Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Thin films of GexS1-x were fabricated by thermal evaporating under vacuum of 10-5Toor on glass substrate. The effect of increasing of germanium content (x) in sulfide films on the electrical properties like d.c conductivity (σDC), concentration of charge carriers (nH) and the activation energy (Ea) and Hall effect were investigated. The measurements show that (Ea) increases with the increasing of germanium content from 0.1to0.2 while it get to reduces with further addition, while charge carrier density (nH) is found to decrease and increase respectively with germanium content. The results were explained in terms of creating and eliminating of states in the band gap

Thin films of ZnSxSe1-x with different sulfide content(x)
(0, 0.02, 0.04, 0.06, 0.8, and 0.1), thickness (t) (0.3, 0.5, and 0.7 μm) and annealing temperature (Ta) (R.T 373 and 423K) were fabricated by thermal evaporating under vacuum of 10-5 Toor on glass substrate. The results show that the increasing of sulfide content (x)and annealing temperature lead to decrease the d.c conductivity σDC of and concentration of charge carriers (nH) but increases the activation energy (Ea1,Ea2), while the increasing of t increases σDC and nH but decrease (Ea1,Ea2). The results were explained in different terms

The electrical properties of polycrystalline cadmium telluride thin films of different thickness (200,300,400)nm deposited by thermal evaporation onto glass substrates at room temperature and treated at different annealing temperature (373, 423, 473) K are reported. Conductivity measurements have been showed that the conductivity increases from 5.69X10-5 to 0.0011, 0.0001 (?.cm)-1 when the film thickness and annealing temperature increase respectively. This increasing in ?d.c due to increasing the carrier concentration which result from the excess free Te in these films.

The information required for construction quantities surveying is not only generated by various participants in different construction phases but also stored in different forms including graphics, text, tables, or various combinations of the three. To report a bill of quantities (BOQ), the project manager has to continuously excerpt information from various resources and record it on papers. Without adequate staff and time, this repetitive and tedious process is difficult for the project manager to handle properly and thus reduces the effectiveness and the accuracy of the quantities surveying process which creates problems during the design, tender, and construction supervision of construction projects for designers and contractors pract

Abstract. Full-waveform airborne laser scanning data has shown its potential to enhance available segmentation and classification approaches through the additional information it can provide. However, this additional information is unable to directly provide a valid physical representation of surface features due to many variables affecting the backscattered energy during travel between the sensor and the target. Effectively, this delivers a mis-match between signals from overlapping flightlines. Therefore direct use of this information is not recommended without the adoption of a comprehensive radiometric calibration strategy that accounts for all these effects. This paper presents a practical and reliable radiometric calibration r

Electrical properties were studied for Pectin/PVA graphene composites films and the effect of aqueous interaction on their properties. The conductivity and the dielectric constant of this composite are important because Polysaccharide like pectin is increasingly being used in biomedical applications and as nanoparticles coating materials. The Dielectric and conductivity of composite films were compared in dry and wet condition the differences in the results were attributed to the water molecules and the hydrogen bond which connect the three composite compounds (Pectin, PVA and Graphene) together. These connections were allowed the hydrogen and hydroxyl group’s migrations in the composite super molecules. On the other hand, graphene was pr

In this paper we prove the boundedness of the solutions and their derivatives of the second order ordinary differential equation x ?+f(x) x ?+g(x)=u(t), under certain conditions on f,g and u. Our results are generalization of those given in [1].

Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability