Wireless networks and communications have witnessed tremendous development and growth in recent periods and up until now, as there is a group of diverse networks such as the well-known wireless communication networks and others that are not linked to an infrastructure such as telephone networks, sensors and wireless networks, especially in important applications that work to send and receive important data and information in relatively unsafe environments, cybersecurity technologies pose an important challenge in protecting unsafe networks in terms of their impact on reducing crime. Detecting hacking in electronic networks and penetration testing. Therefore, these environments must be monitored and protected from hacking and malicio

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

With the development of communication technologies for mobile devices and electronic communications, and went to the world of e-government, e-commerce and e-banking. It became necessary to control these activities from exposure to intrusion or misuse and to provide protection to them, so it's important to design powerful and efficient systems-do-this-purpose. It this paper it has been used several varieties of algorithm selection passive immune algorithm selection passive with real values, algorithm selection with passive detectors with a radius fixed, algorithm selection with passive detectors, variable- sized intrusion detection network type misuse where the algorithm generates a set of detectors to distinguish the self-samples. Practica

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

This in vivo study was conducted to investigate the effect of different concentrations of ozonated water on experimentally skin infection with some of bacterial isolates (Pseudomonas aeruginosa and Staphylococcus aureus ) which Showed dermal infection in experimental animals after 48 hours of exposure to these Microorganisms. Results revealed that ozone has the power to accelerated the healing process depending on the perfect concentration of ozonated water used and the severity of infection & nature of causative agent , in which the recovering period was 7 days for the infection caused by P. aeruginosa and 5 day for S.aureus by using the concentration 60 µg/ml . Results also indicated in this study the stimulated effect of O

Background: Mobile phones are approximately widely used everywhere like in hospital wards, clinics and universities as well as biomedical laboratories. They have become very important tool in students’ life. In contrast, these tools carry many harmful bacteria which are responsible for infectious diseases in human because they serve as a reservoir for different pathogens. Current study was aimed to isolate bacteria from students’ mobile phones at the Institute of Medical Technology/Al-Mansour/The Middle Technical University, Baghdad, Iraq. Also, the study investigated microbial resistance to many antimicrobial agents as well as the appropriate remedial measures. Method: Four hundred and fifty swabs from mobile phones were collected from

A study was conducted to evaluate the antibacterial effect of Phyllanthus emblica extract (ethanol:methanol, 1:1) against Pseudomonas aeruginosa, Staphylococcus aureus and Escherichia coli at different concentrations, i.e. 0.625, 1.25, 2.50, 5.0, 10.0 and 20.0 mg/ml. The antibacterial activity was determined by the agar well diffusion method to investigate the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The alcoholic extract of Phyllanthus emblica had the highest antibacterial activity at 20 mg/ml and 5 mg/ml except for Pseudomonas aeruginosa where the value of inhibition was between 20 and 10 mg/ml. The MIC concentrations were mostly very high and ranged from 5 to 1.25 mg/ml, while the MBC range fro