The present work introduces, external morphological study of the leafhopper Neoalitarus
fenestratus Herrich-Schäeffer (Deltocephalinae:Oposiini), particularly the male genitalia
which were dissected and illustrated.

Diabetes is a disease caused by high sugar levels. Currently, diabetes is one of the most common diseases in the number of people with diabetes worldwide. The increase in diabetes is caused by the delay in establishing the diagnosis of the disease. Therefore, an initial action is needed as a solution that requires the most appropriate and accurate data mining to manage diabetes mellitus. The algorithms used are artificial neural network algorithms, namely Restricted Boltzmann Machine and Backpropagation. This research aims to compare the two algorithms to find which algorithm can produce high accuracy, and determine which algorithm is more accurate in detecting diabetes mellitus. Several stages were involved in this research, including d

A computational investigation is carried out in the field of charged particle optics with the aid of the numerical analysis methods. The work is concerned with the design of symmetrical double pole piece magnetic lens.  The axial magnetic flux density distribution is determined by using exponential model, from which the paraxial-ray equation is solved to obtain the trajectory of particles that satisfy the suggested exponential model.  From the knowledge of the first and second derivatives of axial potential distribution, the optical properties such as the focal length and aberration coefficients (radial distortion coefficient and spiral distortion coefficient) are determined.  Finally, the pole piece profiles capable of pr

In this paper, a mathematical model was built for the supply chain to reduce production, inventory, and transportation in Baghdad Company for Soft Drink. The linear programming method was used to solve this mathematical model. We reduced the cost of production by reduced the daily work hours, the company do not need the overtime hours to work at the same levels of production, and the costs of storage in the company's warehouses and agents' stores have been reduced by making use of the stock correctly, which guarantees reducing costs and preserving products from damage. The units transferred from the company were equal to the units demanded by the agents. The company's mathematical model also achieved profits by (84,663,769) by re

      The method of coordinate conversion is still considered important and laborious due to the shift from the spatial ellipsoidal (geographic) to the flat planned system. The most common method uses a contiguous UTM system as one of the most reliable systems in the conversion process; however, this system faces a problem in large areas that contain more than one zone. The goal of this research is to create a simple and low computational cost model to represent a non-contiguous semi-UTM geographic coordinates for confined regions of the globe. The considered region taken in this study is the northern parts of Arabian Gulf (including parts of Iraq, Kuwait, Iran, and Saudi Arabia). The determined mathematical mode

In this paper we used frequentist and Bayesian approaches for the linear regression model to predict future observations for unemployment rates in Iraq. Parameters are estimated using the ordinary least squares method and for the Bayesian approach using the Markov Chain Monte Carlo (MCMC) method. Calculations are done using the R program. The analysis showed that the linear regression model using the Bayesian approach is better and can be used as an alternative to the frequentist approach. Two criteria, the root mean square error (RMSE) and the median absolute deviation (MAD) were used to compare the performance of the estimates. The results obtained showed that the unemployment rates will continue to increase in the next two decade

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog