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This paper aims to address the detection of COVID-19 by developing an accurate and efficient diagnostic system using chest X-ray images. The research utilizes open-source Kaggle data comprising four categories: COVID-19, Lung-Opacity, Normal, and Viral Pneumonia. The proposed system employs convolutional neural networks (CNNs), including VGG19, RNN-LSTM, and inceptionv3. Results vary among the methodologies, with VGG19 achieving 26% accuracy, RNN-LSTM attaining 25% accuracy (28% with preprocessing), and inceptionv3 with histogram equalization achieving 83% accuracy. A CNN designed from scratch demonstrates the highest performance, with an accuracy of 93% (96% with histogram equalization). The findings emphasize the potential of AI techniques in enhancing disease diagnosis, particularly in distinguishing COVID-19 from other conditions, thereby facilitating timely and effective interventions. [ABSTRACT FROM AUTHOR]

In recent years, there have been many Security vulnerabilities that threaten user security, these threats have led to the finding of user files, so the use of the Internet has become unlimited, and the number of digital network devices has increased,Therefore, maintaining the confidentiality and integrity of information has become an urgent necessity to preserve user information, due to the increase in hackers and intruders, and the innovation of modern methods of penetration every day. Data cryptography has proven to be a secure way to protect a user's data. Many current cryptography algorithms are considered weak regarding data transmission over the Internet, so newly updated algorithms are in high demand. In this paper, we proposed to develop the ancient rotor machine depending on the base64 codding technique, in which we replaced the alphabets of the ancient rotor machine with the alphabets of base64 that contain 64 characters. Furthermore, we proposed a key exchange based on One-time password OTP code via SMS, OTP is mechanism for logging on to a network using unique password that can only be used once, to overcome the static password method that is least secure, and used it to generate the subkeys for rotor machines based on hash and random permutation techniques. MD5 algorithm function is used to authenticate the original message, Finally, we experimented with these techniques of secure sending e-mails by encrypting the contents of them with the proposed technique. However, the proposed security technique got promising results. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSN) such as Facebook, Instagram, Twitter, and others have seen rapid growth in recent years. Such applications provide attractive online social networks and communications with the opportunity to connect with relatives and acquaintances, meet new people, enter communities, talk, exchange photos, organize events, and network with others who are close to real-life; unfortunately, on the other hand, they also raise privacy and security issues. We identified OSN threats in this paper and recommended a digital face-processing authentication method as a double-factor authentication after entering the password using Matlab. After applying deep learning classification by attending to a real dataset from the live webcam to train the model, we achieved the best accuracy rate of 95%. However, such methods have yet to be deployed to all social networks, so we also mentioned the problem of fake accounts, which is one of the most significant problems in OSN. These are effective tools for executing spam campaigns and spreading malware and phishing attacks. Fake accounts could lead to the loss of money for businesses, loss of reputation, stealing information for malicious purposes, and much more. This study is related to detecting fake and legitimate profiles on OSN. For this purpose, we chose two datasets that contain fake and legitimate accounts on Facebook and Instagram. Each contains different features after applying machine learning using Naive Bayes, Logistic Regression, Support Vector Machines, K-Nearest Neighbour, Boosted Tree, Neural Networks, SVM Kernal, and Logistec Regression Kernal. SVM achieved the highest classification accuracy for the Fake Profiles detection datasets with 97.1%. [ABSTRACT FROM AUTHOR]

Non Orthogonal Multiple Access (NOMA) successfully drew attention to the deployment of 5th Generation (5G) wireless communication systems, and it is now considered a significant technology in 5G communications. The primary enhancement in 5G is the speed, which may be 100 times faster than 4G. Due to the rising number of internal or external attacks on the Network, wireless intrusion detection systems are a vital aspect of any system connected to the Internet, and 5G will demand considerable improvements in data rate and security. In this paper, we have built a simulator for NOMA and applied a dropping attack to extract a dataset from the simulation model. The accuracy for detecting dropping attacks using the extracted data after applying ML algorithms was 95.7% for LR. Furthermore, this work suggests a methodology for wireless cyberattack detection in 5G networks based on applying several ML and DL techniques such as Decision Trees, KNN, Multi-class Decision Jungle, Multi-class Decision Forest, and Multi-class Neural Network. The proposed work is implemented and tested using a comprehensive Wi-Fi network benchmark dataset. The conducted experiments resulted in an outstanding performance with an accuracy of 99% for the KNN algorithm and 93% for DF and Neural Network. [ABSTRACT FROM AUTHOR]

Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11042-022-13914-9 B Correction to: Multimedia Tools and Applications b https://doi.org/10.1007/s11042-022-13914-9 The original publication of this article contains incorrect affiliations of the author "Laith Abualigah". Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]