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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined

Md Mokhlesur Rahman1, Ravie Chandren Muniyandi2, Shahnorbanun Sahran3

  • 1Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia. mmarks_cse@yahoo.com.

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|July 9, 2024
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Summary

Protecting sensitive autism data is crucial. This study introduces a novel method using an optimal key from the Enhanced Combined PSO-GWO framework for accurate autism data restoration, enhancing security and privacy.

Keywords:
Autism datasetData restorationOptimal keyPSO-GWO frameworkSecurity and privacy

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Area of Science:

  • Computer Science
  • Medical Informatics
  • Data Security

Background:

  • Timely identification of autism spectrum disorder (ASD) is vital for child development.
  • Sharing sensitive autism data for diagnosis raises significant security and privacy concerns.
  • Existing anonymization methods struggle with accurate data restoration and preventing leakage.

Purpose of the Study:

  • To present a novel approach for improved data restoration of sanitized sensitive autism datasets.
  • To enhance the security and privacy of autism data during transmission and storage.
  • To address the deficiencies in accuracy associated with conventional data restoration processes.

Main Methods:

  • Utilized an optimal key generated by the Enhanced Combined Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO) framework.
  • Applied the generated key for both sanitization (concealing data) and restoration (recovering data).
  • Employed the same optimal key to improve the accuracy of original data recovery from sanitized datasets.

Main Results:

  • Achieved highly competitive accuracies in autism data restoration experiments, reaching up to 99.90%.
  • Demonstrated superior performance compared to existing meta-heuristic algorithms across various datasets.
  • Outperformed other methods specifically on the 30-month autism children dataset.

Conclusions:

  • The proposed method significantly enhances the security and privacy of autism data restoration.
  • The Enhanced Combined PSO-GWO framework effectively generates optimal keys for robust data protection.
  • This approach offers a promising solution for accurate and secure handling of sensitive autism-related information.