Wireless Multimedia Sensor Networks (WMSNs) are a type of sensor network that contains sensor nodes equipped with cameras, microphones; therefore the WMSNS are able to   produce multimedia data such as video and audio streams, still images, and scalar data from the surrounding environment. Most multimedia applications typically produce huge volumes of data, this leads to congestion. To address this challenge, This paper proposes Modify Spike Neural Network control for Traffic Load Parameter with Exponential Weight of Priority Based Rate Control algorithm (MSNTLP with EWBPRC). The Modify Spike Neural Network controller (MSNC) can calculate the appropriate traffi

Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt

     Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments.

The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, o

Antiviral medications may be the best choices for COVID-19 treatment until particular therapeutic treatments become available. Tamiflu (oseltamivir) is a neuraminidase inhibitor licensed for the management and defense against influenza types A and B. Oseltamivir-based medication combinations are currently being used to treat COVID-19 patients who also have the new coronavirus 1 SARS-CoV-2. 1 Oseltamivir administration was related with a less time spent in the hospital, quicker recovery 1 and discharge, and a decreased mortality rate. Docking is a modern computational method for identifying a hit molecule by assessing the binding ability of molecular medicines within the binding target pocket. In this work, we chose 21 ligand compounds that

The coronavirus disease 2019 (COVID-19) pandemic and the infection escalation around the globe encourage the implementation of the global protocol for standard care patients aiming to cease the infection spread. Evaluating the potency of these therapy courses has drawn particular attention in health practice. This observational study aimed to assess the efficacy of Remdesivir and Favipiravir drugs compared to the standard care patients in COVID-19 confirmed patients. One hundred twenty-seven patients showed the disease at different stages, and one hundred and fifty patients received only standard care as a control group were included in this study. Patients under the Remdesivir therapy protocol were (62.20%); meanwhile, there (30.71

Background: Although underdeveloped in Iraq, telehealth was one tool used to continue health service provision during the COVID-19 pandemic. Aim: To assess women’s experiences and satisfaction with gynaecological and obstetric telehealth services in Iraq during the COVID-19 pandemic. Methods: Free telehealth services were provided by 4 obstetrician-gynaecologists associated with private clinics in 2020–2021. All patients who accessed the services between June 2020 and February 2021 were invited to complete a postconsultation survey on their experience and satisfaction with services. Results were analysed using descriptive statistics and logistic regression conducted using SPSS version 25. Results: A total of 151 (30.2%) women re

The coronavirus-pandemic has a major impact on women's-mental and physical-health. Polycystic-ovary-syndrome (PCOS) has a high-predisposition to many cardiometabolic-risk factors that increase susceptibility to severe complications of COVID-19 and also exhibit an increased likelihood of subfertility. The study includes the extent of the effect of COVID-19-virus on renin-levels, glutathione-s-transferase-activity and other biochemical parameters in PCOS-women. The study included 120 samples of ladies that involved: 80 PCOS-patients, and 40 healthy-ladies. Both main groups were divided into subgroups based on COVID-19 infected or not. Blood-samples were collected from PCOS-patients in Kamal-Al-Samara Hospital, at the period between Decembe

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha