Ankylosing spondylitis is a complex debilitating disease because its pathogenesis is not clear. This study aims at detecting some pathogenesis factors that lead to induce the disease. Chlamydia pneumoniae is one of these pathogenesis factors which acts as a triggering factor for the disease. The study groups included forty Iraqi Ankylosing spondylitis patients and forty healthy persons as a control group. Immunological and molecular examinations were done to detect Chlamydia. pneumoniae in AS group. The immunological results were performed by Enzyme-Linked Immunosorbent Assay (ELISA) to detect anti-IgG and anti-IgM antibodies of C. pneumoniae revealed that five of forty AS patients' samples (12.5%) were positive for anti-IgG and IgM C. pneu

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

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.

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

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit

The present study was performed to evaluate the level of some risk factors (biochemical and immunological) in hypothyroid Iraqi patients considering the different thyroid functional states (hypothyroidism and subclinical hypothyroidism).The study includes 82 patients clinically diagnosed with hypothyroidism. Three study groups have been investigated: (47 clinical hypothyroid patients, 12 subclinical hypothyroid patients 23 healthy individuals) of different ages. This study, show that the proportion of females (83.3 %), (87.2%) in subclinical and clinical hypothyroidisim respectively higher than the proportion of males (16.7%),(12.8%) in subclinical and clinical hypothyrodism respectively of the total patients.The majority of subclinical hyp

Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,

Triticale is a hybrid of wheat and rye grown for use as animal feed. In Florida, due to its soft coat, triticale is highly vulnerable to Sitophilus oryzae L. (rice weevil) and there is interest in development of methods to detect early-instar larvae so that infestations can be targeted before they become economically damaging. The objective of this study was to develop prediction models of the infestation degree for triticale seed infested with rice weevils of different growth stages. Spectral signatures were tested as a method to detect rice weevils in triticale seed. Groups of seeds at 11 different levels (degrees) of infestation, 0–62%, were obtained by combining different ratios of infested and uninfested seeds. A spectrophotometer wa