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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Related Experiment Video

Updated: Jul 12, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
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Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

M Ananthi1, Annapoorani Gopal2, K Ramalakshmi3

  • 1Department of Computer Science and Business Systems, Sri Sairam Engineering College, Chennai, India.

Big Data
|October 30, 2023
PubMed
Summary

Accurate resource prediction for big data streaming is challenging. The Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA) framework improves efficiency and reduces errors in cloud-based systems.

Keywords:
Gaussian adapted Markov modelbig data streamingcloudgating strategyoverhauled fluctuation analysisresource scalingresource usage prediction

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Streaming

Background:

  • Accurate resource usage prediction in big data streaming applications remains a complex challenge.
  • Existing resource scaling techniques often suffer from inefficient scaling, inaccurate forecasting, high latency, and prolonged running times.

Purpose of the Study:

  • To develop an efficient framework for big data streaming in cloud systems.
  • To effectively manage time-bounded big data streaming applications with a reduced error rate.

Main Methods:

  • Introduction of the Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA) framework.
  • Utilization of a gating strategy for feature extraction, enabling nonlinear data distribution and fat convergence solutions for fluctuation analysis.
  • Development of a layered architecture to simplify resource forecasting in streaming applications.

Main Results:

  • The proposed GAMM-OFA stream model demonstrates validated and comparable results against different measures.
  • The framework aims to enhance efficiency in big data streaming resource management.

Conclusions:

  • The GAMM-OFA framework offers a novel approach to address limitations in existing big data streaming resource management.
  • This study contributes to more efficient and accurate resource forecasting in cloud-based streaming environments.