Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

Background: Periodontal diseases are inflammatory disorders caused by the accumulation of oral biofilm and the host response to this accumulation which characterized by exaggerated leukocytes and neutrophils attraction to the sites of inflammation by chemoattractants which are a very important part of the pathogenesis of periodontal diseases. This study aimed to determine and compare the clinical periodontal parameters and the leukocyte cell types in the peripheral blood between patients with gingivitis and periodontitis with different severities compared to healthy controls. Materials and methods: This study included 150 male subjects aged between 35-50 years. They were divided into three groups: gingivitis group (n=30), periodontitis p

The types of traditional houses vary from region to region according to physical and non-physical circumstances, and Sulaymaniyah city is characterized by a type of traditional houses that differ significantly from those in most cities and regions neighboring are always different from the general pattern that is prevalent in the region and the Muslim world.

The aim of this research is to study the cause of this difference or distinction in the traditional houses in Sulaimaniyah city, by comparing the most common models in these houses and comparing them with the general models of village houses that originally existed in the region to relaize the convergence and contrast between them. The research was based on a co

Background: Debonding orthodontic brackets and removal of residual bonding material from the enamel surface include critical steps that may cause enamel damage. The aim of the present study was to evaluate and compare the site of bond failure and enamel surface damage after debonding of three types of esthetic brackets (composite, ceramic, sapphire) bonded with light cure composite and resin-modified glass ionomer adhesive. Materials and methods: Seventy two maxillary premolars teeth were divided into three groups each group consisted of 24 teeth according to the type of brackets. Each group was subdivided into two subgroups (12 teeth for each) according to the bonding material that was used. After 7 days of bonding procedure, the brackets

One of the most important problems in the oil production process and when its continuous flow, is emulsified oil (w/o emulsion), which in turn causes many problems, from the production line to the extended pipelines that are then transported to the oil refining process. It was observed that the nanomaterial (SiO₂) supported the separation process by adding it to the emulsion sample and showed a high separation rate with the demulsifiers (RB6000) and (sebamax) where the percentage of separation was greater than (90 and 80 )%  respectively, and less than that when dealing with (Sodium dodecyl sulfate and Diethylene glycol), the percentage of separation was (60% and 50%) respectively.

   The high proportion

The parasite E.histolytica was first isolated from a stool sample, and then cultivated and maintained in vitro using Locke-egg medium (LEM) and Liver infusion agar medium (LIAM) . Then, the effect of some types of erythrocytes (human and sheep), on the growth and activity of the parasite in the two culture media was investigated. The parasite was able to ingest and lysis erythrocytes of human and sheep that were supplemented to the culture media and such manipulation was able to augment the reproduction rate of the cultivated E. histolytica, however, such consequence was media- and concentration-dependent. The reproduction rate was significantly increased (66.0, 57.5 and 58.6%, respectively) in LEM medium containing human erythrocytes ty

The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de