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Record W4400682750 · doi:10.4018/ijswis.345934

A Secure Data E-Governance for Healthcare Application in Cyber Physical Systems

2024· article· en· W4400682750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal on Semantic Web and Information Systems · 2024
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsÉcole de Technologie SupérieureImpact
Fundersnot available
KeywordsComputer scienceComputer securityHealth careCyber-physical systemTransparency (behavior)ArchitectureMedical recordMedicine

Abstract

fetched live from OpenAlex

The bio-medical devices gather patient information and communicate it to data consumers via wireless networks to take the appropriate action and decision by informing the doctors. However, IoMT is adopted by healthcare departments with a greater speed, yet the majority of devices are limited to resource constraints and security perspectives. The classical e-healthcare systems that are centric have the inherent problem of single-point failure with low transparency and low control over records. Many proposals have been validated in IoT for addressing the inadequate computing and storage of records through sensors. The main focus of this paper is to propose a novel hybrid architecture called Zero Trust Blockchain Architecture for decentralized E-health-CPS systems to support low latency along with storage and processing of records while monitoring the patients. In addition, a probability distribution function may further draft an accurate and real-time monitoring of patients. The proposed mechanism is analyzed against adequate decision, storage, accuracy and transmission of records.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.291
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it