ArticleComputational intelligence and neuroscience2022
QoS-Aware Smart Phone-Based User Tracking Application to Prevent Outbreak of COVID-19 in Saudi Arabia.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Retracted: QoS-Aware Smart Phone-Based User Tracking Application to Prevent Outbreak of COVID-19 in Saudi Arabia.Computational intelligence and neuroscience · 2023Article
- The role of the COVID-19 pandemic in expediting digital health-care transformation: Saudi Arabia's experience.Informatics in medicine unlocked · 2022Review
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diverse variants of COVID-19 are repeatedly making everyday living unstable. In reality, the conclusive retort of this highly contagious virus still is in incognito mode. The health experts' primary guideline on the possible prevention of this disease outbreak, including a list of restrictions and confinements, is insufficient in case of any public congregation. As a result, the demand for precise and upgraded real-time COVID-19 tracking and prevention-based applications increases. However, most of the existing android-based applications face a lack of data security and reliability that cannot satisfy the additional quality of service (QoS) requirements. This paper proposes an easy-to-operate android-based multifunctional application to track individuals' health situations, allow uploading scanning report by the authorized organization like universities, mosques, school, and hospitals and helps the users to maintain guidelines via manageable steps. This article offers a three-layered QoS aware service-oriented task scheduling model upon multitasking android-based frontend focusing the cognitive-based AI applications in healthcare with a continual learning paradigm. Designed model is competent to optimize heterogeneous service scheduling and can minimize data delivery time, as well as the resource cost.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.