The theory of probabilistic programming  may be conceived in several different ways. As a method of programming it analyses the implications of probabilistic variations in the parameter space of linear or nonlinear programming model. The generating mechanism of such probabilistic variations in the economic models may be due to incomplete information about changes in demand, pro­duction and technology, specification errors about the econometric relations presumed for different economic agents, uncertainty of various sorts and the consequences of imperfect aggregation or disaggregating of economic variables. In this Research we discuss the probabilistic programming problem when the coefficient b_i is random variable

Obliquely deposited (70o) Bi, Sb, and Bi-Sb alloy thin films have been prepared by thermal
resistive technique. Structural properties of these films were studied using XRD. Their resistance and
voltage responsivity for Nd:YAG and CO2 laser pulses have been recorded as function of operating
temperature between 10 oC and 120 oC. It was found that the maximum responsivity for these detectors
can be obtained at 75 oC. On the other hand, the dependence of responsivity on the width of detectors was
investigated.

   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

Cadmium Oxide and Bi doped Cadmium Oxide thin films are prepared by using the chemical spray pyrolysis technique a glass substrate at a temperature of (400?C) with volumetric concentration (2,4)%. The thickness of all prepared films is about (400±20) nm. Transmittance and Absorbance spectra are recorded in the wave length ranged (400-800) nm. The nature of electronic transitions is determined, it is found out that these films have directly allowed transition with an optical energy gap of (2.37( eV for CdO and ) 2.59, 2.62) eV for (2% ,4%) Bi doped CdO respectively. The optical constants have been evaluated before and after doping.

Nano crystalline copper sulphide (Cu2S) thin films pure and 3% Bi doped were deposited on glass substrate by thermal evaporation technique of thickness 400±20 nm under a vacuum of ~ 2 × 10− 5 mbar to study the influence of annealing temperatures ( as-deposited, and 573) K on structural, surface morphology and optical properties of (Cu2S and Cu2S:3%Bi). (XRD) X-ray diffraction analysis showed (Cu2S and Cu2S:3%Bi) films before and after annealing are polycrystalline and hexagonal structure. AFM measurement approves that (Cu2S and Cu2S:3%Bi) films were Nano crystalline with grain size of (105.05-158.12) nm. The optical properties exhibits good optical absorption for Cu2S:3%Bi films. Decreased of optical band gap from 2.25 to 2 eV after dop

In this work the effect of annealing temperature on the structure and the electrical properties of Bi thin films was studied, the Bi films were deposited on glass substrates at room temperature by thermal evaporation technique with thickness (0.4 µm) and rate of deposition equal to 6.66Å/sec, all samples are annealed in a vacuum for one hour. The X-ray diffraction analysis shows that the prepared samples are polycrystalline and it exhibits hexagonal structure. The electrical properties of these films were studied with different annealing temperatures, the d.c conductivity for films decreases from 16.42 ? 10-2 at 343K to 10.11?10-2 (?.cm)-1 at 363K. The electrical activation energies Ea1 and Ea2 increase from 0.031 to 0.049eV and

   Let M is a Г-ring. In this paper the concept of orthogonal symmetric higher bi-derivations on semiprime Г-ring is presented and studied and the relations of two symmetric higher bi-derivations on Г-ring are introduced.

The fiscal position of governments in rentier economies depends heavily on oil revenues. The relationship between oil prices and the budget surplus or deficit is often nonlinear and characterized by complex temporal dependencies, which may limit the predictive capability of conventional econometric models. Accordingly, this study aims to forecast the Iraqi budget surplus and deficit and compare the predictive performance of the ARDL, NARDL, LSTM, 1D-CNN, and hybrid 1D-CNN-LSTM models using oil prices as the primary predictive variable. The hybrid model integrates the feature-extraction capability of One-Dimensional Convolutional Neural Networks (1D-CNN) with the ability of Long Short-Term Memory (LSTM) networks to capture long-term

Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on rec