Princess Sumaya University for Technology

M. Lin defined a binary operation for two positive semi-definite matrices in studying certain determinantal inequalities that arise from diffusion tensor imaging. This operation enjoys some interesting properties similar to the operator geometric mean. We study this operation further and present numerous properties emphasizing the relationship with the operator geometric mean. In the end, we present an application toward Tsallis relative operator entropy.

The rate of car accidents has been increasing in recent years, resulting in losses in human lives, properties and other financial costs. To address this important issue, an embedded machine learning based system is developed. The system is capable of monitoring road conditions, detecting driving patterns, and identifying aggressive driving behaviors. The system is based on neural networks that are trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system can effectively detect potential risks and help mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of both drivers and vehicles. The process of collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions, sudden starting, sudden stop, and sudden entry. The gathered data is then processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in a 91.9% accuracy, 93.6% precision and 92% recall.

Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score.

Halal brands are gaining popularity among Muslim consumers across the world. This study aims to explore the effect of brand awareness, religious belief, and brand trust on young Muslim consumers’ willingness to purchase halal-branded products. The electronic questionnaires were distributed to participants through Google Forms via a popular social media platform. The survey sample consisted of university students from Bangladesh who identified as Muslims. The paper utilized a purposive sampling technique to select participants for the study, resulting in a total of 517 respondents being included in the sample. Finally, 479 responses were found usable for this study, with a response rate of 92.70%. Among 479, 300 participants were male, while 179 students were female university students. The latest edition of the software SPSS has been employed to examine hypotheses and correlations, with a confidence level of 95%. The results showed a significant impact of brand awareness, religious belief, and brand trust on purchase willingness. Moreover, it indicates that consumers’ brand awareness about halal products (β = 0.392) has a greater effect on their purchase willingness. These findings have the potential to provide valuable insights for halal brands, as they can have a substantial impact on the purchasing choices of young Muslim consumers, particularly regarding their religious and halal concerns.

Source code authorship attribution is the task of identifying who develops the code based on learning based on the programmer style. It is one of the critical activities which used extensively in different aspects such as computer security, computer law, and plagiarism. This paper attempts to investigate source code authorship attribution by capturing natural language aspects of the code rather than only using minimal set of syntactic and stylistic code features as explored in the previous literature. It proposes an evolutionary feature selection model to improve the accuracy of authorship attribution by implementing two language models (uni-gram and bi-gram). The proposed approach uses K-Nearest Neighbor as a classifier and Genetic Algorithm as a feature selection technique. Two experiments have been demonstrated on a public Authorship Attribution dataset on GitHub, the experiments include various evolutionary feature selection models. Notably, the obtained results in both experiments were compared with the related studies, and show a significant improvement in terms of accuracy.

The rapid growth of Artificial Intelligence (AI) algorithms has created the opportunity to solve complex problems such as Internet of Things (IoT) botnet attacks. The severity of IoT botnet attacks is a critical challenge for improving the smart IoT environment. Therefore, there is an urgent need to design and implement an efficient detection model to deal with various IoT bot attacks and simultaneously handle issues related to the massive feature space. This paper introduces a wrapper feature selection technique by adapting the Artificial Rabbit Optimization (ARO) algorithm and the Decision Tree (DT) algorithm to detect various types of IoT botnet attacks. During the design of the suggested DT-ARO model, the N-BaIoT datasets were used as a testbed environment. The feature space optimization step was carried out using the ARO algorithm to select only the high-priority features for detecting the IoT botnet attacks. The binary vector technique was used to distinguish the optimal features. The detection engine was performed using the DT algorithm. The conducted experiments have demonstrated the ability of the suggested DT-ARO model to detect various types of IoT botnet attacks, where the accuracy rate was 99.89%. Meanwhile, effectively reducing the feature’s space. In addition, the accomplished results were compared with the latest typical approaches. The DT-ARO model was found to be competitive with these methods and even outperformed them in reducing the feature space.

