In this paper, image compression technique is presented based on the Zonal transform method. The DCT, Walsh, and Hadamard transform techniques are also implements. These different transforms are applied on SAR images using Different block size. The effects of implementing these different transforms are investigated. The main shortcoming associated with this radar imagery system is the presence of the speckle noise, which affected the compression results.

This paper deals with defining Burr-XII, and how to obtain its p.d.f., and CDF, since this distribution is one of failure distribution which is compound distribution from two failure models which are Gamma model and weibull model. Some equipment may have many important parts and the probability distributions representing which may be of different types, so found that Burr by its different compound formulas is the best model to be studied, and estimated its parameter to compute the mean time to failure rate. Here Burr-XII rather than other models is consider  because it is used to model a wide variety of phenomena including crop prices, household income, option market price distributions, risk and travel time. It has two shape-parame

This work presents a comparison between the Convolutional Encoding CE, Parallel Turbo code and Low density Parity Check (LDPC) coding schemes with a MultiUser Single Output MUSO Multi-Carrier Code Division Multiple Access (MC-CDMA) system over multipath fading channels. The decoding technique used in the simulation was iterative decoding since it gives maximum efficiency at higher iterations. Modulation schemes used is Quadrature Amplitude Modulation QAM. An 8 pilot carrier were
used to compensate channel effect with Least Square Estimation method. The channel model used is Long Term Evolution (LTE) channel with Technical Specification TS 25.101v2.10 and 5 MHz bandwidth bandwidth including the channels of indoor to outdoor/ pedestrian

Regression models are one of the most important models used in modern studies, especially research and health studies because of the important results they achieve. Two regression models were used: Poisson Regression Model and Conway-Max Well-  Poisson), where this study aimed to make a comparison between the two models and choose the best one between them using the simulation method and at different sample sizes (n = 25,50,100) and with repetitions (r = 1000). The Matlab program was adopted.) to conduct a simulation experiment, where the results showed the superiority of the Poisson model through the mean square error criterion (MSE) and also through the Akaiki criterion (AIC) for the same distribution.

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Today, the science of artificial intelligence has become one of the most important sciences in creating intelligent computer programs that simulate the human mind. The goal of artificial intelligence in the medical field is to assist doctors and health care workers in diagnosing diseases and clinical treatment, reducing the rate of medical error, and saving lives of citizens. The main and widely used technologies are expert systems, machine learning and big data. In the article, a brief overview of the three mentioned techniques will be provided to make it easier for readers to understand these techniques and their importance.

Digital Elevation Model (DEM) is one of the developed techniques for relief representation.  The definition of a DEM construction is the modeling technique of earth surface from existing data. DEM plays a role as one of the fundamental information requirement that has been generally utilized in GIS data structures. The main aim of this research is to present a methodology for assessing DEMs generation methods. The DEMs data will be extracted from open source data e.g. Google Earth. The tested data will be compared with data produced from formal institutions such as General Directorate of Surveying. The study area has been chosen in south of Iraq (Al-Gharraf / Dhi Qar governorate. The methods of DEMs creation are kri

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

We need to know the basic facts concerning planning top and bottom limits including any critical levels or the threshold over which the cost would be much higher for land development. Therefore this paper concerned with Baghdad Municipality decision No.2/1004 dated 7/12/2004. The reason behind this decision is the hope to face up at least in the severe housing crisis in the city of Baghdad. This paper attempts to know the attitude of the local community in the general through a field study of people living near such dwelling where third floors are added of. This might indicate any positive or negative effects whether on short or long-term including its effect on the theoretical side including the population growth of Baghdad, the

     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written