Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

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

Background: Patients with decompensated cirrhosis are often given therapeutic and prophylactic drugs. Polypharmacy raises both the likelihood of prescription errors and the complications associated with drugs. Clinical pharmacists are excellent at recognizing, addressing, and preventing clinically significant drug-related problems.

Objectives: Identification types of pharmacist interventions to address drug-related problems in patients with decompensated cirrhosis and assess the acceptance/implementation of these recommendations. And identify patient factors associated with accepting pharmacist recommend

Objectives: The purpose of the study is to ascertain the relationship between the training program and the socio-demographic features of patients with peptic ulcers in order to assess the efficiency of the program on patients' nutritional habits.

Methodology: Between January 17 and October 30 of 2022, The Center of Gastrointestinal Medicine and Surgery at Al-Diwanyiah Teaching Hospital conducted "a quasi-experimental study". A non-probability sample of 30 patients for the case group and 30 patients for the control group was selected based on the study's criteria. The study instrument was divided into 4 sections: the first portion contained 7 questions about demographic information, the second sect

Background. Alopecia areata (AA) is a common form of noncicatricial hair loss of unknown cause, affecting 0.1-0.2% of the general population. Most evidence supports the hypothesis that it is disease of the hair follicle of autoimmune nature mediated by T-cells, with important cytokine role. Objective of the Study. The objective of this study is to study the association and changes in serum levels of interleukin-15 (IL-15) and tumor necrosis factor-α (TNF-α) in patients with AA in relation to the type, activity, and disease duration. Patients and Methods. Thirty-eight patients with AA and 22 individuals without the disease as controls were enrolled in this case-controlled study conducted in the Department of Dermatology in the Al-K

OBJECTIVE: To evaluate the patient satisfaction to hospital services and identify factors that influences this satisfaction.

In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method

Background :Atherosclerosis is the most
frequent underlying cause of ischemic heart
disease and a major cause of death all over the
world. This study was carried out to analyze and
compare the angiographic findings in patients
with diabetes mellitus versus non diabetics with
coronary heart disease , and to correlate these
findings with some risk factors for coronary
heart disease.
Methods: A total of 100 patients were studied,
50 with diabetes mellitus, and 50 non diabetics.
This study was carried out at Al-Sadr teaching
hospital in Basrah, Southern Iraq during the
period April 2009- September 2009. All patients
were known to have coronary heart disease. Risk
factors for coronary heart disease

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m