Background: This in vitro study was carried out to evaluate the effects of various veneering dentin ceramic thicknesses and repeated firings on the color of lithium disilicate glass-ceramic (IPS e.max Press) and zirconium-oxide (IPS ZirCAD) all-ceramic systems, measured by clinical spectrophotometers (Easyshade Advance 4.0) . Materials and methods: The 72specimens cube-shaped have the dimension of about 11 mm in width, 14 mm in length, 1mm in thickness, these cores divided into 3 groups according to the type of material each group have (24)core specimens. Each group had been divided into three sub-groups (each having 8 specimens) according to veneering with dentin ceramic thicknesses: as 0.5, 1, or 2 mm (n=8). IPS e.max press and ZirCAD c

With the increased development in digital media and communication, the need for methods to protection and security became very important factor, where the exchange and transmit date over communication channel led to make effort to protect these data from unauthentication access.

This paper present a new method to protect color image from unauthentication access using watermarking. The watermarking algorithm hide the encoded mark image in frequency domain using Discrete Cosine Transform. The main principle of the algorithm is encode frequent mark in cover color image. The watermark image bits are spread by repeat the mark and arrange in encoded method that provide algorithm more robustness and security. The propos

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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

BACKGROUND: Color Vision Deficiency (CVD) is mostly an inherited trait and is not an uncommon problem. Prevalence of CVD differs among different ethnic and geographic properties of the population that affect their genetic constitution. Ishihara plates remain an internationally accepted tool for screening red-green CVD. OBJECTIVE: To determine the prevalence of red-green CVD among adult males from Baghdad province. PATIENTS AND METHODS: One thousand and five (1005) adult males were enrolled in this study, using a systematic sampling technique, and were screened for CVD utilizing 24-plate Ishihara plates and re-tested by EnChroma 39-Color plates. All males were residing in Baghdad and the center of Iraq. RESULTS: Among all tested males, 948 r

Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You on

Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, co

Physicians are likely to expend significant labor and time while manually calculating blood smears. Automatic computer-based methods for classifying acute lymphoblastic leukemia have trouble correctly lighting stained white blood cell microscopy images and accurately separating cells that touch or overlap. Additionally, incorporating machine learning techniques into medical services is very hard because doctors can deal with rough guesses as long as the results aren't too bad, but they can't use these calculations for actual medical care. Enabling a A deep network having knowledge of the accuracy of its own predictions is a fascinating and crucial issue. Most instances segmentation frameworks weigh the mask quality during the instance