rapti E., Birbilis D.

Drewno. Prace Naukowe. Doniesienia. Komunikaty

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2016

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tom 59

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nr 198

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