A number of recent studies have shown that wastage and inefficiency are a significant problem in all global healthcare systems. One initiative that could radically improve the operational efficiency of health systems is to make a paradigm shift in data ownership—that is, to transition such systems to a patient-centric model of data management by deploying blockchain technology. Such a development would not only make an economic impact, by radically cutting wastage, but would deliver significant social benefits by improving patient outcomes and satisfaction. However, a blockchain-based solution presents considerable challenges. This research seeks to understand the principal factors, which act as barriers to the acceptance of a blockchain-based patient-centric data management infrastructure, in the healthcare systems of the GCC (Gulf Cooperation Council) countries. The study represents an addition to the current literature by examining the perspectives and views of healthcare professionals and users. This approach is rare within this subject area, and is identified in existing systematic reviews as a research gap: a qualitative investigation of motivations and attitudes among these groups is a critical need. The results of the study identified 12 key barriers to the acceptance of blockchain infrastructures, thereby adding to our understanding of the challenges that need to be overcome in order to benefit from this relatively recent technology. The research is expected to be of use to healthcare authorities in planning a way forward for system improvement, particularly in terms of successfully introducing patient-centric systems.

Since 2008, the company Airbnb has brought significant changes to the hospitality industry worldwide. Experiencing remarkable growth, it currently offers over six million listings in 191 countries across one hundred thousand cities. Airbnb has gained immense popularity among travellers seeking accommodations globally. Consequently, Airbnb generates extensive datasets from its listings that contain rich features that have captured the attention of researchers. These datasets offer potentially valuable information that can be extracted to greatly assist individuals and governments in making more informed decisions. Pricing rental properties on Airbnb still presents a challenge for owners, as it directly impacts customer demand. This research aimed to conquer the challenge by developing a sustainable price prediction model for Airbnb listings by incorporating property specifications, owner information and customer reviews. By utilising this model, owners can estimate the expected value of their Airbnb listings. We trained and fine-tuned several machine learning models using an Airbnb listing dataset from Barcelona. Performance evaluation metrics, such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE) and $R^2$ score were then used to compare the models. To enhance the performance of the predictive models, sentiment analysis was used to extract relevant features from customer reviews. Feature importance analysis was also conducted to determine which attributes were the most influential on listing price predictions. The results show that the Lasso and Ridge models outperformed the others considered in the study, with an average $R^2$ score of 99%. We found that amenities-related features had a negligible impact on all models’ performance. The most significant features found were polarity (positive/negative sentiment), the number of bedrooms, the accommodation’s maximum capacity, the number of beds and the quantity of reviews received by the listing in the past 12 months, respectively. We found that certain room types (categorized as entire home/apartment, private room or shared room) are associated with lower predicted prices.

The widespread adoption of Internet of Things (IoT) networks introduces critical security challenges, particularly due to poisoning attacks targeting federated learning (FL)-based intrusion detection systems (IDSs). Traditional FL methods, such as FedAvg, are vulnerable to adversarial updates, which compromise model integrity and reliability. To address these limitations, this article proposes an Adaptive Graph Attention-Based Federated Learning (AGAT-FL) framework designed to enhance the resilience of IoT-based IDSs. AGAT-FL combines dynamic trust-aware aggregation using graph attention networks (GAT), a hybrid convolutional neural network-gated recurrent unit (CNN-GRU) deep learning model for spatial-temporal anomaly detection, and Mahalanobis distance-based filtering to identify and suppress adversarial contributions. Trust scores are adaptively assigned to participating clients based on historical performance and behavioral indicators, allowing AGAT-FL to downweight suspicious updates while preserving data privacy. Experimental evaluations on two benchmark IoT security datasets, N-BaIoT and CIC-ToN-IoT, demonstrate that AGAT-FL consistently outperforms state-of-the-art FL methods. It achieves up to 94.01% accuracy, 93.50% precision, 94.00% recall, and 93.75% F1-score on N-BaIoT, and 91.02% accuracy, 91.00% precision, 91.30% recall, and 91.15% F1-score on CIC-ToN-IoT. Additionally, the use of explainable AI techniques such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhances transparency by identifying key features contributing to anomaly classification. These results underscore AGAT-FL as a robust, interpretable, and scalable solution for securing FL-based IoT networks against sophisticated poisoning attacks.

Although cryptocurrencies are not yet widely used as a method of payment in retail and commercial transactions, they are attracting a growing level of interest. However, if cryptocurrencies are to attract mass adoption for settlement, they will need to have merchant and consumer trust. This paper examines the factors which contribute to the development of trust, and whether the perception of trust varies between cultures (Arab and non-Arab). To explore this, the study analysed, using multigroup partial least squares analysis, 729 survey responses. The analysis examined the impact of four factors on perceived trust in cryptocurrency payments, and found that the perception of information confidentiality, identifiability and traceability all have a significant effect, while perceptions of fraud vulnerability had very little impact. There were also clear socio-cultural effects. This research offers significant insights into the trust dynamics essential for the acceptance of crypto-payment-services and highlights the importance of culture perspectives.

In the era of data analytics, the use of web analytics tools is widely spread among Small and Medium Enterprises (SMEs), especially in the e-commerce sector, to analyze web usage data and measure website performance due to the several features it provides. Most web analytics studies were conducted based on marketing rather than a managerial point of view. Therefore, there is a necessity to study the impact from a managerial perspective by exploring the impact on financial and non-financial performance and the decision-making process. This study assessed the impact of web analytics tools on the overall performance of SMEs, targeting Saudi SMEs and considering the lack of web analytics studies in this sector in Saudi Arabia. The sample involved eight Saudi SMEs who participated by answering interview questions, which were analyzed using thematic analysis. The findings demonstrated four main themes: the current web analytics tools used by Saudi SMEs, the motivations behind their use, and their impact on SMEs' performance and decision-making. The results showed that these tools positively affect the overall performance of SMEs. Google Analytics (GA) was the tool most used by the sample. Seven motives were discovered that encourage SMEs to use web analytics tools. This study also addressed the common Key Performance Indicators (KPIs) used by the participants. Concerning decision-making, it was found that web analytics tools affected and supported SMEs' decisions. Furthermore, this study evaluated the role of Zid, the Saudi e-commerce platform for data analytics and performance measurement.