Aws A. Magableh

The widespread deployment of Internet of Things (IoT) environments has led to an increasing number of cyberattacks, highlighting the need for efficient and accurate intrusion detection systems. Over the last few decades, Intrusion Detection Systems (IDSs) have been proposed to tackle this challenge. However, IDSs face challenges when dealing with high-dimensional IoT data that include redundant or irrelevant features, which can lead to increased false positives and decreased detection performance. An optimization-based IDS framework is one of the solutions for reducing data dimensionality and improving detection accuracy. However, the serial implementation of this type of IDS suffers from high computational time as the volume of data and its dimensionality increase. In this paper, we propose a scalable distributed island-based feature selection IDS using Apache Spark (version 3.5.6), called DISFS-IDS. DISFS-IDS follows a two-level partitioning strategy—data and population—to distribute the workload across worker nodes in order to identify the most informative features while achieving high detection accuracy. Using binary and multiclass IoT datasets, the experimental results demonstrate that DISFS-IDS achieves statistically comparable detection performance to the serial SFOA-based IDS while selecting a smaller subset of features. Moreover, DISFS-IDS provides effective feature reduction and competitive or superior performance compared with Spark-based filter feature selection methods. In the scalability analysis, DISFS-IDS achieves significant speedup as the number of islands increases while maintaining high parallel efficiency.

Research Background: Evaluating user satisfaction is essential for understanding interaction with wearable technology in healthcare, sports science, and research settings. Aims and Objective: This study aimed to adapt and evaluate the psychometric properties of an Arabic version of the User Satisfaction Evaluation Questionnaire (USEQ) for use with wearables providing a reliable instrument for assessing user satisfaction with wearable technologies. Research Methods: We employed a cross‐cultural adaptation approach that included two phases: translation and validation. The translation phase involved forward and backward translation, expert committee reconciliation, and user comprehension testing. Psychometric evaluation was conducted with 127 participants to examine content and face validity, internal consistency (Cronbach’s α and McDonald’s ω ), and factorial validity via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results: The translated USEQ demonstrated acceptable validity and reliability. The content validity indices ranged 0.8–1.0 with (S‐CVI = 0.88), and face validity indices ranged 0.93–1.0 with (S‐FVI = 0.96). The reliability was acceptable with Cronbach’s α = 0.79 and McDonald’s ω = 0.855. EFA (KMO = 0.834, significant Bartlett’s test) revealed high factor loadings (0.771–0.891) explaining 63.18% variance. CFA further supported the model fit, with CFI (0.986) and TLI (0.977) exceeding conventional benchmarks for good fit, and RMSEA (0.063) and SRMR (0.058) within acceptable ranges. Conclusion: The Arabic USEQ demonstrates preliminary validity and reliability for measuring user satisfaction in this context. The instrument provides a usable tool for researchers evaluating wearable user experiences in Arabic‐speaking populations.

Federated learning (FL) has emerged as a paradigm-shifting approach for collaborative machine learning (ML) while preserving data privacy. However, existing FL frameworks face significant challenges in ensuring trustworthiness, regulatory compliance, and security across heterogeneous institutional environments. We introduce AspectFL, a novel aspect-oriented programming (AOP) framework that seamlessly integrates trust, compliance, and security concerns into FL systems through cross-cutting aspect weaving. Our framework implements four core aspects: FAIR (Findability, Accessibility, Interoperability, Reusability) compliance, security threat detection and mitigation, provenance tracking, and institutional policy enforcement. AspectFL employs a sophisticated aspect weaver that intercepts FL execution at critical joinpoints, enabling dynamic policy enforcement and real-time compliance monitoring without modifying core learning algorithms. We demonstrate AspectFL’s effectiveness through experiments on healthcare and financial datasets, including a detailed and reproducible evaluation on the real-world MIMIC-III dataset. Our results, reported with 95% confidence intervals and validated with appropriate statistical tests, show significant improvements in model performance, with a 4.52% and 0.90% increase in Area Under the Curve (AUC) for the healthcare and financial scenarios, respectively. Furthermore, we present a detailed ablation study, a comparative benchmark against existing FL frameworks, and an empirical scalability analysis, demonstrating the practical viability of our approach. AspectFL achieves high FAIR compliance scores (0.762), robust security (0.798 security score), and consistent policy adherence (over 84%), establishing a new standard for trustworthy FL.

In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have sought to integrate technology, especially business intelligence, into their operations to ensure their achievement. This study aims to provide a systematic literature review of the intersection of information systems and sustainability in business intelligence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilized to select high-quality studies from various databases, including ScienceDirect, IEEE Xplore, and Scopus, to be included in this review. The methodology resulted in 32 studies taxonomized into four main categories covering different aspects of the intersection of information systems and sustainability. This study discusses integrating information systems and sustainability in various sectors, such as tourism, health, urban, and other sectors, with different technologies, such as Blockchain, IoT, Industry 4.0, and other innovations. Moreover, the information system types implemented to support sustainability practices in different domains are highlighted.

