Background: It may be an important prospective clinical use of manufacturing of porous implant for clinical situations, such as cases of limitation in bone height, low bone density .The small segment of porous implant an effective osseointegration allows increasing in contact area provided for small segmented porous provided by its surface configuration. This study was done to Fabricate porous titanium implants by powder technology, as well as the observation of removal torque values of porous titanium implants compared to smooth titanium implants. Materials and methods: Twenty porous titanium implants (3.2mm in diameter and 8mm in length) were manufactured by powder technology using commercially pure titanium powder of ≤75um part

ABSTRACT Background: Piezosurgery device is a system developed recently to overcome the limitation of the traditional surgical technique in implant site preparation, which use the principle of ultrasonic microvibrations to create precise & selective cut in bone in harmony with the surrounding tissues. The aim of this study was to evaluate the outcomes of implants inserted by ultrasonic implant site preparation protocol (UISP) using piezosurgery device, regarding the survival rate, stability and other related factors, at 16 weeks postoperative follow up period. Materials and Methods: A total of (24) patients, (6) males and (18) females, aged between (19-51) years old, contributed in this study receiving a total of (42) implants, all of these

Background: The liver is one of the most common organs
injured after blunt abdominal trauma. The control of severe
hemorrhage remains a problem.
Methods: One-hundred thirty-eight patients diagnosed as
liver injury between 09/2003 and 08/2006 had been evaluated
prospectively in Al- Kindy Teaching Hospital.
A distinction was made between hemodynamically stable and
unstable patients. Different modalities of surgical procedures
were done concentrating on perihepatic gauze packing.
Results: (60 out of 138) patients included in the study were
clinically evaluated as hemodynamically stable. The average
abbreviated injury severity score (ISS) was 25. Twenty
patients underwent abdominal surgery. In 12 of them

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Estimating an individual's age from a photograph of their face is critical in many applications, including intelligence and defense, border security and human-machine interaction, as well as soft biometric recognition. There has been recent progress in this discipline that focuses on the idea of deep learning. These solutions need the creation and training of deep neural networks for the sole purpose of resolving this issue. In addition, pre-trained deep neural networks are utilized in the research process for the purpose of facial recognition and fine-tuning for accurate outcomes. The purpose of this study was to offer a method for estimating human ages from the frontal view of the face in a manner that is as accurate as possible and takes

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi