This research study Blur groups (Fuzzy Sets) which is the perception of the most modern in the application in various practical and theoretical areas and in various fields of life, was addressed to the fuzzy random variable whose value is not real, but the numbers Millbh because it expresses the mysterious phenomena or uncertain with measurements are not assertive. Fuzzy data were presented for binocular test and analysis of variance method of random Fuzzy variables , where this method depends on a number of assumptions, which is a problem that prevents the use of this method in the case of non-realized.

In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

In this article, we design an optimal neural network based on new LM training algorithm. The traditional algorithm of LM required high memory, storage and computational overhead because of it required the updated of Hessian approximations in each iteration. The suggested design implemented to converts the original problem into a minimization problem using feed forward type to solve non-linear 3D - PDEs. Also, optimal design is obtained by computing the parameters of learning with highly precise. Examples are provided to portray the efficiency and applicability of this technique. Comparisons with other designs are also conducted to demonstrate the accuracy of the proposed design.

Late Paleocene –Early Eocene algae and benthic foraminifera were identified
from the Sinjar Formation that crop out in three selected section from northern
Iraq. The selected sections: (Derbandikhan ,Qara dagh, Wara Mali has been
measured in the field and closely sampled to undertake details. A number of (131)
samples were collected illustrating all the lithological changes along the sections,
The limestone in the studied section is rich in algae and large benthic foraminifera,
The investigation of the thin sections allowed us to identify the (47 ) species of
foraminifera and (34) species of algae , Five biozones were distinguished for algae
which are :a- Amphiroa iraquensis range zone ,b- Trinocladus perplexus

In this research, the region in the south-west of Iraq is classified using a fuzzy inference system to estimate its desertification degree. Three land cover indices are used which are the Normalized Difference Vegetation Index, Normalized Multi-Band Drought Index and the top of atmosphere surface temperature to build a fuzzy decision about the desertification degree using eight decision roles. The study covers a temporal period of 38 years, where about every 10 years a sample is elected to verify the desertification status of the region, starting from 1990 to 2018. The results show that the desertification status varied every 10 years, wherein 2000 encountered the highest desertification in the south-west of Iraq.

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things. , We conclude that there is a significant association between levels of ferritin and the harshness of COVID-19. In this paper we introduce a semi- parametric method for prediction by making a combination between NN and regression models. So, two methodologies are adopted, Neural Network (NN) and regression model in design the model; the data were collected from مستشفى دار التمريض الخاص for period 11/7/2021- 23/7/2021, we have 100 person, With COVID 12 Female & 38 Male out of 50, while 26 Female & 24 Male non COVID out of 50. The input variables of the NN m

     Soil and plant contamination with heavy metals is one of the current problems in the world especially contamination with mercury. Heavy metals are very harmful because of their long biological half-lives, non-biodegradable nature and their possibility to accumulate at different body parts.  Soil, well water and leafy plant samples (Apium graveoleus, Allium ampeloprasum, Lepidium sativum, Eruca sativa,  Petroselinum hortense, Ocimum basilicum , Mentha pulegium) from three different agricultural fields (AL-Musafer village (site 1), AL-Autaifiyah (site 2) and AL-Huriyah (site 3)) in Baghdad government, Iraq were analyzed for mercury concentration. Hg level in so

The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

This paper presents a hierarchical model of localized company effective when is used in a university campus or site. To highlight the standard criteria for each layer of the model and to prove the positive aspects of this model is the best in use and make the Dell Network as case of study. Through the case of study it has been shown that the expansion of the on-site network does not affect services or bandwidth.