Nowadays, large numbers of organizations may opt for Aspect-Oriented Programming (AOP), which is an enhancement to Object-Oriented Programming (OOP). This is due to the addition of a number of concepts that have assisted in the production of more flexible and reusable components. One of the most important elements added by AOP is software reuse, which is based on reusability attributes. These attributes indicate the possibility of reusing one or more components in the development of a new system. It is one of the most essential attributes to evaluate the quality of a system’s components. Thus far, little attention has been paid to the process of measuring AOP reusability, and it has not yet been standardized. The objective of the current study is to come up with a reasonable measurement for AOP software reuse, which is simultaneously a significant topic for researchers while offering several advantages for organizations. Although numerous models have been built to estimate the reusability of software, most of them are not dedicated to Aspect-Oriented Software (AOS). In this study, a model has been designed for AOS reusability estimation and measurement based on a new equation depending on five attributes that have a range of positive and negative impacts on AOS reusability. Three of those attributes, namely coupling, cohesion, and design size, have been included in previous studies. This study proposes complexity and generality as two new attributes to be considered. Each of these attributes was measured based on the metrics also proposed in this study. A new equation to calculate AOS reusability was constructed based on the most important reusability attributes and metrics. Seven aspect projects were employed as a case study to apply the proposed equation. After the proposed equation was applied to the selected projects, we obtained new values of reusability to compare with the values that resulted from applying the previous equation. The fact that new values emerged indicates that the proposed reusability metrics and attributes had a significant effect.

Modernizing legacy software has always been a core concern of businesses and industries that strive to improve their performance. However, decision makers fail to address each criterion’s concerns and provide precise solutions for how legacy software modernization can be implemented. This paper proposes a new Multi-Criteria Decision-Making (MCDM) method for legacy software modernization. The aim of the proposed method is to rank different software modernization solutions based on the predetermined evaluation criteria. The evaluation criteria considered in this work include Motivating Factors and Challenges criteria that possibly affect legacy software modernization. The method introduced is a combination of the Full Consistency Method (FUCOM) and Weighted Sum Method (WSM), in which FUCOM is used to maintain the reliability of the decision model and WSM to calculate overall importance or weight of each criterion in the decision-making process. This MCDM method is formulated to obtain the most optimal solution that satisfies the evaluation criteria defined by industrial stakeholders based on the survey conducted. A case study on modernizing legacy billing software for a telecommunication company is used to further illustrate the implementation of the proposed methodology in different situations.

As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced artificial intelligence techniques to significantly enhance multiplatform spam classification on Twitter and YouTube. The deep neural networks we use are state-of-the-art. They are recurrent neural network architectures with long- and short-term memory cells that are powered by both static and contextualized word embeddings. Extensive comparative experiments precede rigorous hyperparameter tuning on the datasets. Results reveal a profound impact of tailored, platform-specific AI techniques in combating sophisticated and perpetually evolving threats. The key innovation lies in tailoring deep learning (DL) architectures to leverage both intrinsic platform contexts and extrinsic contextual embeddings for strengthened generalization. The results include consistent accuracy improvements of more than 10–15% in multisource datasets, unlocking actionable guidelines on optimal components of neural models, and embedding strategies for cross-platform defense systems. Contextualized embeddings like BERT and ELMo consistently outperform their noncontextualized counterparts. The standalone ELMo model with logistic regression emerges as the top performer, attaining exceptional accuracy scores of 90% on Twitter and 94% on YouTube data. This signifies the immense potential of contextualized language representations in capturing subtle semantic signals vital for identifying disguised spam. As emerging adversarial attacks exploit human vulnerabilities, advancing defense strategies through enhanced neural language understanding is imperative. We recommend that social media companies and academic researchers build on contextualized language models to strengthen social media security. This research approach demonstrates the immense potential of personalized, platform-specific DL techniques to combat the continuously evolving threats that threaten social media security.

The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.

Blockchain systems are lauded for their security and reliability. Security is a cornerstone, as they employ cryptographic techniques to ensure the immutability of data, making it extremely resistant to tampering. With decentralized networks, they also reduce the risk of a single point of failure, enhancing reliability. Model checking plays a vital role in ensuring the security and reliability of blockchain systems. However, traditional model-checking approaches face challenges in handling the inherent dynamism exhibited in blockchain systems. To overcome this challenge, Aspect-Oriented programming (AOP) offers capabilities to enhance blockchain model checking through the modularization of cross-cutting concerns, enabling traceability and monitoring, facilitating dynamic instrumentation, and supporting fine-grained property specifications. The aim of this research is to enable more effective and efficient verification of dynamic behaviors in blockchain systems compared to conventional model-checking techniques using AOP. As a result, this research introduces BlockASP , a novel blockchain model verification method that leverages AOP to analyze and monitor dynamic behavior of the blockchain system. BlockASP integrates the benefits of aspect-orientation and model checking into the blockchain architecture to strengthen security and reliability. This research has examined prior arts that are related to blockchain modeling using Object-oriented (OO) and those that are using AOP. Our research has proposed and discussed the BlockASP technique, the research provided a case study to demonstrate the validity and superiority in facilitating the monitoring of dynamic blockchain behavior using AOP compared to traditional approaches such as Model-Driven Architecture (MDA).