This paper introduces a new family of non-negative real-valued functions on the algebra of all bounded linear operators on a Hilbert space. These functions will be called semi-norms because they satisfy the norm axioms, except for the triangle inequality, which will be discussed for special cases. Many bounds and relations will be shown for this new family, with a connection to the existing literature.

p>Due to the involvement of human subjects or animals in this study, all ethical and experimental procedures and protocols were approved by the Institutional Review Board (IRB) at Princess Sumaya University for Technology under research protocol number (2023-0071)</p

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

Mach-Zehnder interferometers (MZIs) are vital in optical biosensor applications and high-density integrated photonic circuits. In this research work, we theoretically investigate the design steps of MZIs, consisting of 3-dB plasmonic splitters sandwiched between two dielectric waveguides with an air-gap coupler at the interface from each side. The proposed MZI designs feature a compact size, ensuring high transmission coupling efficiency without introducing additional radiation or reflection losses, as observed in tapered, T- and Y-shaped splitters. The analytical results and simulations prove that the proposed MZI designs offer a broad spectrum range with a high fabrication tolerance and the ability to use them in all-optical plasmonic circuits.

In this paper, we introduce the f -operator radius of Hilbert space operators as a generalization of the Euclidean operator radius and the q -operator radius. The properties of the newly defined radius are discussed, emphasizing how it extends some known results in the literature.

Nowadays, Internet use by all people has become a daily matter, whether to do business, buy online, education, entertainment, or social communication. Therefore, attackers exploit users of the Internet by tricking them into clicking on a specific link to carry out a phishing attack on them to collect sensitive information for users, whether credentials, usernames, email passwords banks, electronic payment information via a phishing attack that targets the end user, so many methods have appeared for protection from this type of attack, and among these methods using algorithms of machine learning (ML) to distinguish between the proper URL from the malicious phishing using several types from algorithm classification based on certain features found in any URL and using a ready-made data set, the best types of algorithm best performance are determined. It is recommended to use them based on the accuracy of the algorithm. We applied different machine learning algorithms on the same data set as Decision Tree, Logistic Regression, Linear Discriminant, Gradient, and Random Forest. The best accuracy was for the RF algorithm, with 97%.

Customers rely on online reviews to make purchasing decisions. However, the prevalence of fake reviews leads to a challenge in trusting the authenticity of online reviews. The manual detection of these fake reviews is hard and time-consuming. This paper presents an automated fake review detection method using text mining and supervised machine learning. In addition, a new fake reviews dataset called ChatGPT-Fake-It is collected from Google Play and chatGPT. Real reviews about the Facebook mobile application are collected from the Google Play store, while fake reviews are generated using chatGPT. A set of lexical and sentimental features are extracted from the textual reviews to build three classifiers. The experimental results reveal that the decision tree-based method achieved better accuracy, precision, recall, and F1-score than other classifiers.

The rapid spread of TOR (The Onion Router) for anonymized communication and internet browsing raises relevant security concerns due to its frequent use in illicit activities. This research is centered around a comprehensive analysis of TOR artifacts on Windows 11 and the creation of a relevant dataset to aid researchers. The study commences with a thorough review and comparative assessment of the existing literature on TOR artifacts and traffic examination. Windows 11 TOR browser artifacts are identified and placed together with those in Windows 10 and preceding versions. The paper's core methodology involves establishing a virtual lab environment, facilitating integration, simulating TOR network traffic, and gathering network forensics artifacts. A comprehensive dataset, assimilated from traffic logs procured from tools such as Wireshark, snort, Suricata, and pfsense firewall, is designed and used to construct machine learning models for TOR traffic detection. Combining these traffic logs into a particular dataset enhances the accuracy and efficiency of the machine-learning models. The findings of this study provide important insights into the evolving landscape of TOR browser artifacts and traffic patterns on different Windows versions. The proposed models showcase impressive accuracy in identifying TOR traffic. This research is crucial in enhancing our knowledge of TOR traffic analysis. It lays the groundwork for further investigations to enhance the detection and prevention of malicious activities utilizing the TOR network. Moreover, it contributes to developing a valuable dataset that can aid ML researchers in future endeavors.