Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Adaptive Sampling Technique Using Regression Modelling and Fuzzy Inference System for Network Traffic.

Abdussalam Salama1, Reza Saatchi1, Derek Burke2

  • 1Materials and Engineering Research Institute, Sheffield Hallam University, Sheffield, UK.

Studies in Health Technology and Informatics
|September 7, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation.

Diagnostics (Basel, Switzerland)·2025
Same author

Fabry disease Enzyme Enhancement on migalastat Study: FEES.

Clinica chimica acta; international journal of clinical chemistry·2024
Same author

Convolutional Neural Network to Classify Infrared Thermal Images of Fractured Wrists in Pediatrics.

Healthcare (Basel, Switzerland)·2024
Same author

Evaluation, in a highly specialised enzyme laboratory, of a digital microfluidics platform for rapid assessment of lysosomal enzyme activity in dried blood spots.

JIMD reports·2024
Same author

Making Emergency Surgical Decisions Without any Imaging Evidence: A Case Report of Complicated Diverticular Phlegmon.

Journal of clinical medicine research·2022
Same author

Combined Gluteal and Posterior Thigh Compartment Syndrome Without Associated Fracture: A Case Report.

Journal of clinical medicine research·2022
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
See all related articles

This study introduces an adaptive sampling method for analyzing electronic-health network traffic. The new approach, using regression and fuzzy logic, offers superior performance over traditional methods for real-time packet analysis.

Area of Science:

  • Computer Science
  • Health Informatics
  • Network Engineering

Background:

  • Electronic-health (e-health) systems depend on robust computer networks for data transmission.
  • Analyzing network traffic parameters is crucial for evaluating e-health system performance.
  • Real-time analysis of large data packet volumes is computationally challenging.

Purpose of the Study:

  • To develop an advanced sampling technique for efficient analysis of e-health network traffic.
  • To create a method that dynamically adapts to changing network conditions.
  • To compare the effectiveness of the proposed adaptive sampling against conventional methods.

Main Methods:

  • Development of an adaptive sampling algorithm integrating regression and fuzzy inference systems.
Keywords:
Quality of Service (QoS)computer network traffic samplinge-healthmultimedia transmission

Related Experiment Videos

  • Dynamic adjustment of sampling rates based on real-time traffic pattern analysis.
  • Comparative performance evaluation against non-adaptive sampling techniques.

    • Main Results:

    • The adaptive sampling method demonstrated superior performance in representing network traffic compared to non-adaptive methods.
    • The system effectively adjusted sampling parameters in response to dynamic traffic fluctuations.
    • Accurate representation of traffic parameters was achieved with a smaller subset of data packets.

    Conclusions:

    • The proposed adaptive sampling method offers a more efficient and effective approach for monitoring e-health network traffic.
    • This technique enhances the feasibility of real-time network performance analysis in e-health environments.
    • Adaptive sampling is a promising strategy for optimizing data packet analysis in large-scale networks.