Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data’s privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system’s performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services.

Fine-tuning a BERT-Base model for specific tasks, such as sentiment analysis, has become resource-intensive and often requires high computational power and memory. This paper introduces SCALE, a novel resource-efficient fine-tuning method that targets the most critical transformer layers, which reduces computational costs without sacrificing performance. By dynamically profiling transformer layers via activation magnitudes and attention entropy, SCALE selects and adapts only the most influential layers with lightweight adapter modules. The proposed method outperforms traditional fine-tuning techniques, achieving a 2.3% improvement in accuracy on the IMDB dataset and reducing training time by 56.3% compared to full-model fine-tuning. Experiments across various sentiment analysis benchmarks demonstrate SCALE’s effectiveness in optimizing fine-tuning for the BERT-base model in resource-constrained environments, achieving up to 99% of the performance of full-model fine-tuning while using only 40% of the parameters. The empirical validation in this study is restricted to binary and multi-class sentiment classification. The evaluation specifically reflects effectiveness in sentiment analysis text classification tasks.

This research is intended to evaluate the efficiency of proceures and processes used by the King Abdulaziz International Airport (KAIA) in Jeddah, Saudi Arabia with the help of Six Sigma. For, we have framed different objectives for analyzing the effect of six sigma in decision makings and quality control of the KAIA. The aim is also to find out the relationship of Six Sigma on an information system used by KAIA. We have used the qualitative research method (semi-structured interview method) with the target population of five employees working at KAIA. Qualitative data was collected from participants through transcribed, audio-recorded and codes. Thematic analysis was carried out in order to obtain the study result. Our study finds that there is a significant effect of Six Sigma in streamlining of all the processes of KAIA, which contributed towards the improvement of the quality and providing better control. We found that the KAIA applies KPIs to measure the decision makin, empowering the decision makers or managers. We have also reflected on the role of Six Sigma towards the effective performance of individuals in finance, customer support, and internal business process and team development.

Protecting the privacy of users’ data while maintaining reliability and accuracy in crowded events remains an open issue, especially with the growing capabilities and resources of attackers. This challenge becomes more difficult in dynamic environments with moving users/devices. Unfortunately, the current privacy-preserving methods suffer from several drawbacks that include reliability and accuracy of results, the need to fully trust a third party, or the incurrence of heavy overheads. This research presents a novel approach that is enhanced by peer cooperation, which is one of the most suitable techniques for crowded environments. The proposed approach is called “Smart Monikers with Multi-Peer Cooperation (SM2Peer)”. The SM2Peer addresses all the drawbacks of the traditional peer cooperation approach through two scenarios. In addition, the SM2Peer exploits the fog computing layer to control the cooperation among peers effectively, where each fog node manages several peers with smart moniker management. Moreover, SM2Peer provides multiple caches to relax the total overhead. The simulation and comparison with other common privacy approaches show the superiority of the SM2Peer in many aspects and metrics of privacy without a significant effect on performance.

In the original article, one of the co-author’s (Abdullah Basahel) given name has been published incorrectly.