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

The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz

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

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

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

This study was conducted on a sample of commercial banks in Iraq, chosen according number of considerations for twenty banks, contained two public banks and eighteen private banks.                                                                                             &

Present study was carried out to find prevalence of MRSA in healthy individual of second stage students, college of pharmacy/Baghdad University. A total of 74 student selected between age 18-23 years old were included in this study, nasal swabs collected and subjected to many diagnostic standard bacteriological identification methods. Culture, colonial morphology, Gram stain,  mannitol fermentation, coagulase ,gelatinasetest, DNAase, MR/VP and antimicrobial susceptibility test was performed on tryptic soy agar by modified Kirby-Bauer muller hinton disc diffusion method and the result show that out of 74 nasal swabs,67(90.5%) were MRSA positive isolates, 21(31.4%) of them were mannitol ferment and 46(68.6%) non mannitol fermenter, am

The m-consecutive-k-out-of-n: F linear and circular system consists of n sequentially connected components; the components are ordered on a line or a circle; it fails if there are at least m non-overlapping runs of consecutive-k failed components. This paper proposes the reliability and failure probability functions for both linearly and circularly m-consecutive-k-out-of-n: F systems. More precisely, the failure states of the system components are separated into two collections (the working and the failure collections); where each one is defined as a collection of finite mutual disjoint classes of the system states. Illustrative example is provided.

Chronic renal failure (CRF) is progressive irreversible destruction of kidney tissue by disease which, if not treated by dialysis or transplant, will result in patient's death. This study was carried out on 30 patients (17 male and 13 female) with chronic renal failure. The aim of this research was studied the changes in the level of total protein ,albumin, calcium ,ionized calcium, phosphorous , iron ,ALP, LDH ,CK and FFA in patients with CRF before and after hemodialysis .The obtained results have been compared with 30 healthy subjects as control group (18male and 12 female). The results showed that there was significant increase in the level of calcium ,ionized calcium, phosphorous ,iron ,ALP,LDH,CK and FFA ,while there was a signifi

Background: Patients with chronic kidney
disease have different grades of sensorineural
deafness .
Objective: To study the incidence of
sensorineural hearing loss and possible contributing
factors in patients with chronic kidney disease.
Methods: A total of 100 patients with chronic
kidney disease were studied. All of them were
males. 92 of them were on regular haemodialysis
programme. Only 8 patients were on conservative
management the age range of the study patients was
18-40 year patients were divided into three groups
according to age. All patients were assessed
clinically and were evaluated by audiometry , and
analysis was made on bone conduction threshold
.The mean follow up period was 2