The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed hybrid models in the prediction process when conducting simulation for different sample sizes and when applied to the daily crude oil price time series data, were more efficient than the single models, and the comparison between the single models and the proposed hybrid models was made by comparison scale, mean square error (MSE), and the results showed that the proposed hybrid models have the ability to predict crude oil prices, as they gave more accurate and efficient results.
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As material flow cost accounting technology focuses on the most efficient use of resources like energy and materials while minimizing negative environmental effects, the research aims to show how this technology can be applied to promote green productivity and its reflection in attaining sustainable development. In addition to studying sustainability, which helps to reduce environmental impacts and increase green productivity, the research aims to demonstrate the knowledge bases for accounting for the costs of material flow and green productivity. It also studies the technology of accounting for the costs of material flow in achieving sustainable development and the role of green productivity in achieving sustainable development. According
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Pollution of the aquatic environment and the depletion of the natural resource cause imbalance in the natural balance of the river environment and contributes to the deterioration of life and the killing of living organisms. Most of the old and modern cities and urban centers were set up close to the rivers because water enters the main lifeblood and all its facilities. The proximity of cities to rivers caused environmental problems resulting from the dumping of residues of these cities to a large and continuous, these wastes include all uses of the city (industrial, agricultural, residential and commercial) and others. The accumulation of these wastes inside the rivers water kills life and makes them unsuitable for various uses to bury
... Show MoreBackground: A core set of checks have been incorporated into World Health Organization (WHO) WHO surgical safety checklist. Lack of access to basic surgical care remains a major concern in low-income settings.
Objective: We use a WHO surgical safety checklist items to improve team communication and cooperation to help in reduction of morbidity and mortality of surgical procedures.
Methods: This is a prospective study involving 300 patients after applying the 19 items of the surgical safety checklist with different types of operations had been operated in the surgical theater at Al-Kindy Teaching Hospital during the period 1st of September 2016
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Food fortification has an important and necessary role in compensating for the shortage of nutritional micronutrients, especially in developing and least developed countries. So, 12 samples of flour available in the local market, whether imported or locally produced flour, were obtained during 2019. The amount of base metal of the necessary iron element in the flour models studied which are available in local markets, measured by spot testing and was compared with the values that should be added according to the specification Iraqi standard. Results revealed the qualitative evaluation of iron in locally produced flour does not conform to the Iraqi standard and is almost free of any reinforcement. While the percentage of imp
... Show MoreIn high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri
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In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func
In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha
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