Neural cryptography deals with the problem of “key exchange” between two neural networks by using the mutual learning concept. The two networks exchange their outputs (in bits) and the key between two communicating parties ar eventually represented in the final learned weights, when the two networks are said to be synchronized. Security of neural synchronization is put at risk if an attacker is capable of synchronizing with any of the two parties during the training process.

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

     Hyperglycemia is a complication of diabetes (high blood sugar). This condition causes biochemical alterations in the cells of the body, which may lead to structural and functional problems throughout the body, including the eye. Diabetes retinopathy (DR) is a type of retinal degeneration induced by long-term diabetes that may lead to blindness. propose our deep learning method for the early detection of retinopathy using an efficient net B1 model and using the APTOS 2019 dataset. we used the Gaussian filter as one of the most significant image-processing algorithms. It recognizes edges in the dataset and reduces superfluous noise. We will enlarge the retina picture to 224×224 (the Efficient Net B1 standard) and utilize data aug

As we live in the era of the fourth technological revolution, it has become necessary to use artificial intelligence to generate electric power through sustainable solar energy, especially in Iraq and what it has gone through in terms of crises and what it suffers from a severe shortage of electric power because of the wars and calamities it went through. During that period of time, its impact is still evident in all aspects of daily life experienced by Iraqis because of the remnants of wars, siege, terrorism, wrong policies ruling before and later, regional interventions and their consequences, such as the destruction of electric power stations and the population increase, which must be followed by an increase in electric power stations,

The penalized least square method is a popular method to deal with high dimensional data ,where  the number of explanatory variables is large than the sample size . The properties of  penalized least square method are given high prediction accuracy and making estimation and variables selection

 At once. The penalized least square method gives a sparse model ,that meaning a model with small variables so that can be interpreted easily .The penalized least square is not robust ,that means very sensitive to the presence of outlying observation , to deal with this problem, we can used a robust loss function to get the robust penalized least square method ,and get robust penalized estimator and

This experiment was carried out in the field of botanical garden which belongs to Biologi
Department , College of Education (Ibn Al-Haitham), University of Baghdad during the
growing season 2009-2010.The experiment was designed to study the effect 5 concentrations
of Gibberellic acid(GA3)( 25,50,75,100and125mg.L
-1
) and 2 levels of NPK (17:17:17)
fertilizer (200 and 400 Kg.ha
-1
) and their interaction on the rates of absorption and transport
of some macronutrient elements in two varieties of chamomile plant ( Local variety ,
Matricaria chamomilla L. and German variety , Matricaria recutitia L.) . Randomized
Complete Block Design (RCBD) was used with 3 replicates for each treatment .Control plants

Oxidative stress and inflammation are connected to the development of metabolic disorders, such as diabetes. Diabetic-related oxidative stress is caused by the overproduction of oxidative-free radicals, which have been implicated in the mechanism of inflammation and damage to tissues. Our study aimed to investigate the effects of ubiquinone treatment on serum indicators of oxidative stress (malondialdehyde (MDA)), inflammation (interleukin 6 (IL-6)), vascular homeostasis (nitric oxide (NO)), and myopathy (myoglobin (MB))  in addition to measuring blood components parameters in streptozotocin-induced diabetic rats. Rats were separated into three groups; negative control group (N), diabetic control group (D), and ubiquinone-treated diabet

     An interpretative study of the two-dimensional seismic data of the Afaq area was conducted using the Petrel 2017 software. 2D seismic reflection sections are used to give a structural interpretation of Afaq structure based on synthetic seismogram and well log data. Three reflectors, Zubair, Yamama, and Gotina Formations, were selected. These reflectors are defined from well west kifl (wk-1), Where located adjacent to the study area. Structural maps of the Zubair, Yamama, and Gotnia formations are prepared and interpreted, including TWT maps, Average velocity maps, and depth maps. The studies concluded that the Afaq structure area does not contain main faults, but secondary faults with short and limited extensions