Abstract: Thorough assessment of the Maxillary,Sinus is very important. Recently 3-dimensional imagewith Cone Beam Computed Tomography (CBCT) is very dependable in Maxillary Sinus diagnosis. The aim of this study is to: shade light on the role of (CBCT) diagnosis of the maxillary sinus anatomical variation and pathological finding among smokers and nonsmokers prior to maxillary sinus lift techniques. Materials and Method: In this study 60 males with age ranged between 20-50 years old, referred for (CBCT) assessment of maxillary sinus in the Specialist Health Center of AL-Sadder city. The scanning were performed using Kodak 9500 (CBCT), the KV was 90, mA10 and scanning time 10 s. Voxel size 0.3mm with( DICOM ) software on a multiplaner re

To perform a secure evaluation of Indoor Design data, the research introduces a Cyber-Neutrosophic Model, which utilizes AES-256 encryption, Role-Based Access Control, and real-time anomaly detection. It measures the percentage of unpredictability, insecurity, and variance present within model features. Also, it provides reliable data security. Similar features have been identified between the final results of the study, corresponding to the Cyber-Neutrosophic Model analysis, and the cybersecurity layer helped mitigate attacks. It is worth noting that Anomaly Detection successfully achieved response times of less than 2.5 seconds, demonstrating that the model can maintain its integrity while providing privacy. Using neutrosophic sim

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

Groundwater can be assessed by studying water wells. This study was conducted in Al-Wafa District, Anbar Governorate, Iraq. The water samples were collected from 24 different wells in the study area, in January 2021. A laboratory examination of the samples was conducted. Geographical information systems technique was relied on to determine the values of polluting elements in the wells. The chemical elements that were measured were [cadmium, lead, cobalt and chromium]. The output of this research were planned to be spatial maps that show the distribution of the elements with respect to their concentrations. The results show a variation in the heavy elements concentrations at the studied area groundwater. The samples show different values

The ligand [Potassium (E)-(4-(((2-((1-(3-aminophenyl) ethylidene) amino)-4-oxo-1, 4-dihydropteridin-6-yl) methyl) amino) benzoyl)-L-glutamate] was prepared from the condensation reaction of folic acid with (3-aminoacetophenone) through Schiff reaction to give a new Schiff base ligand [H2L]. The ligand [H2L] was characterized by elemental analysis CHN, atomic absorption (AA),(FT-IR),(UV-Vis), TLC, ES mass (for spectroscopes), molar conductance, and melting point. The new Schiff base ligand [H2L], reacts with Mn (II), Co (II), Ni (II), Cu (II), Cr (III) and Cd (II) metal ions and (2-aminophenol),(metal: derivative ligand: 2-aminophenol) to give a series of new mixed complexes in the general formula:-K3 [M2 (HL)(HA) 2],(where M= Mn (II) and Cd

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Background: Titanium implant is widely used in dentistry because of its extraordinary biocompatibility and mechanical properties. To increase bone–implant connection and provide early loading after placement, implant is stored in different storage medium and treated with UV light. Both of them are applicable methods to increase the bioactivity of titanium and overcome the biological aging. This study was designed to assess the effect of vacuum storage method and air storage with and without UV light treated of Cp Ti implant mechanically and histologically. Materials and methods: Titanium screws were acid etched and prepared in four different modes using different storage methods (air or vacuum and, with or without UV treatment. The implan