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

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%.

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

Plagiarism is becoming more of a problem in academics. It’s made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has ”taken” and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and

Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels wit