In this paper, we investigate and study quantum theoretical of quark-gluon interaction modeling in QGP matter formatted. In theoretical modeling, we can use a flavor number, strength coupling, critical energy Tc = 190 MeV, system energy (400-650)MeV, fugacity of quark and gluon, and photon energy in range of 1-10 GeV parameter to calculation and investigation spectrum of photon rate. We calculation and study the photon rate produced through bremsstrahlung processes from the stable QGP matter. The photon rate production from cg → dgy systems at bremsstrahlung processes are found to be increased with increased fugacity, decreased strength coupling, decreased the photons energy and temperature of system. The photons rate in cg → dgy is inc

INTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems. OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound sy

Alterations of trace element concentrations adversely affect biological processes and could promote carcinogenesis. Trace element deficiency or excess is implicated in the development or progression of some cancers like colorectal cancer. The aim of the present study was to compare the serum copper (Cu) and zinc (Zn) concentrations in patients with colorectal cancer from Iraqi male patient with those of healthy subjects. During the period of March 2015 until august 2015, a total of 25 patients with metastatic colon cancer and 20 healthy volunteers were enrolled from the Al-Kadhimia Teaching Hospital after the diagnosis using a histopathological examination for the malignant tumor; their age was between (38-60) years. Higher levels o

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated