By definition, the detection of protein complexes that form protein-protein interaction networks (PPINs) is an NP-hard problem. Evolutionary algorithms (EAs), as global search methods, are proven in the literature to be more successful than greedy methods in detecting protein complexes. However, the design of most of these EA-based approaches relies on the topological information of the proteins in the PPIN. Biological information, as a key resource for molecular profiles, on the other hand, acquired a little interest in the design of the components in these EA-based methods. The main aim of this paper is to redesign two operators in the EA based on the functional domain rather than the graph topological domain. The perturb

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynami

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

The ZnO nanoparticles were synthesized at various precursor concentrations i.e. 0.05, 0.1, and 0.5 M by biosynthesis method based on Pometia pinnata Leaf Extracts. Initial nanoparticle concentration influenced the optical bandgap, shape, and structure of nanoparticles. The photodegradation process was carried out under UV illumination. The efficiency of MB degradation was determined by measuring the decrease in MB concentration and by analyzing the optical absorption at 663 nm recorded by UV-Vis spectroscopy. Results showed that the biosynthesized ZnO nanoparticles exhibited efficient photodegradation of MB, with a maximum degradation rate of 80% after 90 minutes of exposure to UV-C light. The study highlights the potential of Pometia pi

In petroleum industry, there are two major operations that can potentially impact the environment: Drilling and production. Both activities generate a significant volume of wastes include drill cuttings contaminated with hydrocarbons, wide variety of chemical additives, produced water and air pollutants. The potential impact depends primarily on the material, its concentration after release, and the biotic community that is exposed.
In this study, many drilling locations and production facilities have been investigated and examined for their adverse effects on the environment. Contamination with hydrocarbons, heavy metals, salts, other associated wastes and air pollution were detected at many sits.
Understanding of drilling and pro

 The study was conducted to isolate and identify endophytic fungi from leaves and twigs of lebbeck (Albizia lebbeck) and study their antagonistic activity against some plant fungal pathogens. Results showed isolation of 75 isolate endophytic fungi from 240 segments of leaves and twigs representing colonization frequency of 47.43%. Isolated fungi included different species of Aspergillus which prevail over other species (6 species), and three different isolates of Penicillium, one more species of Paecilomyces, Drechslera, Fusarium, Curvularia, Nigrospora and hyaline sterile fungus. Results showed variation in colonization frequencies within all species isolated from the two plant parts.  Dual culture methods for

Antibiotic resistance is a problem of deep scientific concern both in hospital and community settings. Rapid detection in clinical laboratories is essential for the judicious recognition of antimicrobial resistant organisms. So, the growth of Uropathgenic Escherichia coli (UPEC) isolates with Multidrug-resistant (MDR) and Extensively Drug-resistant (XDR) profiles that thwart therapy for (UTIs) has been detected and has straight squeezed costs and extended hospital stays. This study aims to detect MDR- and XDR-UPEC isolates. Out of 42 UPEC clinical isolates were composed from UTI patients. The bacterial strains were recognized by standard laboratory protocols. Susceptibility to antibiotic was measured by the standard disk diffusi

The monitoring weld quality is increasingly important because great financial savings are possible because of it, and this especially happens in manufacturing where defective welds lead to losses in production and necessitate time consuming and expensive repair. This research deals with the monitoring and controllability of the fusion arc welding process using Artificial Neural Network (ANN) model. The effect of weld parameters on the weld quality was studied by implementing the experimental results obtained from welding a non-Galvanized steel plate ASTM BN 1323 of 6 mm thickness in different weld parameters (current, voltage, and travel speed) monitored by electronic systems that are followed by destructive (Tensile and Bending) and non