scholarly journals Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

2021 ◽  
Vol 13 (7) ◽  
pp. 3699
Author(s):  
Jarosław Malczewski ◽  
Wawrzyniec Czubak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Milk Powder ◽  
Training Data ◽  
Series Data ◽  
Reconstruction Algorithms ◽  
Price Series ◽  
Skimmed Milk ◽  
The Right ◽  
Skimmed Milk Powder

The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.

Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets

2017 ◽  
Vol 46 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Dennis Bergmann ◽  
Declan O’Connor ◽  
Andreas Thümmel
Keyword(s):  
Crude Oil ◽  
Milk Powder ◽  
Multivariate Garch ◽  
Volatility Transmission ◽  
Crude Oil Prices ◽  
Multivariate Garch Model ◽  
Oil Markets ◽  
Skimmed Milk ◽  
Derivative Markets ◽  
Skimmed Milk Powder

Price and volatility transmission effects between European Union (EU) and World skimmed milk powder (SMP) prices, as well as those between both SMP series, soybeans and crude oil prices from 2004 to 2014 were analysed using a vector error correction model combined with a multivariate GARCH model. The results show significant transmission effects between EU and World SMP prices, but no significant transmission effects from soybeans or crude oil to either of the SMP prices. For policymakers and modellers, these results indicate the need to consider World SMP prices when considering EU prices. On the other hand, the finding of no transmission effects from soybean to SMP prices reduces the opportunity for a successful cross-hedging for dairy commodities using well-established soybean derivative markets.

scholarly journals Feasibility of using jack fruit seeds powder as a substitute of skimmed milk powder for making traditional yogurt

2020 ◽  
pp. 1
Author(s):  
Shabbir Ahmed ◽  
Most Khairunnesa ◽  
Mst Habiba ◽  
Md Islam ◽  
S Hoque ◽  
...  
Keyword(s):  
Milk Powder ◽  
Skimmed Milk ◽  
Skimmed Milk Powder

scholarly journals SATELLITE-DERIVED BATHYMETRY USING CONVOLUTIONAL NEURAL NETWORKS AND MULTISPECTRAL SENTINEL-2 IMAGES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 201-207
Author(s):  
Y. A. Lumban-Gaol ◽  
K. A. Ohori ◽  
R. Y. Peters
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Window Size ◽  
Short Wave ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Linear Transform ◽  
Band Combinations ◽  
Sentinel 2

Abstract. Satellite-Derived Bathymetry (SDB) has been used in many applications related to coastal management. SDB can efficiently fill data gaps obtained from traditional measurements with echo sounding. However, it still requires numerous training data, which is not available in many areas. Furthermore, the accuracy problem still arises considering the linear model could not address the non-relationship between reflectance and depth due to bottom variations and noise. Convolutional Neural Networks (CNN) offers the ability to capture the connection between neighbouring pixels and the non-linear relationship. These CNN characteristics make it compelling to be used for shallow water depth extraction. We investigate the accuracy of different architectures using different window sizes and band combinations. We use Sentinel-2 Level 2A images to provide reflectance values, and Lidar and Multi Beam Echo Sounder (MBES) datasets are used as depth references to train and test the model. A set of Sentinel-2 and in-situ depth subimage pairs are extracted to perform CNN training. The model is compared to the linear transform and applied to two other study areas. Resulting accuracy ranges from 1.3 m to 1.94 m, and the coefficient of determination reaches 0.94. The SDB model generated using a window size of 9x9 indicates compatibility with the reference depths, especially at areas deeper than 15 m. The addition of both short wave infrared bands to the four visible bands in training improves the overall accuracy of SDB. The implementation of the pre-trained model to other study areas provides similar results depending on the water conditions.

Uncertainty quantification in fault detection using convolutional neural networks

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Runhai Feng ◽  
Dario Grana ◽  
Niels Balling
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Reservoir Characterization ◽  
Epistemic Uncertainty ◽  
Confidence Regions ◽  
Training Data ◽  
Model Parameters ◽  
Sigmoid Function ◽  
Learning Tools

Segmentation of faults based on seismic images is an important step in reservoir characterization. With the recent developments of deep-learning methods and the availability of massive computing power, automatic interpretation of seismic faults has become possible. The likelihood of occurrence for a fault can be quantified using a sigmoid function. Our goal is to quantify the fault model uncertainty that is generally not captured by deep-learning tools. We propose to use the dropout approach, a regularization technique to prevent overfitting and co-adaptation in hidden units, to approximate the Bayesian inference and estimate the principled uncertainty over functions. Particularly, the variance of the learned model has been decomposed into aleatoric and epistemic parts. The proposed method is applied to a real dataset from the Netherlands F3 block with two different dropout ratios in convolutional neural networks. The aleatoric uncertainty is irreducible since it relates to the stochastic dependency within the input observations. As the number of Monte-Carlo realizations increases, the epistemic uncertainty asymptotically converges and the model standard deviation decreases, because the variability of model parameters is better simulated or explained with a larger sample size. This analysis can quantify the confidence to use fault predictions with less uncertainty. Additionally, the analysis suggests where more training data are needed to reduce the uncertainty in low confidence regions.

Effect of Storage on Physio-Chemical Properties of Peanut Yogurt

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Naukhaiz Abbas ◽  
Zainab Sharmeen ◽  
Shahid Bashir ◽  
Misbah Arshad ◽  
Zargham Mazhar
Keyword(s):  
Chemical Analysis ◽  
Milk Powder ◽  
Chemical Properties ◽  
Chemical Parameters ◽  
Total Solids ◽  
Storage Treatment ◽  
Skimmed Milk ◽  
Skimmed Milk Powder

Peanuts may be consumed in a variety of processed forms like roasted, raw and processed etc. andrepresent as a multimillion dollar crop worldwide with many potential dietary benefits as it contains highprotein and health effective oils. Objective: The present investigation was planned to evaluate thephysio-chemical properties of peanut milk yogurt by the addition of different concentration of peanut milk(0 %, 10 %, 20 % and 30 %), skimmed milk liquid (60 %, 70 %, 80 %, and 90 %), skimmed milk powder (9 %)and sugar (1 %). Methods: The physio-chemical tests (pH, acidity, moisture, ash, fat, protein, syneresis,and viscosity) were examined after every 5 days of interval for a period of 15 days at 4 ºC. Results: Theresults of physio-chemical analysis revealed that pH, ash, fat, protein and viscosity decrease duringstorage period where as acidity, moisture and rate of syneresis increased during storage. Treatment T1(10 % peanut milk) was comparatively best for manufacturing of peanut milk yogurt followed by T2 (20 %peanut milk + 70 % skimmed milk liquid + 9 % skimmed milk powder + 1 % sugar) while peanut milk yogurtfrom (30 % peanut milk + 60 % skimmed milk liquid + 9 % skimmed milk powder + 1 % sugar) had the lowestdegree of firmness. Conclusions: It was noticed that correlation among fat, total solids and proteincontents in peanut milk affect the extent of serum separation and pH of yogurt. The storage hadsignificant effects on all physio-chemical parameters. Treatments had significant effect on all physiochemicalparameters.

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